



'''simple docstring'''



import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


lowerCAmelCase_     :       Optional[Any]								=						logging.getLogger(__name__)
def 					UpperCAmelCase   (						A  :				Any					,		A  :				Optional[Any]				):
							# save results
							if os.path.exists(A				):
														if os.path.exists(os.path.join(A					,		'''config.json'''				)				) and os.path.isfile(
														    os.path.join(A					,		'''config.json'''				)				):
																					os.remove(os.path.join(A					,		'''config.json'''				)				)
														if os.path.exists(os.path.join(A					,		'''pytorch_model.bin'''				)				) and os.path.isfile(
														    os.path.join(A					,		'''pytorch_model.bin'''				)				):
																					os.remove(os.path.join(A					,		'''pytorch_model.bin'''				)				)
							else:
														os.makedirs(A				)
							model.save_pretrained(A				)
def 					UpperCAmelCase   (						A  :				Union[str, Any]					,		A  :				List[Any]=False				):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			2
							if unlogit:
														SCREAMING_SNAKE_CASE					:      Any     					=			torch.pow(A					,		A				)
							SCREAMING_SNAKE_CASE					:      Any     					=			p * torch.log(A				)
							SCREAMING_SNAKE_CASE					:      Dict     					=			0
							return -plogp.sum(dim=-1				)
def 					UpperCAmelCase   (						A  :				Optional[int]				):
							logger.info('''lv, h >\t''' + '''\t'''.join(F"""{x + 1}""" for x in range(len(A				)				)				)				)
							for row in range(len(A				)				):
														if tensor.dtype != torch.long:
																					logger.info(F"""layer {row + 1}:\t""" + '''\t'''.join(F"""{x:.5f}""" for x in tensor[row].cpu().data				)				)
														else:
																					logger.info(F"""layer {row + 1}:\t""" + '''\t'''.join(F"""{x:d}""" for x in tensor[row].cpu().data				)				)
def 					UpperCAmelCase   (						A  :				Dict					,		A  :				List[Any]					,		A  :				Any					,		A  :				Optional[int]=True					,		A  :				Optional[Any]=True					,		A  :				str=None					,		A  :				List[str]=False				):
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			model.config.num_hidden_layers, model.config.num_attention_heads
							SCREAMING_SNAKE_CASE					:      int     					=			torch.zeros(A					,		A				).to(args.device				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			torch.zeros(A					,		A				).to(args.device				)

							if head_mask is None:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.ones(A					,		A				).to(args.device				)

							head_mask.requires_grad_(requires_grad=A				)
							# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
							if actually_pruned:
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			None

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			0.0
							SCREAMING_SNAKE_CASE					:      Any     					=			0.0
							for step, inputs in enumerate(tqdm(A					,		desc='''Iteration'''					,		disable=args.local_rank not in [-1, 0]				)				):
														SCREAMING_SNAKE_CASE					:      int     					=			tuple(t.to(args.device				) for t in inputs				)
														((SCREAMING_SNAKE_CASE)					,)					:      str     					=			inputs

														# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
														SCREAMING_SNAKE_CASE					:      Dict     					=			model(A					,		labels=A					,		head_mask=A				)
														#  (loss), lm_logits, presents, (all hidden_states), (attentions)
														SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			(
														    outputs[0],
														    outputs[1],
														    outputs[-1],
														)  # Loss and logits are the first, attention the last
														loss.backward()  # Backpropagate to populate the gradients in the head mask
														total_loss += loss.detach().cpu().numpy()
														if compute_entropy:
																					for layer, attn in enumerate(A				):
																												SCREAMING_SNAKE_CASE					:      List[str]     					=			entropy(attn.detach()					,		A				)
																												attn_entropy[layer] += masked_entropy.sum(-1				).sum(0				).sum(0				).detach()

														if compute_importance:
																					head_importance += head_mask.grad.abs().detach()
														tot_tokens += torch.ones_like(A				).float().detach().sum().data

							# Normalize
							attn_entropy /= tot_tokens
							head_importance /= tot_tokens
							# Layerwise importance normalization
							if not args.dont_normalize_importance_by_layer:
														SCREAMING_SNAKE_CASE					:      Any     					=			2
														SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.pow(torch.pow(A					,		A				).sum(-1				)					,		1 / exponent				)
														head_importance /= norm_by_layer.unsqueeze(-1				) + 1e-2_0

							if not args.dont_normalize_global_importance:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

							# Print matrices
							if compute_entropy:
														logger.info('''Attention entropies'''				)
														print_ad_tensor(A				)
							if compute_importance:
														logger.info('''Head importance scores'''				)
														print_ad_tensor(A				)
							logger.info('''Head ranked by importance scores'''				)
							SCREAMING_SNAKE_CASE					:      Dict     					=			torch.zeros(head_importance.numel()					,		dtype=torch.long					,		device=args.device				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.arange(
							    head_importance.numel()					,		device=args.device				)
							SCREAMING_SNAKE_CASE					:      Any     					=			head_ranks.view_as(A				)
							print_ad_tensor(A				)
							return attn_entropy, head_importance, total_loss
def 					UpperCAmelCase   (						A  :				str					,		A  :				List[str]					,		A  :				Dict				):
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			compute_heads_importance(A					,		A					,		A					,		compute_entropy=A				)
							SCREAMING_SNAKE_CASE					:      Dict     					=			1 / loss  # instead of downsteam score use the LM loss
							logger.info('''Pruning: original score: %f, threshold: %f'''					,		A					,		original_score * args.masking_threshold				)

							SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.ones_like(A				)
							SCREAMING_SNAKE_CASE					:      Any     					=			max(1					,		int(new_head_mask.numel() * args.masking_amount				)				)

							SCREAMING_SNAKE_CASE					:      List[str]     					=			original_score
							while current_score >= original_score * args.masking_threshold:
														SCREAMING_SNAKE_CASE					:      Dict     					=			new_head_mask.clone().detach()  # save current head mask
														# heads from least important to most - keep only not-masked heads
														SCREAMING_SNAKE_CASE					:      Tuple     					=			float('''Inf'''				)
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			head_importance.view(-1				).sort()[1]

														if len(A				) <= num_to_mask:
																					print('''BREAK BY num_to_mask'''				)
																					break

														# mask heads
														SCREAMING_SNAKE_CASE					:      Any     					=			current_heads_to_mask[:num_to_mask]
														logger.info('''Heads to mask: %s'''					,		str(current_heads_to_mask.tolist()				)				)
														SCREAMING_SNAKE_CASE					:      Tuple     					=			new_head_mask.view(-1				)
														SCREAMING_SNAKE_CASE					:      Any     					=			0.0
														SCREAMING_SNAKE_CASE					:      Any     					=			new_head_mask.view_as(A				)
														SCREAMING_SNAKE_CASE					:      Dict     					=			new_head_mask.clone().detach()
														print_ad_tensor(A				)

														# Compute metric and head importance again
														SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			compute_heads_importance(
														    A					,		A					,		A					,		compute_entropy=A					,		head_mask=A				)
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			1 / loss
														logger.info(
														    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''					,		A					,		new_head_mask.sum()					,		new_head_mask.sum() / new_head_mask.numel() * 100					,		)

							logger.info('''Final head mask'''				)
							print_ad_tensor(A				)
							np.save(os.path.join(args.output_dir					,		'''head_mask.npy'''				)					,		head_mask.detach().cpu().numpy()				)

							return head_mask
def 					UpperCAmelCase   (						A  :				int					,		A  :				Optional[int]					,		A  :				Any					,		A  :				Dict				):
							SCREAMING_SNAKE_CASE					:      str     					=			datetime.now()
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Any     					=			compute_heads_importance(
							    A					,		A					,		A					,		compute_entropy=A					,		compute_importance=A					,		head_mask=A				)
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			1 / loss
							SCREAMING_SNAKE_CASE					:      Dict     					=			datetime.now() - before_time

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			sum(p.numel() for p in model.parameters()				)
							SCREAMING_SNAKE_CASE					:      Any     					=			{
							    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A				)				)
							}

							for k, v in heads_to_prune.items():
														if isinstance(A					,		A				):
																					SCREAMING_SNAKE_CASE					:      int     					=			[
																					    v,
																					]

							assert sum(len(A				) for h in heads_to_prune.values()				) == (1 - head_mask.long()).sum().item()
							model.prune_heads(A				)
							SCREAMING_SNAKE_CASE					:      Dict     					=			sum(p.numel() for p in model.parameters()				)

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			datetime.now()
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			compute_heads_importance(
							    A					,		A					,		A					,		compute_entropy=A					,		compute_importance=A					,		head_mask=A					,		actually_pruned=A					,		)

							SCREAMING_SNAKE_CASE					:      Tuple     					=			1 / loss
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			datetime.now() - before_time

							logger.info(
							    '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''					,		A					,		A					,		pruned_num_params / original_num_params * 100					,		)
							logger.info('''Pruning: score with masking: %f score with pruning: %f'''					,		A					,		A				)
							logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''					,		original_time / new_time * 100				)
							save_model(A					,		args.output_dir				)
def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--data_dir'''					,		default=A					,		type=A					,		required=A					,		help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''					,		)
							parser.add_argument(
							    '''--model_name_or_path'''					,		default=A					,		type=A					,		required=A					,		help='''Path to pretrained model or model identifier from huggingface.co/models'''					,		)
							parser.add_argument(
							    '''--output_dir'''					,		default=A					,		type=A					,		required=A					,		help='''The output directory where the model predictions and checkpoints will be written.'''					,		)

							# Other parameters
							parser.add_argument(
							    '''--config_name'''					,		default=''''''					,		type=A					,		help='''Pretrained config name or path if not the same as model_name_or_path'''					,		)
							parser.add_argument(
							    '''--tokenizer_name'''					,		default=''''''					,		type=A					,		help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''					,		)
							parser.add_argument(
							    '''--cache_dir'''					,		default=A					,		type=A					,		help='''Where do you want to store the pre-trained models downloaded from s3'''					,		)
							parser.add_argument(
							    '''--data_subset'''					,		type=A					,		default=-1					,		help='''If > 0: limit the data to a subset of data_subset instances.'''				)
							parser.add_argument(
							    '''--overwrite_output_dir'''					,		action='''store_true'''					,		help='''Whether to overwrite data in output directory'''				)
							parser.add_argument(
							    '''--overwrite_cache'''					,		action='''store_true'''					,		help='''Overwrite the cached training and evaluation sets'''				)

							parser.add_argument(
							    '''--dont_normalize_importance_by_layer'''					,		action='''store_true'''					,		help='''Don\'t normalize importance score by layers'''				)
							parser.add_argument(
							    '''--dont_normalize_global_importance'''					,		action='''store_true'''					,		help='''Don\'t normalize all importance scores between 0 and 1'''					,		)

							parser.add_argument(
							    '''--try_masking'''					,		action='''store_true'''					,		help='''Whether to try to mask head until a threshold of accuracy.'''				)
							parser.add_argument(
							    '''--masking_threshold'''					,		default=0.9					,		type=A					,		help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''					,		)
							parser.add_argument(
							    '''--masking_amount'''					,		default=0.1					,		type=A					,		help='''Amount to heads to masking at each masking step.'''				)
							parser.add_argument('''--metric_name'''					,		default='''acc'''					,		type=A					,		help='''Metric to use for head masking.'''				)

							parser.add_argument(
							    '''--max_seq_length'''					,		default=128					,		type=A					,		help=(
							        '''The maximum total input sequence length after WordPiece tokenization. \n'''
							        '''Sequences longer than this will be truncated, sequences shorter padded.'''
							    )					,		)
							parser.add_argument('''--batch_size'''					,		default=1					,		type=A					,		help='''Batch size.'''				)

							parser.add_argument('''--seed'''					,		type=A					,		default=42				)
							parser.add_argument('''--local_rank'''					,		type=A					,		default=-1					,		help='''local_rank for distributed training on gpus'''				)
							parser.add_argument('''--no_cuda'''					,		action='''store_true'''					,		help='''Whether not to use CUDA when available'''				)
							parser.add_argument('''--server_ip'''					,		type=A					,		default=''''''					,		help='''Can be used for distant debugging.'''				)
							parser.add_argument('''--server_port'''					,		type=A					,		default=''''''					,		help='''Can be used for distant debugging.'''				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			parser.parse_args()

							if args.server_ip and args.server_port:
														# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
														import ptvsd

														print('''Waiting for debugger attach'''				)
														ptvsd.enable_attach(address=(args.server_ip, args.server_port)					,		redirect_output=A				)
														ptvsd.wait_for_attach()

							# Setup devices and distributed training
							if args.local_rank == -1 or args.no_cuda:
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''				)
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			0 if args.no_cuda else torch.cuda.device_count()
							else:
														torch.cuda.set_device(args.local_rank				)
														SCREAMING_SNAKE_CASE					:      str     					=			torch.device('''cuda'''					,		args.local_rank				)
														SCREAMING_SNAKE_CASE					:      Tuple     					=			1
														torch.distributed.init_process_group(backend='''nccl'''				)  # Initializes the distributed backend

							# Setup logging
							logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN				)
							logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device					,		args.n_gpu					,		bool(args.local_rank != -1				)				)				)

							SCREAMING_SNAKE_CASE					:      int     					=			GPTaLMHeadModel.from_pretrained(args.model_name_or_path				)

							# Distributed and parallel training
							model.to(args.device				)
							if args.local_rank != -1:
														SCREAMING_SNAKE_CASE					:      int     					=			nn.parallel.DistributedDataParallel(
														    A					,		device_ids=[args.local_rank]					,		output_device=args.local_rank					,		find_unused_parameters=A				)
							elif args.n_gpu > 1:
														SCREAMING_SNAKE_CASE					:      int     					=			nn.DataParallel(A				)

							# Print/save training arguments
							os.makedirs(args.output_dir					,		exist_ok=A				)
							torch.save(A					,		os.path.join(args.output_dir					,		'''run_args.bin'''				)				)
							logger.info('''Training/evaluation parameters %s'''					,		A				)

							# Prepare dataset
							SCREAMING_SNAKE_CASE					:      Dict     					=			np.concatenate(
							    [
							        np.loadtxt(args.data_dir					,		dtype=np.intaa				),
							    ]				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			(torch.from_numpy(A				),)
							SCREAMING_SNAKE_CASE					:      Any     					=			TensorDataset(*A				)
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			RandomSampler(A				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			DataLoader(A					,		sampler=A					,		batch_size=args.batch_size				)

							# Compute head entropy and importance score
							compute_heads_importance(A					,		A					,		A				)

							# Try head masking (set heads to zero until the score goes under a threshole)
							# and head pruning (remove masked heads and see the effect on the network)
							if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			mask_heads(A					,		A					,		A				)
														prune_heads(A					,		A					,		A					,		A				)


if __name__ == "__main__":
							main()









'''simple docstring'''



import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    BioGptForCausalLM,
							    BioGptForSequenceClassification,
							    BioGptForTokenClassification,
							    BioGptModel,
							    BioGptTokenizer,
							)
							from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class 	lowerCamelCase_							:







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      str=13				,    lowerCAmelCase__   :      List[Any]=7				,    lowerCAmelCase__   :      Optional[int]=True				,    lowerCAmelCase__   :      Dict=True				,    lowerCAmelCase__   :      Union[str, Any]=False				,    lowerCAmelCase__   :      Tuple=True				,    lowerCAmelCase__   :      Dict=99				,    lowerCAmelCase__   :      List[str]=32				,    lowerCAmelCase__   :      str=5				,    lowerCAmelCase__   :      str=4				,    lowerCAmelCase__   :      Any=37				,    lowerCAmelCase__   :      Tuple="gelu"				,    lowerCAmelCase__   :      int=0.1				,    lowerCAmelCase__   :      Dict=0.1				,    lowerCAmelCase__   :      str=5_12				,    lowerCAmelCase__   :      List[str]=16				,    lowerCAmelCase__   :      List[str]=2				,    lowerCAmelCase__   :      Union[str, Any]=0.02				,    lowerCAmelCase__   :      int=3				,    lowerCAmelCase__   :      Tuple=4				,    lowerCAmelCase__   :      List[Any]=None				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			parent
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			batch_size
													SCREAMING_SNAKE_CASE					:      Any     					=			seq_length
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			is_training
													SCREAMING_SNAKE_CASE					:      Tuple     					=			use_input_mask
													SCREAMING_SNAKE_CASE					:      Tuple     					=			use_token_type_ids
													SCREAMING_SNAKE_CASE					:      Tuple     					=			use_labels
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			vocab_size
													SCREAMING_SNAKE_CASE					:      int     					=			hidden_size
													SCREAMING_SNAKE_CASE					:      int     					=			num_hidden_layers
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_attention_heads
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			intermediate_size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      int     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			max_position_embeddings
													SCREAMING_SNAKE_CASE					:      str     					=			type_vocab_size
													SCREAMING_SNAKE_CASE					:      int     					=			type_sequence_label_size
													SCREAMING_SNAKE_CASE					:      Tuple     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      Tuple     					=			num_labels
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			num_choices
													SCREAMING_SNAKE_CASE					:      Any     					=			scope







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			None
													if self.use_input_mask:
																				SCREAMING_SNAKE_CASE					:      str     					=			random_attention_mask([self.batch_size, self.seq_length]				)

													SCREAMING_SNAKE_CASE					:      str     					=			None
													if self.use_token_type_ids:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.type_vocab_size				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			None
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			None
													SCREAMING_SNAKE_CASE					:      List[str]     					=			None
													if self.use_labels:
																				SCREAMING_SNAKE_CASE					:      Any     					=			ids_tensor([self.batch_size]				,    self.type_sequence_label_size				)
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels				)
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			ids_tensor([self.batch_size]				,    self.num_choices				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_config()

													return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													return BioGptConfig(
													    vocab_size=self.vocab_size				,    hidden_size=self.hidden_size				,    num_hidden_layers=self.num_hidden_layers				,    num_attention_heads=self.num_attention_heads				,    intermediate_size=self.intermediate_size				,    hidden_act=self.hidden_act				,    hidden_dropout_prob=self.hidden_dropout_prob				,    attention_probs_dropout_prob=self.attention_probs_dropout_prob				,    max_position_embeddings=self.max_position_embeddings				,    type_vocab_size=self.type_vocab_size				,    is_decoder=lowerCAmelCase__				,    initializer_range=self.initializer_range				,    )







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			BioGptModel(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(lowerCAmelCase__				)
													self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)				)







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Dict				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptForCausalLM(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    token_type_ids=lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      str				,    *lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptModel(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()

													# create attention mask
													SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.ones(input_ids.shape				,    dtype=torch.long				,    device=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.seq_length // 2
													SCREAMING_SNAKE_CASE					:      Any     					=			0

													# first forward pass
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Tuple     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				).to_tuple()

													# create hypothetical next token and extent to next_input_ids
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ids_tensor((self.batch_size, 1)				,    config.vocab_size				)

													# change a random masked slice from input_ids
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			ids_tensor((1,)				,    lowerCAmelCase__				).item() + 1
													SCREAMING_SNAKE_CASE					:      Tuple     					=			ids_tensor((self.batch_size, 1)				,    config.vocab_size				).squeeze(-1				)
													SCREAMING_SNAKE_CASE					:      str     					=			random_other_next_tokens

													# append to next input_ids and attn_mask
													SCREAMING_SNAKE_CASE					:      Tuple     					=			torch.cat([input_ids, next_tokens]				,    dim=-1				)
													SCREAMING_SNAKE_CASE					:      str     					=			torch.cat(
													    [attn_mask, torch.ones((attn_mask.shape[0], 1)				,    dtype=torch.long				,    device=lowerCAmelCase__				)]				,    dim=1				,    )

													# get two different outputs
													SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)['''last_hidden_state''']
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(lowerCAmelCase__				,    past_key_values=lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)['''last_hidden_state''']

													# select random slice
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ids_tensor((1,)				,    output_from_past.shape[-1]				).item()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			output_from_no_past[:, -1, random_slice_idx].detach()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			output_from_past[:, 0, random_slice_idx].detach()

													# test that outputs are equal for slice
													self.parent.assertTrue(torch.allclose(lowerCAmelCase__				,    lowerCAmelCase__				,    atol=1e-3				)				)







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      List[Any]				,    *lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			BioGptModel(config=lowerCAmelCase__				).to(lowerCAmelCase__				).eval()

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			torch.ones(input_ids.shape				,    dtype=torch.long				,    device=lowerCAmelCase__				)

													# first forward pass
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    use_cache=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			outputs.to_tuple()

													# create hypothetical multiple next token and extent to next_input_ids
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ids_tensor((self.batch_size, 3)				,    config.vocab_size				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			ids_tensor((self.batch_size, 3)				,    2				)

													# append to next input_ids and
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.cat([input_ids, next_tokens]				,    dim=-1				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.cat([attention_mask, next_attn_mask]				,    dim=-1				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)['''last_hidden_state''']
													SCREAMING_SNAKE_CASE					:      str     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    past_key_values=lowerCAmelCase__				)[
													    '''last_hidden_state'''
													]

													# select random slice
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ids_tensor((1,)				,    output_from_past.shape[-1]				).item()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			output_from_no_past[:, -3:, random_slice_idx].detach()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			output_from_past[:, :, random_slice_idx].detach()

													self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

													# test that outputs are equal for slice
													self.parent.assertTrue(torch.allclose(lowerCAmelCase__				,    lowerCAmelCase__				,    atol=1e-3				)				)







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      str				,    *lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      int=False				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			BioGptForCausalLM(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													if gradient_checkpointing:
																				model.gradient_checkpointing_enable()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													self.parent.assertEqual(result.loss.shape				,    ()				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)				)
													result.loss.backward()







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      str				,    *lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptModel(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers				)
													for key in model.state_dict().keys():
																				if "c_proj" in key and "weight" in key:
																											self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]				) - model_std				)				,    0.001				)
																											self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]				) - 0.0				)				,    0.01				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Tuple				,    *lowerCAmelCase__   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Tuple     					=			BioGptForTokenClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    token_type_ids=lowerCAmelCase__				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels)				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.prepare_config_and_inputs()
													(
													    (
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,
													)					:      Optional[Any]     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Any     					=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
													return config, inputs_dict


@require_torch
class 	lowerCamelCase_							(					snake_case_    ,				snake_case_    ,				snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Dict	     =				(
						    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
						    if is_torch_available()
						    else ()
						)
						_lowerCAmelCase     :   Optional[Any]	     =				(BioGptForCausalLM,) if is_torch_available() else ()
						_lowerCAmelCase     :   List[str]	     =				(
						    {
						        'feature-extraction': BioGptModel,
						        'text-classification': BioGptForSequenceClassification,
						        'text-generation': BioGptForCausalLM,
						        'token-classification': BioGptForTokenClassification,
						        'zero-shot': BioGptForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowerCAmelCase     :   Dict	     =				False







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptModelTester(self				)
													SCREAMING_SNAKE_CASE					:      Any     					=			ConfigTester(self				,    config_class=lowerCAmelCase__				,    hidden_size=37				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													self.config_tester.run_common_tests()







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			type
																				self.model_tester.create_and_check_model(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase__				,    gradient_checkpointing=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
													model.to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			'''left'''

													# Define PAD Token = EOS Token = 50256
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer.eos_token
													SCREAMING_SNAKE_CASE					:      Dict     					=			model.config.eos_token_id

													# use different length sentences to test batching
													SCREAMING_SNAKE_CASE					:      Any     					=			[
													    '''Hello, my dog is a little''',
													    '''Today, I''',
													]

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer(lowerCAmelCase__				,    return_tensors='''pt'''				,    padding=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			inputs['''input_ids'''].to(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model.generate(
													    input_ids=lowerCAmelCase__				,    attention_mask=inputs['''attention_mask'''].to(lowerCAmelCase__				)				,    )

													SCREAMING_SNAKE_CASE					:      int     					=			tokenizer(sentences[0]				,    return_tensors='''pt'''				).input_ids.to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Any     					=			model.generate(input_ids=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer(sentences[1]				,    return_tensors='''pt'''				).input_ids.to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model.generate(input_ids=lowerCAmelCase__				,    max_length=model.config.max_length - num_paddings				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer.batch_decode(lowerCAmelCase__				,    skip_special_tokens=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.decode(output_non_padded[0]				,    skip_special_tokens=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tokenizer.decode(output_padded[0]				,    skip_special_tokens=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			[
													    '''Hello, my dog is a little bit bigger than a little bit.''',
													    '''Today, I have a good idea of how to use the information''',
													]
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    [non_padded_sentence, padded_sentence]				)







						@slow
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			BioGptModel.from_pretrained(lowerCAmelCase__				)
																				self.assertIsNotNone(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			3
													SCREAMING_SNAKE_CASE					:      Dict     					=			input_dict['''input_ids''']
													SCREAMING_SNAKE_CASE					:      str     					=			input_ids.ne(1				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ids_tensor([self.model_tester.batch_size]				,    self.model_tester.type_sequence_label_size				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			BioGptForSequenceClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													self.assertEqual(result.logits.shape				,    (self.model_tester.batch_size, self.model_tester.num_labels)				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			3
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			'''multi_label_classification'''
													SCREAMING_SNAKE_CASE					:      Any     					=			input_dict['''input_ids''']
													SCREAMING_SNAKE_CASE					:      Any     					=			input_ids.ne(1				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ids_tensor(
													    [self.model_tester.batch_size, config.num_labels]				,    self.model_tester.type_sequence_label_size				).to(torch.float				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			BioGptForSequenceClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													self.assertEqual(result.logits.shape				,    (self.model_tester.batch_size, self.model_tester.num_labels)				)


@require_torch
class 	lowerCamelCase_							(					unittest.TestCase			):







						@slow
						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
													SCREAMING_SNAKE_CASE					:      str     					=			torch.tensor([[2, 48_05, 9, 6_56, 21]]				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(lowerCAmelCase__				)[0]

													SCREAMING_SNAKE_CASE					:      Tuple     					=			4_23_84

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.Size((1, 5, vocab_size)				)
													self.assertEqual(output.shape				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			torch.tensor(
													    [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]				)

													self.assertTrue(torch.allclose(output[:, :3, :3]				,    lowerCAmelCase__				,    atol=1e-4				)				)







						@slow
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
													model.to(lowerCAmelCase__				)

													torch.manual_seed(0				)
													SCREAMING_SNAKE_CASE					:      str     					=			tokenizer('''COVID-19 is'''				,    return_tensors='''pt'''				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model.generate(
													    **lowerCAmelCase__				,    min_length=1_00				,    max_length=10_24				,    num_beams=5				,    early_stopping=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			tokenizer.decode(output_ids[0]				,    skip_special_tokens=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Any     					=			(
													    '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
													    ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
													    ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
													    ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
													    ''' more than 800,000 deaths.'''
													)
													self.assertEqual(lowerCAmelCase__				,    lowerCAmelCase__				)




