



'''simple docstring'''



import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
							import tensorflow as tf

							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowerCAmelCase_     :       List[str]								=						logging.get_logger(__name__)
class 	lowerCamelCase_							(					enum.Enum			):
						_lowerCAmelCase     :   Optional[Any]	     =				0
						_lowerCAmelCase     :   Dict	     =				1


@add_end_docstrings(_UpperCAmelCase			)
class 	lowerCamelCase_							(					_UpperCAmelCase			):
						_lowerCAmelCase     :   List[Any]	     =				'generated'







						def __init__(  self   :      int				,    *lowerCAmelCase__   :      Optional[int]				,    **lowerCAmelCase__   :      str				):

													"""simple docstring"""
													super().__init__(*__UpperCamelCase				,    **__UpperCamelCase				)

													self.check_model_type(
													    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
													    if self.framework == '''tf'''
													    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING				)







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      List[Any]=None				,    lowerCAmelCase__   :      Union[str, Any]=None				,    lowerCAmelCase__   :      Any=None				,    lowerCAmelCase__   :      int=None				,    lowerCAmelCase__   :      Tuple=None				,    lowerCAmelCase__   :      Dict=None				,    **lowerCAmelCase__   :      Any				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			{}
													if truncation is not None:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			truncation

													SCREAMING_SNAKE_CASE					:      Dict     					=			generate_kwargs

													SCREAMING_SNAKE_CASE					:      List[str]     					=			{}
													if return_tensors is not None and return_type is None:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ReturnType.TENSORS if return_tensors else ReturnType.TEXT
													if return_type is not None:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			return_type

													if clean_up_tokenization_spaces is not None:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			clean_up_tokenization_spaces

													if stop_sequence is not None:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			self.tokenizer.encode(__UpperCamelCase				,    add_special_tokens=__UpperCamelCase				)
																				if len(__UpperCamelCase				) > 1:
																											warnings.warn(
																											    '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
																											    ''' the stop sequence will be used as the stop sequence string in the interim.'''				)
																				SCREAMING_SNAKE_CASE					:      int     					=			stop_sequence_ids[0]

													return preprocess_params, forward_params, postprocess_params







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													return True







						def 					__lowercase    (  self   :      Optional[Any]				,    *lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model.config.prefix if self.model.config.prefix is not None else ''''''
													if isinstance(args[0]				,    __UpperCamelCase				):
																				if self.tokenizer.pad_token_id is None:
																											raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input'''				)
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			([prefix + arg for arg in args[0]],)
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			True

													elif isinstance(args[0]				,    __UpperCamelCase				):
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			(prefix + args[0],)
																				SCREAMING_SNAKE_CASE					:      Dict     					=			False
													else:
																				raise ValueError(
																				    F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.tokenizer(*__UpperCamelCase				,    padding=__UpperCamelCase				,    truncation=__UpperCamelCase				,    return_tensors=self.framework				)
													# This is produced by tokenizers but is an invalid generate kwargs
													if "token_type_ids" in inputs:
																				del inputs["token_type_ids"]
													return inputs







						def __call__(  self   :      Dict				,    *lowerCAmelCase__   :      str				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			super().__call__(*__UpperCamelCase				,    **__UpperCamelCase				)
													if (
													    isinstance(args[0]				,    __UpperCamelCase				)
													    and all(isinstance(__UpperCamelCase				,    __UpperCamelCase				) for el in args[0]				)
													    and all(len(__UpperCamelCase				) == 1 for res in result				)
													):
																				return [res[0] for res in result]
													return result







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self._parse_and_tokenize(__UpperCamelCase				,    truncation=__UpperCamelCase				,    **__UpperCamelCase				)
													return inputs







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Dict				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""






													if self.framework == "pt":
																				SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model_inputs['''input_ids'''].shape
													elif self.framework == "tf":
																				SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tf.shape(model_inputs['''input_ids''']				).numpy()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			generate_kwargs.get('''min_length'''				,    self.model.config.min_length				)
													SCREAMING_SNAKE_CASE					:      str     					=			generate_kwargs.get('''max_length'''				,    self.model.config.max_length				)
													self.check_inputs(__UpperCamelCase				,    generate_kwargs['''min_length''']				,    generate_kwargs['''max_length''']				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.model.generate(**__UpperCamelCase				,    **__UpperCamelCase				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			output_ids.shape[0]
													if self.framework == "pt":
																				SCREAMING_SNAKE_CASE					:      Any     					=			output_ids.reshape(__UpperCamelCase				,    out_b // in_b				,    *output_ids.shape[1:]				)
													elif self.framework == "tf":
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			tf.reshape(__UpperCamelCase				,    (in_b, out_b // in_b, *output_ids.shape[1:])				)
													return {"output_ids": output_ids}







						def 					__lowercase    (  self   :      Tuple				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Union[str, Any]=ReturnType.TEXT				,    lowerCAmelCase__   :      int=False				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			[]
													for output_ids in model_outputs["output_ids"][0]:
																				if return_type == ReturnType.TENSORS:
																											SCREAMING_SNAKE_CASE					:      str     					=			{F"""{self.return_name}_token_ids""": output_ids}
																				elif return_type == ReturnType.TEXT:
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			{
																											    F"""{self.return_name}_text""": self.tokenizer.decode(
																											        __UpperCamelCase				,    skip_special_tokens=__UpperCamelCase				,    clean_up_tokenization_spaces=__UpperCamelCase				,    )
																											}
																				records.append(__UpperCamelCase				)
													return records


@add_end_docstrings(_UpperCAmelCase			)
class 	lowerCamelCase_							(					_UpperCAmelCase			):
						_lowerCAmelCase     :   List[Any]	     =				'summary'







						def __call__(  self   :      Optional[Any]				,    *lowerCAmelCase__   :      Optional[Any]				,    **lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													return super().__call__(*__UpperCamelCase				,    **__UpperCamelCase				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""






													if max_length < min_length:
																				logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}."""				)

													if input_length < max_length:
																				logger.warning(
																				    F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
																				    '''a summarization task, where outputs shorter than the input are typically wanted, you might '''
																				    F"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""				)


@add_end_docstrings(_UpperCAmelCase			)
class 	lowerCamelCase_							(					_UpperCAmelCase			):
						_lowerCAmelCase     :   Optional[int]	     =				'translation'







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""






													if input_length > 0.9 * max_length:
																				logger.warning(
																				    F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
																				    '''increasing your max_length manually, e.g. translator(\'...\', max_length=400)'''				)
													return True







						def 					__lowercase    (  self   :      Tuple				,    *lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Tuple=TruncationStrategy.DO_NOT_TRUNCATE				,    lowerCAmelCase__   :      Tuple=None				,    lowerCAmelCase__   :      Union[str, Any]=None				):

													"""simple docstring"""






													if getattr(self.tokenizer				,    '''_build_translation_inputs'''				,    __UpperCamelCase				):
																				return self.tokenizer._build_translation_inputs(
																				    *__UpperCamelCase				,    return_tensors=self.framework				,    truncation=__UpperCamelCase				,    src_lang=__UpperCamelCase				,    tgt_lang=__UpperCamelCase				)
													else:
																				return super()._parse_and_tokenize(*__UpperCamelCase				,    truncation=__UpperCamelCase				)







						def 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      int=None				,    lowerCAmelCase__   :      int=None				,    **lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			super()._sanitize_parameters(**__UpperCamelCase				)
													if src_lang is not None:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			src_lang
													if tgt_lang is not None:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tgt_lang
													if src_lang is None and tgt_lang is None:
																				# Backward compatibility, direct arguments use is preferred.
																				SCREAMING_SNAKE_CASE					:      int     					=			kwargs.get('''task'''				,    self.task				)
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			task.split('''_'''				)
																				if task and len(__UpperCamelCase				) == 4:
																											# translation, XX, to YY
																											SCREAMING_SNAKE_CASE					:      Dict     					=			items[1]
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			items[3]
													return preprocess_params, forward_params, postprocess_params







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

													"""simple docstring"""
													return super().__call__(*__UpperCamelCase				,    **__UpperCamelCase				)











'''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__				)




