


'''simple docstring'''



import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel






class  UpperCamelCase__:




				def __init__(	self    :				str   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Union[str, Any]=13   ,					lowerCAmelCase    :				Tuple=7   ,					lowerCAmelCase    :				int=True   ,					lowerCAmelCase    :				List[str]=True   ,					lowerCAmelCase    :				Union[str, Any]=False   ,					lowerCAmelCase    :				Union[str, Any]=True   ,					lowerCAmelCase    :				int=99   ,					lowerCAmelCase    :				Optional[Any]=32   ,					lowerCAmelCase    :				Optional[Any]=5   ,					lowerCAmelCase    :				Tuple=4   ,					lowerCAmelCase    :				Optional[Any]=37   ,					lowerCAmelCase    :				Any="gelu"   ,					lowerCAmelCase    :				Optional[int]=0.1   ,					lowerCAmelCase    :				Optional[Any]=0.1   ,					lowerCAmelCase    :				Any=512   ,					lowerCAmelCase    :				Any=16   ,					lowerCAmelCase    :				List[Any]=2   ,					lowerCAmelCase    :				Tuple=0.02   ,					lowerCAmelCase    :				Optional[Any]=3   ,					lowerCAmelCase    :				List[Any]=4   ,					lowerCAmelCase    :				Any=None   ,					)->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			parent
										UpperCAmelCase         =			batch_size
										UpperCAmelCase         =			seq_length
										UpperCAmelCase         =			is_training
										UpperCAmelCase         =			use_input_mask
										UpperCAmelCase         =			use_token_type_ids
										UpperCAmelCase         =			use_labels
										UpperCAmelCase         =			vocab_size
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_hidden_layers
										UpperCAmelCase         =			num_attention_heads
										UpperCAmelCase         =			intermediate_size
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			hidden_dropout_prob
										UpperCAmelCase         =			attention_probs_dropout_prob
										UpperCAmelCase         =			max_position_embeddings
										UpperCAmelCase         =			type_vocab_size
										UpperCAmelCase         =			type_sequence_label_size
										UpperCAmelCase         =			initializer_range
										UpperCAmelCase         =			num_labels
										UpperCAmelCase         =			num_choices
										UpperCAmelCase         =			scope




				def   a__(	self    :				Optional[int]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			ids_tensor([self.batch_size, self.seq_length]   ,					self.vocab_size   )

										UpperCAmelCase         =			None
										if self.use_input_mask:
																UpperCAmelCase         =			random_attention_mask([self.batch_size, self.seq_length]   )

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

										UpperCAmelCase         =			None
										UpperCAmelCase         =			None
										UpperCAmelCase         =			None
										if self.use_labels:
																UpperCAmelCase         =			ids_tensor([self.batch_size]   ,					self.type_sequence_label_size   )
																UpperCAmelCase         =			ids_tensor([self.batch_size, self.seq_length]   ,					self.num_labels   )
																UpperCAmelCase         =			ids_tensor([self.batch_size]   ,					self.num_choices   )

										UpperCAmelCase         =			self.get_config()

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




				def   a__(	self    :				Optional[Any]   )->  int:

										"""simple docstring"""




										return OpenLlamaConfig(
										    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   ,					use_stable_embedding=lowerCAmelCase   ,					)




				def   a__(	self    :				int   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Any   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			OpenLlamaModel(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   )
										UpperCAmelCase         =			model(lowerCAmelCase   )
										self.parent.assertEqual(result.last_hidden_state.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )




				def   a__(	self    :				Any   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Union[str, Any]   ,					)->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			True
										UpperCAmelCase         =			OpenLlamaModel(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(
										    lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ,					encoder_attention_mask=lowerCAmelCase   ,					)
										UpperCAmelCase         =			model(
										    lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ,					)
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   )
										self.parent.assertEqual(result.last_hidden_state.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				int   ,					)->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			OpenLlamaForCausalLM(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					labels=lowerCAmelCase   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.seq_length, self.vocab_size)   )




				def   a__(	self    :				int   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Tuple   ,					)->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			True
										UpperCAmelCase         =			True
										UpperCAmelCase         =			OpenLlamaForCausalLM(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()

										# first forward pass
										UpperCAmelCase         =			model(
										    lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ,					encoder_attention_mask=lowerCAmelCase   ,					use_cache=lowerCAmelCase   ,					)
										UpperCAmelCase         =			outputs.past_key_values

										# create hypothetical multiple next token and extent to next_input_ids
										UpperCAmelCase         =			ids_tensor((self.batch_size, 3)   ,					config.vocab_size   )
										UpperCAmelCase         =			ids_tensor((self.batch_size, 3)   ,					vocab_size=2   )

										# append to next input_ids and
										UpperCAmelCase         =			torch.cat([input_ids, next_tokens]   ,					dim=-1   )
										UpperCAmelCase         =			torch.cat([input_mask, next_mask]   ,					dim=-1   )

										UpperCAmelCase         =			model(
										    lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ,					encoder_attention_mask=lowerCAmelCase   ,					output_hidden_states=lowerCAmelCase   ,					)['''hidden_states'''][0]
										UpperCAmelCase         =			model(
										    lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ,					encoder_attention_mask=lowerCAmelCase   ,					past_key_values=lowerCAmelCase   ,					output_hidden_states=lowerCAmelCase   ,					)['''hidden_states'''][0]

										# select random slice
										UpperCAmelCase         =			ids_tensor((1,)   ,					output_from_past.shape[-1]   ).item()
										UpperCAmelCase         =			output_from_no_past[:, -3:, random_slice_idx].detach()
										UpperCAmelCase         =			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   a__(	self    :				Optional[int]   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			self.prepare_config_and_inputs()
										(
										    (
										    UpperCAmelCase       
										)       ,       (
										    UpperCAmelCase       
										)       ,       (
										    UpperCAmelCase       
										)       ,       (
										    UpperCAmelCase       
										)       ,       (
										    UpperCAmelCase       
										)       ,       (
										    UpperCAmelCase       
										)       ,       (
										    UpperCAmelCase       
										)       ,              
										)         =			config_and_inputs
										UpperCAmelCase         =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict







@require_torch
class  UpperCamelCase__(     lowerCAmelCase		,       lowerCAmelCase		,       lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   int							= (
				    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
				)
				__magic_name__       :   int							= (OpenLlamaForCausalLM,) if is_torch_available() else ()
				__magic_name__       :   Tuple							= (
				    {
				        "feature-extraction": OpenLlamaModel,
				        "text-classification": OpenLlamaForSequenceClassification,
				        "text-generation": OpenLlamaForCausalLM,
				        "zero-shot": OpenLlamaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__magic_name__       :   Dict							= False
				__magic_name__       :   str							= False




				def   a__(	self    :				int   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			OpenLlamaModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   ,					hidden_size=37   )




				def   a__(	self    :				Tuple   )->  Optional[Any]:

										"""simple docstring"""




										self.config_tester.run_common_tests()




				def   a__(	self    :				Optional[int]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase   )




				def   a__(	self    :				Dict   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																UpperCAmelCase         =			type
																self.model_tester.create_and_check_model(*lowerCAmelCase   )




				def   a__(	self    :				Union[str, Any]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase         =			3
										UpperCAmelCase         =			input_dict['''input_ids''']
										UpperCAmelCase         =			input_ids.ne(1   ).to(lowerCAmelCase   )
										UpperCAmelCase         =			ids_tensor([self.model_tester.batch_size]   ,					self.model_tester.type_sequence_label_size   )
										UpperCAmelCase         =			OpenLlamaForSequenceClassification(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					labels=lowerCAmelCase   )
										self.assertEqual(result.logits.shape   ,					(self.model_tester.batch_size, self.model_tester.num_labels)   )




				def   a__(	self    :				List[str]   )->  int:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase         =			3
										UpperCAmelCase         =			'''single_label_classification'''
										UpperCAmelCase         =			input_dict['''input_ids''']
										UpperCAmelCase         =			input_ids.ne(1   ).to(lowerCAmelCase   )
										UpperCAmelCase         =			ids_tensor([self.model_tester.batch_size]   ,					self.model_tester.type_sequence_label_size   )
										UpperCAmelCase         =			OpenLlamaForSequenceClassification(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					labels=lowerCAmelCase   )
										self.assertEqual(result.logits.shape   ,					(self.model_tester.batch_size, self.model_tester.num_labels)   )




				def   a__(	self    :				Tuple   )->  str:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase         =			3
										UpperCAmelCase         =			'''multi_label_classification'''
										UpperCAmelCase         =			input_dict['''input_ids''']
										UpperCAmelCase         =			input_ids.ne(1   ).to(lowerCAmelCase   )
										UpperCAmelCase         =			ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]   ,					self.model_tester.type_sequence_label_size   ).to(torch.float   )
										UpperCAmelCase         =			OpenLlamaForSequenceClassification(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					labels=lowerCAmelCase   )
										self.assertEqual(result.logits.shape   ,					(self.model_tester.batch_size, self.model_tester.num_labels)   )




				@unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test'''   )
				def   a__(	self    :				Optional[int]   )->  str:

										"""simple docstring"""




										pass





				@parameterized.expand([('''linear''',), ('''dynamic''',)]   )
				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				Union[str, Any]   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase         =			ids_tensor([1, 10]   ,					config.vocab_size   )
										UpperCAmelCase         =			ids_tensor([1, int(config.max_position_embeddings * 1.5   )]   ,					config.vocab_size   )

										set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
										UpperCAmelCase         =			OpenLlamaModel(lowerCAmelCase   )
										original_model.to(lowerCAmelCase   )
										original_model.eval()
										UpperCAmelCase         =			original_model(lowerCAmelCase   ).last_hidden_state
										UpperCAmelCase         =			original_model(lowerCAmelCase   ).last_hidden_state

										set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
										UpperCAmelCase         =			{'''type''': scaling_type, '''factor''': 10.0}
										UpperCAmelCase         =			OpenLlamaModel(lowerCAmelCase   )
										scaled_model.to(lowerCAmelCase   )
										scaled_model.eval()
										UpperCAmelCase         =			scaled_model(lowerCAmelCase   ).last_hidden_state
										UpperCAmelCase         =			scaled_model(lowerCAmelCase   ).last_hidden_state

										# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
										# maximum sequence length, so the outputs for the short input should match.
										if scaling_type == "dynamic":
																self.assertTrue(torch.allclose(lowerCAmelCase   ,					lowerCAmelCase   ,					atol=1E-5   )   )
										else:
																self.assertFalse(torch.allclose(lowerCAmelCase   ,					lowerCAmelCase   ,					atol=1E-5   )   )

										# The output should be different for long inputs
										self.assertFalse(torch.allclose(lowerCAmelCase   ,					lowerCAmelCase   ,					atol=1E-5   )   )









'''simple docstring'''



import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_lowercase    :    Tuple			     =  {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def    lowerCamelCase__   ( A     :    Optional[Any]  ):




						'''simple docstring'''






						assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
						assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
						if args.mlm:
												assert os.path.isfile(args.token_counts  )
												assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
						else:
												assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

						assert args.teacher_type == args.student_type or (
						    args.student_type == "distilbert" and args.teacher_type == "bert"
						)
						assert os.path.isfile(args.student_config  )
						if args.student_pretrained_weights is not None:
												assert os.path.isfile(args.student_pretrained_weights  )

						if args.freeze_token_type_embds:
												assert args.student_type in ["roberta"]

						assert args.alpha_ce >= 0.0
						assert args.alpha_mlm >= 0.0
						assert args.alpha_clm >= 0.0
						assert args.alpha_mse >= 0.0
						assert args.alpha_cos >= 0.0
						assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def    lowerCamelCase__   ( A     :    Any   ,     A     :    str  ):




						'''simple docstring'''






						if args.student_type == "roberta":
												UpperCAmelCase         =			False
						elif args.student_type == "gpt2":
												UpperCAmelCase         =			False


def    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    List[str]  ):




						'''simple docstring'''






						if args.student_type == "roberta":
												UpperCAmelCase         =			False


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			argparse.ArgumentParser(description='''Training'''  )
						parser.add_argument('''--force'''   ,     action='''store_true'''   ,     help='''Overwrite dump_path if it already exists.'''  )

						parser.add_argument(
						    '''--dump_path'''   ,     type=A   ,     required=A   ,     help='''The output directory (log, checkpoints, parameters, etc.)'''  )
						parser.add_argument(
						    '''--data_file'''   ,     type=A   ,     required=A   ,     help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''   ,     )

						parser.add_argument(
						    '''--student_type'''   ,     type=A   ,     choices=['''distilbert''', '''roberta''', '''gpt2''']   ,     required=A   ,     help='''The student type (DistilBERT, RoBERTa).'''   ,     )
						parser.add_argument('''--student_config'''   ,     type=A   ,     required=A   ,     help='''Path to the student configuration.'''  )
						parser.add_argument(
						    '''--student_pretrained_weights'''   ,     default=A   ,     type=A   ,     help='''Load student initialization checkpoint.'''  )

						parser.add_argument(
						    '''--teacher_type'''   ,     choices=['''bert''', '''roberta''', '''gpt2''']   ,     required=A   ,     help='''Teacher type (BERT, RoBERTa).'''  )
						parser.add_argument('''--teacher_name'''   ,     type=A   ,     required=A   ,     help='''The teacher model.'''  )

						parser.add_argument('''--temperature'''   ,     default=2.0   ,     type=A   ,     help='''Temperature for the softmax temperature.'''  )
						parser.add_argument(
						    '''--alpha_ce'''   ,     default=0.5   ,     type=A   ,     help='''Linear weight for the distillation loss. Must be >=0.'''  )
						parser.add_argument(
						    '''--alpha_mlm'''   ,     default=0.0   ,     type=A   ,     help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'''   ,     )
						parser.add_argument('''--alpha_clm'''   ,     default=0.5   ,     type=A   ,     help='''Linear weight for the CLM loss. Must be >=0.'''  )
						parser.add_argument('''--alpha_mse'''   ,     default=0.0   ,     type=A   ,     help='''Linear weight of the MSE loss. Must be >=0.'''  )
						parser.add_argument(
						    '''--alpha_cos'''   ,     default=0.0   ,     type=A   ,     help='''Linear weight of the cosine embedding loss. Must be >=0.'''  )

						parser.add_argument(
						    '''--mlm'''   ,     action='''store_true'''   ,     help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''  )
						parser.add_argument(
						    '''--mlm_mask_prop'''   ,     default=0.15   ,     type=A   ,     help='''Proportion of tokens for which we need to make a prediction.'''   ,     )
						parser.add_argument('''--word_mask'''   ,     default=0.8   ,     type=A   ,     help='''Proportion of tokens to mask out.'''  )
						parser.add_argument('''--word_keep'''   ,     default=0.1   ,     type=A   ,     help='''Proportion of tokens to keep.'''  )
						parser.add_argument('''--word_rand'''   ,     default=0.1   ,     type=A   ,     help='''Proportion of tokens to randomly replace.'''  )
						parser.add_argument(
						    '''--mlm_smoothing'''   ,     default=0.7   ,     type=A   ,     help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''   ,     )
						parser.add_argument('''--token_counts'''   ,     type=A   ,     help='''The token counts in the data_file for MLM.'''  )

						parser.add_argument(
						    '''--restrict_ce_to_mask'''   ,     action='''store_true'''   ,     help='''If true, compute the distillation loss only the [MLM] prediction distribution.'''   ,     )
						parser.add_argument(
						    '''--freeze_pos_embs'''   ,     action='''store_true'''   ,     help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'''   ,     )
						parser.add_argument(
						    '''--freeze_token_type_embds'''   ,     action='''store_true'''   ,     help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'''   ,     )

						parser.add_argument('''--n_epoch'''   ,     type=A   ,     default=3   ,     help='''Number of pass on the whole dataset.'''  )
						parser.add_argument('''--batch_size'''   ,     type=A   ,     default=5   ,     help='''Batch size (for each process).'''  )
						parser.add_argument(
						    '''--group_by_size'''   ,     action='''store_false'''   ,     help='''If true, group sequences that have similar length into the same batch. Default is true.'''   ,     )

						parser.add_argument(
						    '''--gradient_accumulation_steps'''   ,     type=A   ,     default=50   ,     help='''Gradient accumulation for larger training batches.'''   ,     )
						parser.add_argument('''--warmup_prop'''   ,     default=0.05   ,     type=A   ,     help='''Linear warmup proportion.'''  )
						parser.add_argument('''--weight_decay'''   ,     default=0.0   ,     type=A   ,     help='''Weight decay if we apply some.'''  )
						parser.add_argument('''--learning_rate'''   ,     default=5E-4   ,     type=A   ,     help='''The initial learning rate for Adam.'''  )
						parser.add_argument('''--adam_epsilon'''   ,     default=1E-6   ,     type=A   ,     help='''Epsilon for Adam optimizer.'''  )
						parser.add_argument('''--max_grad_norm'''   ,     default=5.0   ,     type=A   ,     help='''Max gradient norm.'''  )
						parser.add_argument('''--initializer_range'''   ,     default=0.02   ,     type=A   ,     help='''Random initialization range.'''  )

						parser.add_argument(
						    '''--fp16'''   ,     action='''store_true'''   ,     help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''   ,     )
						parser.add_argument(
						    '''--fp16_opt_level'''   ,     type=A   ,     default='''O1'''   ,     help=(
						        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
						        '''See details at https://nvidia.github.io/apex/amp.html'''
						    )   ,     )
						parser.add_argument('''--n_gpu'''   ,     type=A   ,     default=1   ,     help='''Number of GPUs in the node.'''  )
						parser.add_argument('''--local_rank'''   ,     type=A   ,     default=-1   ,     help='''Distributed training - Local rank'''  )
						parser.add_argument('''--seed'''   ,     type=A   ,     default=56   ,     help='''Random seed'''  )

						parser.add_argument('''--log_interval'''   ,     type=A   ,     default=5_00   ,     help='''Tensorboard logging interval.'''  )
						parser.add_argument('''--checkpoint_interval'''   ,     type=A   ,     default=40_00   ,     help='''Checkpoint interval.'''  )
						UpperCAmelCase         =			parser.parse_args()
						sanity_checks(A  )

						# ARGS #
						init_gpu_params(A  )
						set_seed(A  )
						if args.is_master:
												if os.path.exists(args.dump_path  ):
																		if not args.force:
																								raise ValueError(
																								    f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
																								    ''' itUse `--force` if you want to overwrite it'''  )
																		else:
																								shutil.rmtree(args.dump_path  )

												if not os.path.exists(args.dump_path  ):
																		os.makedirs(args.dump_path  )
												logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""  )

												# SAVE PARAMS #
												logger.info(f"""Param: {args}"""  )
												with open(os.path.join(args.dump_path   ,     '''parameters.json'''  )   ,     '''w'''  ) as f:
																		json.dump(vars(A  )   ,     A   ,     indent=4  )
												git_log(args.dump_path  )

						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			MODEL_CLASSES[args.student_type]
						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						UpperCAmelCase         =			teacher_tokenizer_class.from_pretrained(args.teacher_name  )
						UpperCAmelCase         =			{}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												UpperCAmelCase         =			tokenizer.all_special_tokens.index(A  )
												UpperCAmelCase         =			tokenizer.all_special_ids[idx]
						logger.info(f"""Special tokens {special_tok_ids}"""  )
						UpperCAmelCase         =			special_tok_ids
						UpperCAmelCase         =			tokenizer.max_model_input_sizes[args.teacher_name]

						# DATA LOADER #
						logger.info(f"""Loading data from {args.data_file}"""  )
						with open(args.data_file   ,     '''rb'''  ) as fp:
												UpperCAmelCase         =			pickle.load(A  )

						if args.mlm:
												logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""  )
												with open(args.token_counts   ,     '''rb'''  ) as fp:
																		UpperCAmelCase         =			pickle.load(A  )

												UpperCAmelCase         =			np.maximum(A   ,     1  ) ** -args.mlm_smoothing
												for idx in special_tok_ids.values():
																		UpperCAmelCase         =			0.0  # do not predict special tokens
												UpperCAmelCase         =			torch.from_numpy(A  )
						else:
												UpperCAmelCase         =			None

						UpperCAmelCase         =			LmSeqsDataset(params=A   ,     data=A  )
						logger.info('''Data loader created.'''  )

						# STUDENT #
						logger.info(f"""Loading student config from {args.student_config}"""  )
						UpperCAmelCase         =			student_config_class.from_pretrained(args.student_config  )
						UpperCAmelCase         =			True

						if args.student_pretrained_weights is not None:
												logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""  )
												UpperCAmelCase         =			student_model_class.from_pretrained(args.student_pretrained_weights   ,     config=A  )
						else:
												UpperCAmelCase         =			student_model_class(A  )

						if args.n_gpu > 0:
												student.to(f"""cuda:{args.local_rank}"""  )
						logger.info('''Student loaded.'''  )

						# TEACHER #
						UpperCAmelCase         =			teacher_model_class.from_pretrained(args.teacher_name   ,     output_hidden_states=A  )
						if args.n_gpu > 0:
												teacher.to(f"""cuda:{args.local_rank}"""  )
						logger.info(f"""Teacher loaded from {args.teacher_name}."""  )

						# FREEZING #
						if args.freeze_pos_embs:
												freeze_pos_embeddings(A   ,     A  )
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(A   ,     A  )

						# SANITY CHECKS #
						assert student.config.vocab_size == teacher.config.vocab_size
						assert student.config.hidden_size == teacher.config.hidden_size
						assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
						if args.mlm:
												assert token_probs.size(0  ) == stu_architecture_config.vocab_size

						# DISTILLER #
						torch.cuda.empty_cache()
						UpperCAmelCase         =			Distiller(
						    params=A   ,     dataset=A   ,     token_probs=A   ,     student=A   ,     teacher=A  )
						distiller.train()
						logger.info('''Let\'s go get some drinks.'''  )


if __name__ == "__main__":
	main()





