



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


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


def 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		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 			UpperCamelCase(  __UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		int ):
		if args.student_type == "roberta":
				lowerCAmelCase_			:     Optional[Any]									= False
		elif args.student_type == "gpt2":
				lowerCAmelCase_			:     str									= False


def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		Union[str, Any] ):
		if args.student_type == "roberta":
				lowerCAmelCase_			:     Optional[Any]									= False


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Optional[int]									= 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=SCREAMING_SNAKE_CASE__     ,required=SCREAMING_SNAKE_CASE__     ,help='''The output directory (log, checkpoints, parameters, etc.)''' )
		parser.add_argument(
		    '''--data_file'''     ,type=SCREAMING_SNAKE_CASE__     ,required=SCREAMING_SNAKE_CASE__     ,help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''     ,)

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

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

		parser.add_argument('''--temperature'''     ,default=2.0     ,type=SCREAMING_SNAKE_CASE__     ,help='''Temperature for the softmax temperature.''' )
		parser.add_argument(
		    '''--alpha_ce'''     ,default=0.5     ,type=SCREAMING_SNAKE_CASE__     ,help='''Linear weight for the distillation loss. Must be >=0.''' )
		parser.add_argument(
		    '''--alpha_mlm'''     ,default=0.0     ,type=SCREAMING_SNAKE_CASE__     ,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=SCREAMING_SNAKE_CASE__     ,help='''Linear weight for the CLM loss. Must be >=0.''' )
		parser.add_argument('''--alpha_mse'''     ,default=0.0     ,type=SCREAMING_SNAKE_CASE__     ,help='''Linear weight of the MSE loss. Must be >=0.''' )
		parser.add_argument(
		    '''--alpha_cos'''     ,default=0.0     ,type=SCREAMING_SNAKE_CASE__     ,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=0.1_5     ,type=SCREAMING_SNAKE_CASE__     ,help='''Proportion of tokens for which we need to make a prediction.'''     ,)
		parser.add_argument('''--word_mask'''     ,default=0.8     ,type=SCREAMING_SNAKE_CASE__     ,help='''Proportion of tokens to mask out.''' )
		parser.add_argument('''--word_keep'''     ,default=0.1     ,type=SCREAMING_SNAKE_CASE__     ,help='''Proportion of tokens to keep.''' )
		parser.add_argument('''--word_rand'''     ,default=0.1     ,type=SCREAMING_SNAKE_CASE__     ,help='''Proportion of tokens to randomly replace.''' )
		parser.add_argument(
		    '''--mlm_smoothing'''     ,default=0.7     ,type=SCREAMING_SNAKE_CASE__     ,help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''     ,)
		parser.add_argument('''--token_counts'''     ,type=SCREAMING_SNAKE_CASE__     ,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=SCREAMING_SNAKE_CASE__     ,default=3     ,help='''Number of pass on the whole dataset.''' )
		parser.add_argument('''--batch_size'''     ,type=SCREAMING_SNAKE_CASE__     ,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=SCREAMING_SNAKE_CASE__     ,default=50     ,help='''Gradient accumulation for larger training batches.'''     ,)
		parser.add_argument('''--warmup_prop'''     ,default=0.0_5     ,type=SCREAMING_SNAKE_CASE__     ,help='''Linear warmup proportion.''' )
		parser.add_argument('''--weight_decay'''     ,default=0.0     ,type=SCREAMING_SNAKE_CASE__     ,help='''Weight decay if we apply some.''' )
		parser.add_argument('''--learning_rate'''     ,default=5e-4     ,type=SCREAMING_SNAKE_CASE__     ,help='''The initial learning rate for Adam.''' )
		parser.add_argument('''--adam_epsilon'''     ,default=1e-6     ,type=SCREAMING_SNAKE_CASE__     ,help='''Epsilon for Adam optimizer.''' )
		parser.add_argument('''--max_grad_norm'''     ,default=5.0     ,type=SCREAMING_SNAKE_CASE__     ,help='''Max gradient norm.''' )
		parser.add_argument('''--initializer_range'''     ,default=0.0_2     ,type=SCREAMING_SNAKE_CASE__     ,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=SCREAMING_SNAKE_CASE__     ,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=SCREAMING_SNAKE_CASE__     ,default=1     ,help='''Number of GPUs in the node.''' )
		parser.add_argument('''--local_rank'''     ,type=SCREAMING_SNAKE_CASE__     ,default=-1     ,help='''Distributed training - Local rank''' )
		parser.add_argument('''--seed'''     ,type=SCREAMING_SNAKE_CASE__     ,default=56     ,help='''Random seed''' )

		parser.add_argument('''--log_interval'''     ,type=SCREAMING_SNAKE_CASE__     ,default=500     ,help='''Tensorboard logging interval.''' )
		parser.add_argument('''--checkpoint_interval'''     ,type=SCREAMING_SNAKE_CASE__     ,default=4000     ,help='''Checkpoint interval.''' )
		lowerCAmelCase_			:     Optional[Any]									= parser.parse_args()
		sanity_checks(SCREAMING_SNAKE_CASE__ )

		# ARGS #
		init_gpu_params(SCREAMING_SNAKE_CASE__ )
		set_seed(SCREAMING_SNAKE_CASE__ )
		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(SCREAMING_SNAKE_CASE__ )     ,SCREAMING_SNAKE_CASE__     ,indent=4 )
				git_log(args.dump_path )

		lowerCAmelCase_			:     List[str]									= MODEL_CLASSES[args.student_type]
		lowerCAmelCase_			:     List[str]									= MODEL_CLASSES[args.teacher_type]

		# TOKENIZER #
		lowerCAmelCase_			:     List[Any]									= teacher_tokenizer_class.from_pretrained(args.teacher_name )
		lowerCAmelCase_			:     Any									= {}
		for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
				lowerCAmelCase_			:     Optional[int]									= tokenizer.all_special_tokens.index(SCREAMING_SNAKE_CASE__ )
				lowerCAmelCase_			:     List[Any]									= tokenizer.all_special_ids[idx]
		logger.info(f"""Special tokens {special_tok_ids}""" )
		lowerCAmelCase_			:     List[str]									= special_tok_ids
		lowerCAmelCase_			:     str									= 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:
				lowerCAmelCase_			:     List[Any]									= pickle.load(SCREAMING_SNAKE_CASE__ )

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

				lowerCAmelCase_			:     List[Any]									= np.maximum(SCREAMING_SNAKE_CASE__     ,1 ) ** -args.mlm_smoothing
				for idx in special_tok_ids.values():
						lowerCAmelCase_			:     Tuple									= 0.0  # do not predict special tokens
				lowerCAmelCase_			:     Any									= torch.from_numpy(SCREAMING_SNAKE_CASE__ )
		else:
				lowerCAmelCase_			:     Any									= None

		lowerCAmelCase_			:     int									= LmSeqsDataset(params=SCREAMING_SNAKE_CASE__     ,data=SCREAMING_SNAKE_CASE__ )
		logger.info('''Data loader created.''' )

		# STUDENT #
		logger.info(f"""Loading student config from {args.student_config}""" )
		lowerCAmelCase_			:     Union[str, Any]									= student_config_class.from_pretrained(args.student_config )
		lowerCAmelCase_			:     int									= True

		if args.student_pretrained_weights is not None:
				logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}""" )
				lowerCAmelCase_			:     List[Any]									= student_model_class.from_pretrained(args.student_pretrained_weights     ,config=SCREAMING_SNAKE_CASE__ )
		else:
				lowerCAmelCase_			:     str									= student_model_class(SCREAMING_SNAKE_CASE__ )

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

		# TEACHER #
		lowerCAmelCase_			:     Union[str, Any]									= teacher_model_class.from_pretrained(args.teacher_name     ,output_hidden_states=SCREAMING_SNAKE_CASE__ )
		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(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__ )
		if args.freeze_token_type_embds:
				freeze_token_type_embeddings(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__ )

		# 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()
		lowerCAmelCase_			:     List[str]									= Distiller(
		    params=SCREAMING_SNAKE_CASE__     ,dataset=SCREAMING_SNAKE_CASE__     ,token_probs=SCREAMING_SNAKE_CASE__     ,student=SCREAMING_SNAKE_CASE__     ,teacher=SCREAMING_SNAKE_CASE__ )
		distiller.train()
		logger.info('''Let\'s go get some drinks.''' )


if __name__ == "__main__":
						main()












import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
      import tensorflow as tf

      from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments







@require_tf
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Any							,				A :    Union[str, Any]		):
         for model_result in results.values():
           for batch_size, sequence_length in zip(model_result["""bs"""]							,				model_result["""ss"""]		):
             __snake_case:						Dict   =						model_result["""result"""][batch_size][sequence_length]
             self.assertIsNotNone(A		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				eager_mode=A							,				multi_process=A							,				)
         __snake_case:						Dict   =						TensorFlowBenchmark(A		)
         __snake_case:						List[str]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Optional[int]   =						"""sgugger/tiny-distilbert-classification"""
         __snake_case:						str   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				only_pretrain_model=A							,				)
         __snake_case:						Any   =						TensorFlowBenchmark(A		)
         __snake_case:						List[Any]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						int   =						TensorFlowBenchmark(A		)
         __snake_case:						str   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						int   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Optional[Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				eager_mode=A							,				multi_process=A							,				)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Optional[int]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Tuple   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						str   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						str   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Tuple   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						str   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						Tuple   =						TensorFlowBenchmark(A		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Optional[int]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						List[str]   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						Dict   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						List[Any]   =						"""patrickvonplaten/t5-tiny-random"""
         __snake_case:						List[str]   =						AutoConfig.from_pretrained(A		)
         __snake_case:						List[str]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						Optional[int]   =						TensorFlowBenchmark(A							,				configs=[config]		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""		)		) == 0							,				"""Cannot do xla on CPU."""		)
       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						List[Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				use_xla=A							,				multi_process=A							,				)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmark(A		)
         __snake_case:						List[Any]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						List[str]   =						"""sshleifer/tiny-gpt2"""
         with tempfile.TemporaryDirectory() as tmp_dir:
           __snake_case:						int   =						TensorFlowBenchmarkArguments(
               models=[MODEL_ID]							,				inference=A							,				save_to_csv=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				inference_time_csv_file=os.path.join(A							,				"""inf_time.csv"""		)							,				inference_memory_csv_file=os.path.join(A							,				"""inf_mem.csv"""		)							,				env_info_csv_file=os.path.join(A							,				"""env.csv"""		)							,				multi_process=A							,				)
           __snake_case:						Tuple   =						TensorFlowBenchmark(A		)
           benchmark.run()
           self.assertTrue(Path(os.path.join(A							,				"""inf_time.csv"""		)		).exists()		)
           self.assertTrue(Path(os.path.join(A							,				"""inf_mem.csv"""		)		).exists()		)
           self.assertTrue(Path(os.path.join(A							,				"""env.csv"""		)		).exists()		)






       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Any   =						"""sshleifer/tiny-gpt2"""

         def _check_summary_is_not_empty(A :    Union[str, Any]		):
           self.assertTrue(hasattr(A							,				"""sequential"""		)		)
           self.assertTrue(hasattr(A							,				"""cumulative"""		)		)
           self.assertTrue(hasattr(A							,				"""current"""		)		)
           self.assertTrue(hasattr(A							,				"""total"""		)		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
               models=[MODEL_ID]							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				log_filename=os.path.join(A							,				"""log.txt"""		)							,				log_print=A							,				trace_memory_line_by_line=A							,				eager_mode=A							,				multi_process=A							,				)
           __snake_case:						Dict   =						TensorFlowBenchmark(A		)
           __snake_case:						List[str]   =						benchmark.run()
           _check_summary_is_not_empty(result.inference_summary		)
           self.assertTrue(Path(os.path.join(A							,				"""log.txt"""		)		).exists()		)






