
"""simple docstring"""







import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=True	,  _UpperCamelCase="pt"		):
	__lowerCAmelCase      :						List[str]						      =				{'add_prefix_space': True} if isinstance(_UpperCamelCase	,  _UpperCamelCase		) and not line.startswith(' '		) else {}
	__lowerCAmelCase      :						Union[str, Any]						      =				padding_side
	return tokenizer(
	    [line]	,  max_length=_UpperCamelCase	,  padding='max_length' if pad_to_max_length else None	,  truncation=_UpperCamelCase	,  return_tensors=_UpperCamelCase	,  add_special_tokens=_UpperCamelCase	,  **_UpperCamelCase	,  )
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=None	,  ):
	__lowerCAmelCase      :						Optional[int]						      =				input_ids.ne(_UpperCamelCase		).any(dim=0		)
	if attention_mask is None:
		return input_ids[:, keep_column_mask]
	else:
		return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])




class 			A__	(     _lowerCamelCase):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE="train"	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=""	,		):
			super().__init__()
			__lowerCAmelCase      :						List[str]						      =				Path(_SCREAMING_SNAKE_CASE			).joinpath(type_path + '.source'			)
			__lowerCAmelCase      :						Any						      =				Path(_SCREAMING_SNAKE_CASE			).joinpath(type_path + '.target'			)
			__lowerCAmelCase      :						Any						      =				self.get_char_lens(self.src_file			)
			__lowerCAmelCase      :						Optional[int]						      =				max_source_length
			__lowerCAmelCase      :						Any						      =				max_target_length
			assert min(self.src_lens			) > 0, f"found empty line in {self.src_file}"
			__lowerCAmelCase      :						Optional[int]						      =				tokenizer
			__lowerCAmelCase      :						Tuple						      =				prefix
			if n_obs is not None:
				__lowerCAmelCase      :						int						      =				self.src_lens[:n_obs]
			__lowerCAmelCase      :						Union[str, Any]						      =				src_lang
			__lowerCAmelCase      :						List[Any]						      =				tgt_lang

		def __len__(				self			):
			return len(self.src_lens			)

		def __getitem__(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						str						      =				index + 1  # linecache starts at 1
			__lowerCAmelCase      :						int						      =				self.prefix + linecache.getline(str(self.src_file			)	,		_SCREAMING_SNAKE_CASE			).rstrip('\n'			)
			__lowerCAmelCase      :						Tuple						      =				linecache.getline(str(self.tgt_file			)	,		_SCREAMING_SNAKE_CASE			).rstrip('\n'			)
			assert source_line, f"empty source line for index {index}"
			assert tgt_line, f"empty tgt line for index {index}"

			# Need to add eos token manually for T5
			if isinstance(self.tokenizer	,		_SCREAMING_SNAKE_CASE			):
				source_line += self.tokenizer.eos_token
				tgt_line += self.tokenizer.eos_token

			# Pad source and target to the right
			__lowerCAmelCase      :						List[Any]						      =				(
			    self.tokenizer.question_encoder if isinstance(self.tokenizer	,		_SCREAMING_SNAKE_CASE			) else self.tokenizer
			)
			__lowerCAmelCase      :						Optional[Any]						      =				self.tokenizer.generator if isinstance(self.tokenizer	,		_SCREAMING_SNAKE_CASE			) else self.tokenizer

			__lowerCAmelCase      :						Any						      =				encode_line(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		self.max_source_length	,		'right'			)
			__lowerCAmelCase      :						int						      =				encode_line(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		self.max_target_length	,		'right'			)

			__lowerCAmelCase      :						Union[str, Any]						      =				source_inputs['input_ids'].squeeze()
			__lowerCAmelCase      :						str						      =				target_inputs['input_ids'].squeeze()
			__lowerCAmelCase      :						Optional[Any]						      =				source_inputs['attention_mask'].squeeze()
			return {
			    "input_ids": source_ids,
			    "attention_mask": src_mask,
			    "decoder_input_ids": target_ids,
			}

		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE			):
			return [len(_SCREAMING_SNAKE_CASE			) for x in Path(_SCREAMING_SNAKE_CASE			).open().readlines()]







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Any						      =				torch.stack([x['input_ids'] for x in batch]			)
			__lowerCAmelCase      :						Any						      =				torch.stack([x['attention_mask'] for x in batch]			)
			__lowerCAmelCase      :						Tuple						      =				torch.stack([x['decoder_input_ids'] for x in batch]			)
			__lowerCAmelCase      :						Any						      =				(
			    self.tokenizer.generator.pad_token_id
			    if isinstance(self.tokenizer	,		_SCREAMING_SNAKE_CASE			)
			    else self.tokenizer.pad_token_id
			)
			__lowerCAmelCase      :						Union[str, Any]						      =				(
			    self.tokenizer.question_encoder.pad_token_id
			    if isinstance(self.tokenizer	,		_SCREAMING_SNAKE_CASE			)
			    else self.tokenizer.pad_token_id
			)
			__lowerCAmelCase      :						Tuple						      =				trim_batch(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      ,       __lowerCAmelCase      :						int						      =				trim_batch(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				{
			    'input_ids': source_ids,
			    'attention_mask': source_mask,
			    'decoder_input_ids': y,
			}
			return batch



lowerCamelCase__        =		getLogger(__name__)
def    __lowerCAmelCase    (_UpperCamelCase		):
	return list(itertools.chain.from_iterable(_UpperCamelCase		)		)
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Optional[Any]						      =				get_git_info()
	save_json(_UpperCamelCase	,  os.path.join(_UpperCamelCase	,  'git_log.json'		)		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=4	,  **_UpperCamelCase		):
	with open(_UpperCamelCase	,  'w'		) as f:
		json.dump(_UpperCamelCase	,  _UpperCamelCase	,  indent=_UpperCamelCase	,  **_UpperCamelCase		)
def    __lowerCAmelCase    (_UpperCamelCase		):
	with open(_UpperCamelCase		) as f:
		return json.load(_UpperCamelCase		)
def    __lowerCAmelCase    ():
	__lowerCAmelCase      :						Any						      =				git.Repo(search_parent_directories=_UpperCamelCase		)
	__lowerCAmelCase      :						int						      =				{
	    'repo_id': str(_UpperCamelCase		),
	    'repo_sha': str(repo.head.object.hexsha		),
	    'repo_branch': str(repo.active_branch		),
	    'hostname': str(socket.gethostname()		),
	}
	return repo_infos
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	return list(map(_UpperCamelCase	,  _UpperCamelCase		)		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	with open(_UpperCamelCase	,  'wb'		) as f:
		return pickle.dump(_UpperCamelCase	,  _UpperCamelCase		)
def    __lowerCAmelCase    (_UpperCamelCase		):

	def remove_articles(_UpperCamelCase		):
		return re.sub(r'\b(a|an|the)\b'	,  ' '	,  _UpperCamelCase		)

	def white_space_fix(_UpperCamelCase		):
		return " ".join(text.split()		)

	def remove_punc(_UpperCamelCase		):
		__lowerCAmelCase      :						Optional[Any]						      =				set(string.punctuation		)
		return "".join(ch for ch in text if ch not in exclude		)

	def lower(_UpperCamelCase		):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase		)		)		)		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Tuple						      =				normalize_answer(_UpperCamelCase		).split()
	__lowerCAmelCase      :						Tuple						      =				normalize_answer(_UpperCamelCase		).split()
	__lowerCAmelCase      :						Optional[int]						      =				Counter(_UpperCamelCase		) & Counter(_UpperCamelCase		)
	__lowerCAmelCase      :						List[Any]						      =				sum(common.values()		)
	if num_same == 0:
		return 0
	__lowerCAmelCase      :						Optional[Any]						      =				1.0 * num_same / len(_UpperCamelCase		)
	__lowerCAmelCase      :						Union[str, Any]						      =				1.0 * num_same / len(_UpperCamelCase		)
	__lowerCAmelCase      :						Any						      =				(2 * precision * recall) / (precision + recall)
	return fa
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	return normalize_answer(_UpperCamelCase		) == normalize_answer(_UpperCamelCase		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	assert len(_UpperCamelCase		) == len(_UpperCamelCase		)
	__lowerCAmelCase      :						str						      =				0
	for hypo, pred in zip(_UpperCamelCase	,  _UpperCamelCase		):
		em += exact_match_score(_UpperCamelCase	,  _UpperCamelCase		)
	if len(_UpperCamelCase		) > 0:
		em /= len(_UpperCamelCase		)
	return {"em": em}
def    __lowerCAmelCase    (_UpperCamelCase		):
	return model_prefix.startswith('rag'		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Tuple						      =				{p: p for p in extra_params}
	# T5 models don't have `dropout` param, they have `dropout_rate` instead
	__lowerCAmelCase      :						int						      =				'dropout_rate'
	for p in extra_params:
		if getattr(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
			if not hasattr(_UpperCamelCase	,  _UpperCamelCase		) and not hasattr(_UpperCamelCase	,  equivalent_param[p]		):
				logger.info('config doesn\'t have a `{}` attribute'.format(_UpperCamelCase		)		)
				delattr(_UpperCamelCase	,  _UpperCamelCase		)
				continue
			__lowerCAmelCase      :						List[str]						      =				p if hasattr(_UpperCamelCase	,  _UpperCamelCase		) else equivalent_param[p]
			setattr(_UpperCamelCase	,  _UpperCamelCase	,  getattr(_UpperCamelCase	,  _UpperCamelCase		)		)
			delattr(_UpperCamelCase	,  _UpperCamelCase		)
	return hparams, config

"""simple docstring"""







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 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			for model_result in results.values():
				for batch_size, sequence_length in zip(model_result['bs']	,		model_result['ss']			):
					__lowerCAmelCase      :						Optional[Any]						      =				model_result['result'][batch_size][sequence_length]
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						int						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		eager_mode=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Tuple						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				'sgugger/tiny-distilbert-classification'
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		only_pretrain_model=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Optional[int]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						int						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		eager_mode=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Dict						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__lowerCAmelCase      :						str						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						Tuple						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Any						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__lowerCAmelCase      :						Dict						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						str						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_train_result			)
			self.check_results_dict_not_empty(results.memory_train_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						Any						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						List[Any]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		[config]			)
			__lowerCAmelCase      :						List[Any]						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_train_result			)
			self.check_results_dict_not_empty(results.memory_train_result			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				'patrickvonplaten/t5-tiny-random'
			__lowerCAmelCase      :						int						      =				AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Dict						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE	,		configs=[config]			)
			__lowerCAmelCase      :						Any						      =				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 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				'sshleifer/tiny-gpt2'
			__lowerCAmelCase      :						List[Any]						      =				TensorFlowBenchmarkArguments(
			    models=[MODEL_ID]	,		training=_SCREAMING_SNAKE_CASE	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		use_xla=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Optional[int]						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				benchmark.run()
			self.check_results_dict_not_empty(results.time_inference_result			)
			self.check_results_dict_not_empty(results.memory_inference_result			)

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







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				'sshleifer/tiny-gpt2'

			def _check_summary_is_not_empty(_SCREAMING_SNAKE_CASE			):
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'sequential'			)			)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'cumulative'			)			)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'current'			)			)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE	,		'total'			)			)

			with tempfile.TemporaryDirectory() as tmp_dir:
				__lowerCAmelCase      :						Any						      =				TensorFlowBenchmarkArguments(
				    models=[MODEL_ID]	,		inference=_SCREAMING_SNAKE_CASE	,		sequence_lengths=[8]	,		batch_sizes=[1]	,		log_filename=os.path.join(_SCREAMING_SNAKE_CASE	,		'log.txt'			)	,		log_print=_SCREAMING_SNAKE_CASE	,		trace_memory_line_by_line=_SCREAMING_SNAKE_CASE	,		eager_mode=_SCREAMING_SNAKE_CASE	,		multi_process=_SCREAMING_SNAKE_CASE	,		)
				__lowerCAmelCase      :						Any						      =				TensorFlowBenchmark(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						int						      =				benchmark.run()
				_check_summary_is_not_empty(result.inference_summary			)
				self.assertTrue(Path(os.path.join(_SCREAMING_SNAKE_CASE	,		'log.txt'			)			).exists()			)