

import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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 (
	    FlaubertForMultipleChoice,
	    FlaubertForQuestionAnswering,
	    FlaubertForQuestionAnsweringSimple,
	    FlaubertForSequenceClassification,
	    FlaubertForTokenClassification,
	    FlaubertModel,
	    FlaubertWithLMHeadModel,
	)
	from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class    _a						(__magic_name__    ):


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__=13   ,	A__=7   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=True   ,	A__=False   ,	A__=False   ,	A__=False   ,	A__=2   ,	A__=99   ,	A__=0   ,	A__=32   ,	A__=5   ,	A__=4   ,	A__=0.1   ,	A__=0.1   ,	A__=512   ,	A__=12   ,	A__=2   ,	A__=0.0_2   ,	A__=3   ,	A__=4   ,	A__="last"   ,	A__=None   ,	A__=None   ,	):
						A__							:   List[str]					       =		parent
						A__							:   int					       =		batch_size
						A__							:   Optional[int]					       =		seq_length
						A__							:   int					       =		is_training
						A__							:   Union[str, Any]					       =		use_input_lengths
						A__							:   Dict					       =		use_token_type_ids
						A__							:   Dict					       =		use_labels
						A__							:   Any					       =		gelu_activation
						A__							:   Union[str, Any]					       =		sinusoidal_embeddings
						A__							:   Dict					       =		causal
						A__							:   str					       =		asm
						A__							:   List[Any]					       =		n_langs
						A__							:   Any					       =		vocab_size
						A__							:   Dict					       =		n_special
						A__							:   Union[str, Any]					       =		hidden_size
						A__							:   Dict					       =		num_hidden_layers
						A__							:   str					       =		num_attention_heads
						A__							:   Union[str, Any]					       =		hidden_dropout_prob
						A__							:   List[Any]					       =		attention_probs_dropout_prob
						A__							:   Optional[Any]					       =		max_position_embeddings
						A__							:   Union[str, Any]					       =		type_vocab_size
						A__							:   Optional[int]					       =		type_sequence_label_size
						A__							:   Union[str, Any]					       =		initializer_range
						A__							:   Union[str, Any]					       =		num_labels
						A__							:   Tuple					       =		num_choices
						A__							:   Optional[Any]					       =		summary_type
						A__							:   List[Any]					       =		use_proj
						A__							:   Any					       =		scope






					def   __A      (			self      ):
						A__							:   str					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )
						A__							:   Dict					       =		random_attention_mask([self.batch_size, self.seq_length]      )

						A__							:   Optional[Any]					       =		None
						if self.use_input_lengths:
							A__							:   Union[str, Any]					       =		(
							    ids_tensor([self.batch_size]   ,	vocab_size=2      ) + self.seq_length - 2
							)  # small variation of seq_length

						A__							:   int					       =		None
						if self.use_token_type_ids:
							A__							:   List[str]					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.n_langs      )

						A__							:   Dict					       =		None
						A__							:   Dict					       =		None
						A__							:   Dict					       =		None
						if self.use_labels:
							A__							:   List[str]					       =		ids_tensor([self.batch_size]   ,	self.type_sequence_label_size      )
							A__							:   Tuple					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.num_labels      )
							A__							:   Union[str, Any]					       =		ids_tensor([self.batch_size]   ,	2      ).float()
							A__							:   List[Any]					       =		ids_tensor([self.batch_size]   ,	self.num_choices      )

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

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_lengths,
						    sequence_labels,
						    token_labels,
						    is_impossible_labels,
						    choice_labels,
						    input_mask,
						)






					def   __A      (			self      ):
						return FlaubertConfig(
						    vocab_size=self.vocab_size   ,	n_special=self.n_special   ,	emb_dim=self.hidden_size   ,	n_layers=self.num_hidden_layers   ,	n_heads=self.num_attention_heads   ,	dropout=self.hidden_dropout_prob   ,	attention_dropout=self.attention_probs_dropout_prob   ,	gelu_activation=self.gelu_activation   ,	sinusoidal_embeddings=self.sinusoidal_embeddings   ,	asm=self.asm   ,	causal=self.causal   ,	n_langs=self.n_langs   ,	max_position_embeddings=self.max_position_embeddings   ,	initializer_range=self.initializer_range   ,	summary_type=self.summary_type   ,	use_proj=self.use_proj   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   List[Any]					       =		FlaubertModel(config=A__      )
						model.to(A__      )
						model.eval()
						A__							:   Tuple					       =		model(A__   ,	lengths=A__   ,	langs=A__      )
						A__							:   str					       =		model(A__   ,	langs=A__      )
						A__							:   Tuple					       =		model(A__      )
						self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Any					       =		FlaubertWithLMHeadModel(A__      )
						model.to(A__      )
						model.eval()

						A__							:   Union[str, Any]					       =		model(A__   ,	token_type_ids=A__   ,	labels=A__      )
						self.parent.assertEqual(result.loss.shape   ,	()      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Union[str, Any]					       =		FlaubertForQuestionAnsweringSimple(A__      )
						model.to(A__      )
						model.eval()

						A__							:   int					       =		model(A__      )

						A__							:   List[Any]					       =		model(A__   ,	start_positions=A__   ,	end_positions=A__      )
						self.parent.assertEqual(result.start_logits.shape   ,	(self.batch_size, self.seq_length)      )
						self.parent.assertEqual(result.end_logits.shape   ,	(self.batch_size, self.seq_length)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   List[str]					       =		FlaubertForQuestionAnswering(A__      )
						model.to(A__      )
						model.eval()

						A__							:   str					       =		model(A__      )

						A__							:   int					       =		model(
						    A__   ,	start_positions=A__   ,	end_positions=A__   ,	cls_index=A__   ,	is_impossible=A__   ,	p_mask=A__   ,	)

						A__							:   Optional[int]					       =		model(
						    A__   ,	start_positions=A__   ,	end_positions=A__   ,	cls_index=A__   ,	is_impossible=A__   ,	)

						((A__)       ,       )							:   int					       =		result_with_labels.to_tuple()

						A__							:   Union[str, Any]					       =		model(A__   ,	start_positions=A__   ,	end_positions=A__      )

						((A__)       ,       )							:   List[str]					       =		result_with_labels.to_tuple()

						self.parent.assertEqual(result_with_labels.loss.shape   ,	()      )
						self.parent.assertEqual(result.start_top_log_probs.shape   ,	(self.batch_size, model.config.start_n_top)      )
						self.parent.assertEqual(result.start_top_index.shape   ,	(self.batch_size, model.config.start_n_top)      )
						self.parent.assertEqual(
						    result.end_top_log_probs.shape   ,	(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
						self.parent.assertEqual(
						    result.end_top_index.shape   ,	(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
						self.parent.assertEqual(result.cls_logits.shape   ,	(self.batch_size,)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Any					       =		FlaubertForSequenceClassification(A__      )
						model.to(A__      )
						model.eval()

						A__							:   Optional[int]					       =		model(A__      )
						A__							:   Tuple					       =		model(A__   ,	labels=A__      )

						self.parent.assertEqual(result.loss.shape   ,	()      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.type_sequence_label_size)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   str					       =		self.num_labels
						A__							:   Any					       =		FlaubertForTokenClassification(A__      )
						model.to(A__      )
						model.eval()

						A__							:   Dict					       =		model(A__   ,	attention_mask=A__   ,	labels=A__      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.num_labels)      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Optional[int]					       =		self.num_choices
						A__							:   List[Any]					       =		FlaubertForMultipleChoice(config=A__      )
						model.to(A__      )
						model.eval()
						A__							:   Optional[int]					       =		input_ids.unsqueeze(1      ).expand(-1   ,	self.num_choices   ,	-1      ).contiguous()
						A__							:   List[Any]					       =		token_type_ids.unsqueeze(1      ).expand(-1   ,	self.num_choices   ,	-1      ).contiguous()
						A__							:   Union[str, Any]					       =		input_mask.unsqueeze(1      ).expand(-1   ,	self.num_choices   ,	-1      ).contiguous()
						A__							:   str					       =		model(
						    A__   ,	attention_mask=A__   ,	token_type_ids=A__   ,	labels=A__   ,	)
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_choices)      )






					def   __A      (			self      ):
						A__							:   Union[str, Any]					       =		self.prepare_config_and_inputs()
						(
						    (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       (
						    A__
						)       ,       
						)							:   Any					       =		config_and_inputs
						A__							:   Optional[Any]					       =		{
						    """input_ids""": input_ids,
						    """token_type_ids""": token_type_ids,
						    """lengths""": input_lengths,
						    """attention_mask""": input_mask,
						}
						return config, inputs_dict



@require_torch
class    _a						(__magic_name__		,       __magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	(
					    (
					        FlaubertModel,
					        FlaubertWithLMHeadModel,
					        FlaubertForQuestionAnswering,
					        FlaubertForQuestionAnsweringSimple,
					        FlaubertForSequenceClassification,
					        FlaubertForTokenClassification,
					        FlaubertForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCAmelCase__:       Optional[int]  							=	(
					    {
					        '''feature-extraction''': FlaubertModel,
					        '''fill-mask''': FlaubertWithLMHeadModel,
					        '''question-answering''': FlaubertForQuestionAnsweringSimple,
					        '''text-classification''': FlaubertForSequenceClassification,
					        '''token-classification''': FlaubertForTokenClassification,
					        '''zero-shot''': FlaubertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__      ):
						if (
						    pipeline_test_casse_name == "QAPipelineTests"
						    and tokenizer_name is not None
						    and not tokenizer_name.endswith("""Fast"""      )
						):
							# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
							# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
							# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
							return True

						return False






					def   __A      (			self   ,	A__   ,	A__   ,	A__=False      ):
						A__							:   Optional[int]					       =		super()._prepare_for_class(A__   ,	A__   ,	return_labels=A__      )

						if return_labels:
							if model_class.__name__ == "FlaubertForQuestionAnswering":
								A__							:   Tuple					       =		torch.zeros(
								    self.model_tester.batch_size   ,	dtype=torch.long   ,	device=A__      )
								A__							:   Optional[Any]					       =		torch.zeros(
								    self.model_tester.batch_size   ,	dtype=torch.long   ,	device=A__      )

						return inputs_dict






					def   __A      (			self      ):
						A__							:   int					       =		FlaubertModelTester(self      )
						A__							:   Union[str, Any]					       =		ConfigTester(self   ,	config_class=A__   ,	emb_dim=37      )






					def   __A      (			self      ):
						self.config_tester.run_common_tests()






					def   __A      (			self      ):
						A__							:   List[Any]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_model(*A__      )






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_lm_head(*A__      )






					def   __A      (			self      ):
						A__							:   int					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_simple_qa(*A__      )






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_qa(*A__      )






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_sequence_classif(*A__      )






					def   __A      (			self      ):
						A__							:   List[Any]					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_token_classif(*A__      )






					def   __A      (			self      ):
						A__							:   int					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_multiple_choice(*A__      )






					@slow
					def   __A      (			self      ):
						for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:   Dict					       =		FlaubertModel.from_pretrained(A__      )
							self.assertIsNotNone(A__      )






					@slow
					@require_torch_gpu
					def   __A      (			self      ):
						A__       ,       A__							:   Dict					       =		self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
							# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
							if model_class == FlaubertForMultipleChoice:
								return

							A__							:   Optional[Any]					       =		True
							A__							:   List[str]					       =		model_class(config=A__      )

							A__							:   List[Any]					       =		self._prepare_for_class(A__   ,	A__      )
							A__							:   int					       =		torch.jit.trace(
							    A__   ,	(inputs_dict["""input_ids"""].to("""cpu"""      ), inputs_dict["""attention_mask"""].to("""cpu"""      ))      )

							with tempfile.TemporaryDirectory() as tmp:
								torch.jit.save(A__   ,	os.path.join(A__   ,	"""traced_model.pt"""      )      )
								A__							:   Optional[int]					       =		torch.jit.load(os.path.join(A__   ,	"""traced_model.pt"""      )   ,	map_location=A__      )
								loaded(inputs_dict["""input_ids"""].to(A__      )   ,	inputs_dict["""attention_mask"""].to(A__      )      )



@require_torch
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   List[Any]					       =		FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""      )
						A__							:   List[Any]					       =		torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
						with torch.no_grad():
							A__							:   Optional[int]					       =		model(A__      )[0]
						A__							:   Any					       =		torch.Size((1, 11, 768)      )
						self.assertEqual(output.shape   ,	A__      )
						A__							:   int					       =		torch.tensor(
						    [[[-2.6_2_5_1, -1.4_2_9_8, -0.0_2_2_7], [-2.8_5_1_0, -1.6_3_8_7, 0.2_2_5_8], [-2.8_1_1_4, -1.1_8_3_2, -0.3_0_6_6]]]      )

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






import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


A_	:			Optional[int]  	=      '<<<<<<< This should probably be modified because it mentions: '

A_	:			int  	=      '=======\n>>>>>>>\n'

A_	:			Optional[Any]  	=      [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

A_	:			List[Any]  	=      [
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'tfds\.core', r'datasets'),
    (r'tf\.io\.gfile\.GFile', r'open'),
    (r'tf\.([\w\d]+)', r'datasets.Value(\'\1\')'),
    (r'tfds\.features\.Text\(\)', r'datasets.Value(\'string\')'),
    (r'tfds\.features\.Text\(', r'datasets.Value(\'string\'),'),
    (r'features\s*=\s*tfds.features.FeaturesDict\(', r'features=datasets.Features('),
    (r'tfds\.features\.FeaturesDict\(', r'dict('),
    (r'The TensorFlow Datasets Authors', r'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (r'tfds\.', r'datasets.'),
    (r'dl_manager\.manual_dir', r'self.config.data_dir'),
    (r'self\.builder_config', r'self.config'),
]

def 						UpperCamelCase (lowercase_:			Namespace )					->				str:
	return ConvertCommand(args.tfds_path   ,				args.datasets_directory )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		parser.add_parser(
						    """convert"""   ,	help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""   ,	)
						train_parser.add_argument(
						    """--tfds_path"""   ,	type=A__   ,	required=A__   ,	help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""   ,	)
						train_parser.add_argument(
						    """--datasets_directory"""   ,	type=A__   ,	required=A__   ,	help="""Path to the HuggingFace Datasets folder."""      )
						train_parser.set_defaults(func=A__      )






					def __init__(			self   ,	A__   ,	A__   ,	*A__      ):
						A__							:   Optional[Any]					       =		get_logger("""datasets-cli/converting"""      )

						A__							:   Optional[Any]					       =		tfds_path
						A__							:   Dict					       =		datasets_directory






					def   __A      (			self      ):
						if os.path.isdir(self._tfds_path      ):
							A__							:   Any					       =		os.path.abspath(self._tfds_path      )
						elif os.path.isfile(self._tfds_path      ):
							A__							:   int					       =		os.path.dirname(self._tfds_path      )
						else:
							raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""      )

						A__							:   Optional[int]					       =		os.path.abspath(self._datasets_directory      )

						self._logger.info(F"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""      )

						A__							:   Optional[Any]					       =		[]
						A__							:   List[Any]					       =		[]
						A__							:   List[Any]					       =		{}

						if os.path.isdir(self._tfds_path      ):
							A__							:   List[str]					       =		os.listdir(A__      )
						else:
							A__							:   Optional[Any]					       =		[os.path.basename(self._tfds_path      )]

						for f_name in file_names:
							self._logger.info(F"""Looking at file {f_name}"""      )
							A__							:   Any					       =		os.path.join(A__   ,	A__      )
							A__							:   Optional[Any]					       =		os.path.join(A__   ,	A__      )

							if not os.path.isfile(A__      ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
								self._logger.info("""Skipping file"""      )
								continue

							with open(A__   ,	encoding="""utf-8"""      ) as f:
								A__							:   Dict					       =		f.readlines()

							A__							:   Tuple					       =		[]
							A__							:   str					       =		False
							A__							:   Tuple					       =		False
							A__							:   Optional[Any]					       =		[]
							for line in lines:
								A__							:   Optional[int]					       =		line

								# Convert imports
								if "import tensorflow.compat.v2 as tf" in out_line:
									continue
								elif "@tfds.core" in out_line:
									continue
								elif "builder=self" in out_line:
									continue
								elif "import tensorflow_datasets.public_api as tfds" in out_line:
									A__							:   Dict					       =		"""import datasets\n"""
								elif "import tensorflow" in out_line:
									# order is important here
									A__							:   Any					       =		""""""
									continue
								elif "from absl import logging" in out_line:
									A__							:   int					       =		"""from datasets import logging\n"""
								elif "getLogger" in out_line:
									A__							:   List[str]					       =		out_line.replace("""getLogger"""   ,	"""get_logger"""      )
								elif any(expression in out_line for expression in TO_HIGHLIGHT      ):
									A__							:   Optional[Any]					       =		True
									A__							:   int					       =		list(filter(lambda A__      : e in out_line   ,	A__      )      )
									out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(A__      ) + """\n"""      )
									out_lines.append(A__      )
									out_lines.append(A__      )
									continue
								else:
									for pattern, replacement in TO_CONVERT:
										A__							:   List[str]					       =		re.sub(A__   ,	A__   ,	A__      )

                # Take care of saving utilities (to later move them together with main script)
								if "tensorflow_datasets" in out_line:
									A__							:   Dict					       =		re.match(r"""from\stensorflow_datasets.*import\s([^\.\r\n]+)"""   ,	A__      )
									tfds_imports.extend(imp.strip() for imp in match.group(1      ).split(""","""      )      )
									A__							:   Tuple					       =		"""from . import """ + match.group(1      )

								# Check we have not forget anything
								if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
									raise ValueError(F"""Error converting {out_line.strip()}"""      )

								if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
									A__							:   List[str]					       =		True
								out_lines.append(A__      )

							if is_builder or "wmt" in f_name:
								# We create a new directory for each dataset
								A__							:   Tuple					       =		f_name.replace(""".py"""   ,	""""""      )
								A__							:   Optional[int]					       =		os.path.join(A__   ,	A__      )
								A__							:   Optional[Any]					       =		os.path.join(A__   ,	A__      )
								os.makedirs(A__   ,	exist_ok=A__      )
								self._logger.info(F"""Adding directory {output_dir}"""      )
								imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}      )
							else:
								# Utilities will be moved at the end
								utils_files.append(A__      )

							if needs_manual_update:
								with_manual_update.append(A__      )

							with open(A__   ,	"""w"""   ,	encoding="""utf-8"""      ) as f:
								f.writelines(A__      )
							self._logger.info(F"""Converted in {output_file}"""      )

						for utils_file in utils_files:
							try:
								A__							:   int					       =		os.path.basename(A__      )
								A__							:   List[str]					       =		imports_to_builder_map[f_name.replace(""".py"""   ,	""""""      )]
								self._logger.info(F"""Moving {dest_folder} to {utils_file}"""      )
								shutil.copy(A__   ,	A__      )
							except KeyError:
								self._logger.error(F"""Cannot find destination folder for {utils_file}. Please copy manually."""      )

						if with_manual_update:
							for file_path in with_manual_update:
								self._logger.warning(
								    F"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."""      )



