




from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


UpperCamelCase__    			=			logging.get_logger(__name__)

# General docstring
UpperCamelCase__    			=			'''RegNetConfig'''

# Base docstring
UpperCamelCase__    			=			'''facebook/regnet-y-040'''
UpperCamelCase__    			=			[1, 1088, 7, 7]

# Image classification docstring
UpperCamelCase__    			=			'''facebook/regnet-y-040'''
UpperCamelCase__    			=			'''tabby, tabby cat'''

UpperCamelCase__    			=			[
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 			a__    (					nn.Module      ):






						def __init__(   self		,			_A		,			_A		,			_A = 3		,			_A = 1		,			_A = 1		,			_A = "relu"		,			):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase				   =       nn.Convad(
											    lowerCAmelCase__		,			lowerCAmelCase__		,			kernel_size=lowerCAmelCase__		,			stride=lowerCAmelCase__		,			padding=kernel_size // 2		,			groups=lowerCAmelCase__		,			bias=lowerCAmelCase__		,			)
											__lowerCAmelCase				   =       nn.BatchNormad(lowerCAmelCase__			)
											__lowerCAmelCase				   =       ACTaFN[activation] if activation is not None else nn.Identity()






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.convolution(lowerCAmelCase__			)
											__lowerCAmelCase				   =       self.normalization(lowerCAmelCase__			)
											__lowerCAmelCase				   =       self.activation(lowerCAmelCase__			)
											return hidden_state






class 			a__    (					nn.Module      ):






						def __init__(   self		,			_A			):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase				   =       RegNetConvLayer(
											    config.num_channels		,			config.embedding_size		,			kernel_size=3		,			stride=2		,			activation=config.hidden_act			)
											__lowerCAmelCase				   =       config.num_channels






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       pixel_values.shape[1]
											if num_channels != self.num_channels:
																raise ValueError(
																    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."			)
											__lowerCAmelCase				   =       self.embedder(lowerCAmelCase__			)
											return hidden_state






class 			a__    (					nn.Module      ):






						def __init__(   self		,			_A		,			_A		,			_A = 2			):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase				   =       nn.Convad(lowerCAmelCase__		,			lowerCAmelCase__		,			kernel_size=1		,			stride=lowerCAmelCase__		,			bias=lowerCAmelCase__			)
											__lowerCAmelCase				   =       nn.BatchNormad(lowerCAmelCase__			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.convolution(lowerCAmelCase__			)
											__lowerCAmelCase				   =       self.normalization(lowerCAmelCase__			)
											return hidden_state






class 			a__    (					nn.Module      ):






						def __init__(   self		,			_A		,			_A			):




											"""simple docstring"""



											super().__init__()

											__lowerCAmelCase				   =       nn.AdaptiveAvgPoolad((1, 1)			)
											__lowerCAmelCase				   =       nn.Sequential(
											    nn.Convad(lowerCAmelCase__		,			lowerCAmelCase__		,			kernel_size=1			)		,			nn.ReLU()		,			nn.Convad(lowerCAmelCase__		,			lowerCAmelCase__		,			kernel_size=1			)		,			nn.Sigmoid()		,			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.pooler(lowerCAmelCase__			)
											__lowerCAmelCase				   =       self.attention(lowerCAmelCase__			)
											__lowerCAmelCase				   =       hidden_state * attention
											return hidden_state






class 			a__    (					nn.Module      ):






						def __init__(   self		,			_A		,			_A		,			_A		,			_A = 1			):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase				   =       in_channels != out_channels or stride != 1
											__lowerCAmelCase				   =       max(1		,			out_channels // config.groups_width			)
											__lowerCAmelCase				   =       (
											    RegNetShortCut(lowerCAmelCase__		,			lowerCAmelCase__		,			stride=lowerCAmelCase__			) if should_apply_shortcut else nn.Identity()
											)
											__lowerCAmelCase				   =       nn.Sequential(
											    RegNetConvLayer(lowerCAmelCase__		,			lowerCAmelCase__		,			kernel_size=1		,			activation=config.hidden_act			)		,			RegNetConvLayer(lowerCAmelCase__		,			lowerCAmelCase__		,			stride=lowerCAmelCase__		,			groups=lowerCAmelCase__		,			activation=config.hidden_act			)		,			RegNetConvLayer(lowerCAmelCase__		,			lowerCAmelCase__		,			kernel_size=1		,			activation=lowerCAmelCase__			)		,			)
											__lowerCAmelCase				   =       ACTaFN[config.hidden_act]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       hidden_state
											__lowerCAmelCase				   =       self.layer(lowerCAmelCase__			)
											__lowerCAmelCase				   =       self.shortcut(lowerCAmelCase__			)
											hidden_state += residual
											__lowerCAmelCase				   =       self.activation(lowerCAmelCase__			)
											return hidden_state






class 			a__    (					nn.Module      ):






						def __init__(   self		,			_A		,			_A		,			_A		,			_A = 1			):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase				   =       in_channels != out_channels or stride != 1
											__lowerCAmelCase				   =       max(1		,			out_channels // config.groups_width			)
											__lowerCAmelCase				   =       (
											    RegNetShortCut(lowerCAmelCase__		,			lowerCAmelCase__		,			stride=lowerCAmelCase__			) if should_apply_shortcut else nn.Identity()
											)
											__lowerCAmelCase				   =       nn.Sequential(
											    RegNetConvLayer(lowerCAmelCase__		,			lowerCAmelCase__		,			kernel_size=1		,			activation=config.hidden_act			)		,			RegNetConvLayer(lowerCAmelCase__		,			lowerCAmelCase__		,			stride=lowerCAmelCase__		,			groups=lowerCAmelCase__		,			activation=config.hidden_act			)		,			RegNetSELayer(lowerCAmelCase__		,			reduced_channels=int(round(in_channels / 4			)			)			)		,			RegNetConvLayer(lowerCAmelCase__		,			lowerCAmelCase__		,			kernel_size=1		,			activation=lowerCAmelCase__			)		,			)
											__lowerCAmelCase				   =       ACTaFN[config.hidden_act]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       hidden_state
											__lowerCAmelCase				   =       self.layer(lowerCAmelCase__			)
											__lowerCAmelCase				   =       self.shortcut(lowerCAmelCase__			)
											hidden_state += residual
											__lowerCAmelCase				   =       self.activation(lowerCAmelCase__			)
											return hidden_state






class 			a__    (					nn.Module      ):






						def __init__(   self		,			_A		,			_A		,			_A		,			_A = 2		,			_A = 2		,			):




											"""simple docstring"""



											super().__init__()

											__lowerCAmelCase				   =       RegNetXLayer if config.layer_type == "x" else RegNetYLayer

											__lowerCAmelCase				   =       nn.Sequential(
											    # downsampling is done in the first layer with stride of 2
											    layer(
											        lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__		,			stride=lowerCAmelCase__		,			)		,			*[layer(lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__			) for _ in range(depth - 1			)]		,			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.layers(lowerCAmelCase__			)
											return hidden_state






class 			a__    (					nn.Module      ):






						def __init__(   self		,			_A			):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase				   =       nn.ModuleList([]			)
											# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
											self.stages.append(
											    RegNetStage(
											        lowerCAmelCase__		,			config.embedding_size		,			config.hidden_sizes[0]		,			stride=2 if config.downsample_in_first_stage else 1		,			depth=config.depths[0]		,			)			)
											__lowerCAmelCase				   =       zip(config.hidden_sizes		,			config.hidden_sizes[1:]			)
											for (in_channels, out_channels), depth in zip(lowerCAmelCase__		,			config.depths[1:]			):
																self.stages.append(RegNetStage(lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__		,			depth=lowerCAmelCase__			)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = False		,			_A = True			):




											"""simple docstring"""



											__lowerCAmelCase				   =       () if output_hidden_states else None

											for stage_module in self.stages:
																if output_hidden_states:
																					__lowerCAmelCase				   =       hidden_states + (hidden_state,)

																__lowerCAmelCase				   =       stage_module(lowerCAmelCase__			)

											if output_hidden_states:
																__lowerCAmelCase				   =       hidden_states + (hidden_state,)

											if not return_dict:
																return tuple(v for v in [hidden_state, hidden_states] if v is not None			)

											return BaseModelOutputWithNoAttention(last_hidden_state=lowerCAmelCase__		,			hidden_states=lowerCAmelCase__			)






class 			a__    (					a__      ):
						_a							:    Union[str, Any]					=    RegNetConfig
						_a							:    Dict					=    """regnet"""
						_a							:    Tuple					=    """pixel_values"""
						_a							:    List[Any]					=    True






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											if isinstance(lowerCAmelCase__		,			nn.Convad			):
																nn.init.kaiming_normal_(module.weight		,			mode="fan_out"		,			nonlinearity="relu"			)
											elif isinstance(lowerCAmelCase__		,			(nn.BatchNormad, nn.GroupNorm)			):
																nn.init.constant_(module.weight		,			1			)
																nn.init.constant_(module.bias		,			0			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=False			):




											"""simple docstring"""



											if isinstance(lowerCAmelCase__		,			lowerCAmelCase__			):
																__lowerCAmelCase				   =       value






UpperCamelCase__    			=			R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

UpperCamelCase__    			=			R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top.""" ,				a__ ,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 			a__    (					a__      ):






						def __init__(   self		,			_A			):




											"""simple docstring"""



											super().__init__(lowerCAmelCase__			)
											__lowerCAmelCase				   =       config
											__lowerCAmelCase				   =       RegNetEmbeddings(lowerCAmelCase__			)
											__lowerCAmelCase				   =       RegNetEncoder(lowerCAmelCase__			)
											__lowerCAmelCase				   =       nn.AdaptiveAvgPoolad((1, 1)			)
											# Initialize weights and apply final processing
											self.post_init()






						@add_start_docstrings_to_model_forward(lowerCAmelCase__			)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC		,			output_type=lowerCAmelCase__		,			config_class=_CONFIG_FOR_DOC		,			modality="vision"		,			expected_output=_EXPECTED_OUTPUT_SHAPE		,			)
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None		,			_A = None			):




											"""simple docstring"""



											__lowerCAmelCase				   =       (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__lowerCAmelCase				   =       return_dict if return_dict is not None else self.config.use_return_dict

											__lowerCAmelCase				   =       self.embedder(lowerCAmelCase__			)

											__lowerCAmelCase				   =       self.encoder(
											    lowerCAmelCase__		,			output_hidden_states=lowerCAmelCase__		,			return_dict=lowerCAmelCase__			)

											__lowerCAmelCase				   =       encoder_outputs[0]

											__lowerCAmelCase				   =       self.pooler(lowerCAmelCase__			)

											if not return_dict:
																return (last_hidden_state, pooled_output) + encoder_outputs[1:]

											return BaseModelOutputWithPoolingAndNoAttention(
											    last_hidden_state=lowerCAmelCase__		,			pooler_output=lowerCAmelCase__		,			hidden_states=encoder_outputs.hidden_states		,			)






@add_start_docstrings(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """ ,				a__ ,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 			a__    (					a__      ):






						def __init__(   self		,			_A			):




											"""simple docstring"""



											super().__init__(lowerCAmelCase__			)
											__lowerCAmelCase				   =       config.num_labels
											__lowerCAmelCase				   =       RegNetModel(lowerCAmelCase__			)
											# classification head
											__lowerCAmelCase				   =       nn.Sequential(
											    nn.Flatten()		,			nn.Linear(config.hidden_sizes[-1]		,			config.num_labels			) if config.num_labels > 0 else nn.Identity()		,			)
											# initialize weights and apply final processing
											self.post_init()






						@add_start_docstrings_to_model_forward(lowerCAmelCase__			)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT		,			output_type=lowerCAmelCase__		,			config_class=_CONFIG_FOR_DOC		,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,			)
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       return_dict if return_dict is not None else self.config.use_return_dict

											__lowerCAmelCase				   =       self.regnet(lowerCAmelCase__		,			output_hidden_states=lowerCAmelCase__		,			return_dict=lowerCAmelCase__			)

											__lowerCAmelCase				   =       outputs.pooler_output if return_dict else outputs[1]

											__lowerCAmelCase				   =       self.classifier(lowerCAmelCase__			)

											__lowerCAmelCase				   =       None

											if labels is not None:
																if self.config.problem_type is None:
																					if self.num_labels == 1:
																										__lowerCAmelCase				   =       "regression"
																					elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																										__lowerCAmelCase				   =       "single_label_classification"
																					else:
																										__lowerCAmelCase				   =       "multi_label_classification"
																if self.config.problem_type == "regression":
																					__lowerCAmelCase				   =       MSELoss()
																					if self.num_labels == 1:
																										__lowerCAmelCase				   =       loss_fct(logits.squeeze()		,			labels.squeeze()			)
																					else:
																										__lowerCAmelCase				   =       loss_fct(lowerCAmelCase__		,			lowerCAmelCase__			)
																elif self.config.problem_type == "single_label_classification":
																					__lowerCAmelCase				   =       CrossEntropyLoss()
																					__lowerCAmelCase				   =       loss_fct(logits.view(-1		,			self.num_labels			)		,			labels.view(-1			)			)
																elif self.config.problem_type == "multi_label_classification":
																					__lowerCAmelCase				   =       BCEWithLogitsLoss()
																					__lowerCAmelCase				   =       loss_fct(lowerCAmelCase__		,			lowerCAmelCase__			)

											if not return_dict:
																__lowerCAmelCase				   =       (logits,) + outputs[2:]
																return (loss,) + output if loss is not None else output

											return ImageClassifierOutputWithNoAttention(loss=lowerCAmelCase__		,			logits=lowerCAmelCase__		,			hidden_states=outputs.hidden_states			)











"""simple docstring"""




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

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


a						: int     	=			'''<<<<<<< This should probably be modified because it mentions: '''

a						: Tuple     	=			'''=======
>>>>>>>
'''

a						: List[Any]     	=			[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

a						: Union[str, 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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Namespace					) ->Any:






	'''simple docstring'''






	return ConvertCommand(args.tfds_path    ,       args.datasets_directory					)







class        __UpperCamelCase				(						a__						):






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






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					*lowerCAmelCase__      )	-> Tuple:
								a      :		List[str]	=				get_logger("datasets-cli/converting"      )

								a      :		Optional[int]	=				tfds_path
								a      :		str	=				datasets_directory






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

								a      :		str	=				os.path.abspath(self._datasets_directory      )

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

								a      :		str	=				[]
								a      :		Union[str, Any]	=				[]
								a      :		str	=				{}

								if os.path.isdir(self._tfds_path      ):
									a      :		Dict	=				os.listdir(lowerCAmelCase__      )
								else:
									a      :		Dict	=				[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(lowerCAmelCase__      ,					lowerCAmelCase__      )
									a      :		Dict	=				os.path.join(lowerCAmelCase__      ,					lowerCAmelCase__      )

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

									with open(lowerCAmelCase__      ,					encoding="utf-8"      ) as f:
										a      :		Any	=				f.readlines()

									a      :		Tuple	=				[]
									a      :		Any	=				False
									a      :		Dict	=				False
									a      :		Dict	=				[]
									for line in lines:
										a      :		Tuple	=				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      :		Optional[int]	=				"import datasets\n"
										elif "import tensorflow" in out_line:
											# order is important here
											a      :		Optional[Any]	=				""
											continue
										elif "from absl import logging" in out_line:
											a      :		Any	=				"from datasets import logging\n"
										elif "getLogger" in out_line:
											a      :		int	=				out_line.replace("getLogger"      ,					"get_logger"      )
										elif any(expression in out_line for expression in TO_HIGHLIGHT      ):
											a      :		Optional[Any]	=				True
											a      :		Optional[Any]	=				list(filter(lambda lowerCAmelCase__      : e in out_line      ,					lowerCAmelCase__      )      )
											out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(lowerCAmelCase__      ) + "\n"      )
											out_lines.append(lowerCAmelCase__      )
											out_lines.append(lowerCAmelCase__      )
											continue
										else:
											for pattern, replacement in TO_CONVERT:
												a      :		int	=				re.sub(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )

                # Take care of saving utilities (to later move them together with main script)
										if "tensorflow_datasets" in out_line:
											a      :		int	=				re.match(R"from\stensorflow_datasets.*import\s([^\.\r\n]+)"      ,					lowerCAmelCase__      )
											tfds_imports.extend(imp.strip() for imp in match.group(1      ).split(","      )      )
											a      :		List[str]	=				"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      :		str	=				True
										out_lines.append(lowerCAmelCase__      )

									if is_builder or "wmt" in f_name:
										# We create a new directory for each dataset
										a      :		Tuple	=				f_name.replace(".py"      ,					""      )
										a      :		Optional[Any]	=				os.path.join(lowerCAmelCase__      ,					lowerCAmelCase__      )
										a      :		Dict	=				os.path.join(lowerCAmelCase__      ,					lowerCAmelCase__      )
										os.makedirs(lowerCAmelCase__      ,					exist_ok=lowerCAmelCase__      )
										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(lowerCAmelCase__      )

									if needs_manual_update:
										with_manual_update.append(lowerCAmelCase__      )

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

								for utils_file in utils_files:
									try:
										a      :		int	=				os.path.basename(lowerCAmelCase__      )
										a      :		int	=				imports_to_builder_map[f_name.replace(".py"      ,					""      )]
										self._logger.info(f"""Moving {dest_folder} to {utils_file}"""      )
										shutil.copy(lowerCAmelCase__      ,					lowerCAmelCase__      )
									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'."""      )


