




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

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


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

__lowerCamelCase   :						List[Any]				=			'''=======
>>>>>>>
'''

__lowerCamelCase   :						str				=			[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

__lowerCamelCase   :						Optional[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 		lowercase__   (		__A:	Namespace				):





					'''simple docstring'''






					return ConvertCommand(args.tfds_path							,args.datasets_directory				)


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			@staticmethod
			def 							UpperCAmelCase__       ( lowerCamelCase_			:   ArgumentParser  )						->   Tuple:
								__magic_name__							:	Dict    							=							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			:   Union[str, Any]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   str       ,						*lowerCamelCase_			:   str  )						->   Any:
								__magic_name__							:	Optional[Any]    							=							get_logger('''datasets-cli/converting'''  )

								__magic_name__							:	List[Any]    							=							tfds_path
								__magic_name__							:	Any    							=							datasets_directory


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

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

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

								__magic_name__							:	Dict    							=							[]
								__magic_name__							:	Tuple    							=							[]
								__magic_name__							:	Any    							=							{}

								if os.path.isdir(self._tfds_path  ):
													__magic_name__							:	List[Any]    							=							os.listdir(lowerCamelCase_  )
								else:
													__magic_name__							:	Optional[int]    							=							[os.path.basename(self._tfds_path  )]

								for f_name in file_names:
													self._logger.info(F'''Looking at file {f_name}'''  )
													__magic_name__							:	int    							=							os.path.join(lowerCamelCase_       ,						lowerCamelCase_  )
													__magic_name__							:	List[Any]    							=							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:
																		__magic_name__							:	Optional[Any]    							=							f.readlines()

													__magic_name__							:	str    							=							[]
													__magic_name__							:	int    							=							False
													__magic_name__							:	Optional[int]    							=							False
													__magic_name__							:	str    							=							[]
													for line in lines:
																		__magic_name__							:	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:
																							__magic_name__							:	Optional[Any]    							=							'''import datasets\n'''
																		elif "import tensorflow" in out_line:
																							# order is important here
																							__magic_name__							:	Tuple    							=							''''''
																							continue
																		elif "from absl import logging" in out_line:
																							__magic_name__							:	Dict    							=							'''from datasets import logging\n'''
																		elif "getLogger" in out_line:
																							__magic_name__							:	Any    							=							out_line.replace('''getLogger'''       ,						'''get_logger'''  )
																		elif any(expression in out_line for expression in TO_HIGHLIGHT  ):
																							__magic_name__							:	int    							=							True
																							__magic_name__							:	Union[str, 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:
																												__magic_name__							:	Tuple    							=							re.sub(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )

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

													if is_builder or "wmt" in f_name:
																		# We create a new directory for each dataset
																		__magic_name__							:	Union[str, Any]    							=							f_name.replace('''.py'''       ,						''''''  )
																		__magic_name__							:	Union[str, Any]    							=							os.path.join(lowerCamelCase_       ,						lowerCamelCase_  )
																		__magic_name__							:	List[str]    							=							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:
																		__magic_name__							:	Optional[Any]    							=							os.path.basename(lowerCamelCase_  )
																		__magic_name__							:	List[str]    							=							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\'.'''  )









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]=1       ,						lowerCamelCase_			:   Any=0       ,						lowerCamelCase_			:   Dict=2       ,						lowerCamelCase_			:   Dict=512       ,						lowerCamelCase_			:   int="cls"       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   List[Any]=True       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							project_dim
								__magic_name__							:	int    							=							pooler_fn
								__magic_name__							:	str    							=							learn_encoder
								__magic_name__							:	Optional[int]    							=							use_attention_mask






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[R'''pooler''', R'''logit_scale''']
			UpperCamelCase__								=[R'''position_ids''', R'''predictions.decoder.bias''']
			UpperCamelCase__								='''roberta'''
			UpperCamelCase__								=RobertaSeriesConfig

			def __init__( self			:   str       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							XLMRobertaModel(lowerCamelCase_  )
								__magic_name__							:	Any    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
								__magic_name__							:	Optional[int]    							=							getattr(lowerCamelCase_       ,						'''has_pre_transformation'''       ,						lowerCamelCase_  )
								if self.has_pre_transformation:
													__magic_name__							:	List[Any]    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
													__magic_name__							:	Union[str, Any]    							=							nn.LayerNorm(config.hidden_size       ,						eps=config.layer_norm_eps  )
								self.post_init()


			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						)						->   List[Any]:
								__magic_name__							:	Optional[int]    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	Any    							=							self.base_model(
								    input_ids=lowerCamelCase_       ,						attention_mask=lowerCamelCase_       ,						token_type_ids=lowerCamelCase_       ,						position_ids=lowerCamelCase_       ,						head_mask=lowerCamelCase_       ,						inputs_embeds=lowerCamelCase_       ,						encoder_hidden_states=lowerCamelCase_       ,						encoder_attention_mask=lowerCamelCase_       ,						output_attentions=lowerCamelCase_       ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,						return_dict=lowerCamelCase_       ,						)

								if self.has_pre_transformation:
													__magic_name__							:	Any    							=							outputs['''hidden_states'''][-2]
													__magic_name__							:	Dict    							=							self.pre_LN(lowerCamelCase_  )
													__magic_name__							:	int    							=							self.transformation_pre(lowerCamelCase_  )

													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)
								else:
													__magic_name__							:	Union[str, Any]    							=							self.transformation(outputs.last_hidden_state  )
													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)



