

"""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


__UpperCamelCase    : Union[str, Any]				 =						'''<<<<<<< This should probably be modified because it mentions: '''

__UpperCamelCase    : Dict				 =						'''=======
>>>>>>>
'''

__UpperCamelCase    : Optional[int]				 =						[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

__UpperCamelCase    : Dict				 =						[
    # (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    ( A_					):
	return ConvertCommand(args.tfds_path		,	args.datasets_directory					)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					@staticmethod
					def 			__lowerCAmelCase					(			lowercase_     :							ArgumentParser						):
						lowerCAmelCase__      :		Union[str, Any]    							=      parser.add_parser(
						    '''convert'''  ,help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''  ,)
						train_parser.add_argument(
						    '''--tfds_path'''  ,type=lowercase_  ,required=lowercase_  ,help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''  ,)
						train_parser.add_argument(
						    '''--datasets_directory'''  ,type=lowercase_  ,required=lowercase_  ,help='''Path to the HuggingFace Datasets folder.'''						)
						train_parser.set_defaults(func=lowercase_						)







					def __init__(			self     :							Tuple  ,lowercase_     :							str  ,lowercase_     :							str  ,*lowercase_     :							Optional[Any]						):
						lowerCAmelCase__      :		List[str]    							=      get_logger('''datasets-cli/converting'''						)

						lowerCAmelCase__      :		Optional[int]    							=      tfds_path
						lowerCAmelCase__      :		Dict    							=      datasets_directory







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

						lowerCAmelCase__      :		List[str]    							=      os.path.abspath(self._datasets_directory						)

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

						lowerCAmelCase__      :		Any    							=      []
						lowerCAmelCase__      :		List[Any]    							=      []
						lowerCAmelCase__      :		Any    							=      {}

						if os.path.isdir(self._tfds_path						):
							lowerCAmelCase__      :		Tuple    							=      os.listdir(lowercase_						)
						else:
							lowerCAmelCase__      :		int    							=      [os.path.basename(self._tfds_path						)]

						for f_name in file_names:
							self._logger.info(F'Looking at file {f_name}'						)
							lowerCAmelCase__      :		List[str]    							=      os.path.join(lowercase_  ,lowercase_						)
							lowerCAmelCase__      :		int    							=      os.path.join(lowercase_  ,lowercase_						)

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

							with open(lowercase_  ,encoding='''utf-8'''						) as f:
								lowerCAmelCase__      :		List[str]    							=      f.readlines()

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

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

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

							if needs_manual_update:
								with_manual_update.append(lowercase_						)

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

						for utils_file in utils_files:
							try:
								lowerCAmelCase__      :		str    							=      os.path.basename(lowercase_						)
								lowerCAmelCase__      :		List[Any]    							=      imports_to_builder_map[f_name.replace('''.py'''  ,''''''						)]
								self._logger.info(F'Moving {dest_folder} to {utils_file}'						)
								shutil.copy(lowercase_  ,lowercase_						)
							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\'.'						)







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase    : List[str]				 =						{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Union[str, Any]				 =						['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Optional[int]				 =						[
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase    : Optional[int]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)




