


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

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


_lowercase							:   Tuple						=						"<<<<<<< This should probably be modified because it mentions: "

_lowercase							:   str						=						"=======\n>>>>>>>\n"

_lowercase							:   Any						=						[
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

_lowercase							:   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 	_lowerCAmelCase					( UpperCamelCase__:  Namespace    )			-> Optional[Any]:





	"""simple docstring"""







	return ConvertCommand(args.tfds_path				,       args.datasets_directory    )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				@staticmethod
				def   _UpperCAmelCase       (    a__ )						->					List[str]:
					A             =					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__ )						->					Dict:
					A             =					get_logger("""datasets-cli/converting""" )

					A             =					tfds_path
					A             =					datasets_directory

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

					A             =					os.path.abspath(self._datasets_directory )

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

					A             =					[]
					A             =					[]
					A             =					{}

					if os.path.isdir(self._tfds_path ):
						A             =					os.listdir(a__ )
					else:
						A             =					[os.path.basename(self._tfds_path )]

					for f_name in file_names:
						self._logger.info(f'Looking at file {f_name}' )
						A             =					os.path.join(a__					, a__ )
						A             =					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             =					f.readlines()

						A             =					[]
						A             =					False
						A             =					False
						A             =					[]
						for line in lines:
							A             =					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             =					"""import datasets\n"""
							elif "import tensorflow" in out_line:
								# order is important here
								A             =					""""""
								continue
							elif "from absl import logging" in out_line:
								A             =					"""from datasets import logging\n"""
							elif "getLogger" in out_line:
								A             =					out_line.replace("""getLogger"""					, """get_logger""" )
							elif any(expression in out_line for expression in TO_HIGHLIGHT ):
								A             =					True
								A             =					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             =					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             =					re.match(r"""from\stensorflow_datasets.*import\s([^\.\r\n]+)"""					, a__ )
								tfds_imports.extend(imp.strip() for imp in match.group(1 ).split(""",""" ) )
								A             =					"""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             =					True
							out_lines.append(a__ )

						if is_builder or "wmt" in f_name:
							# We create a new directory for each dataset
							A             =					f_name.replace(""".py"""					, """""" )
							A             =					os.path.join(a__					, a__ )
							A             =					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             =					os.path.basename(a__ )
							A             =					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\'.' )







import numpy as np

from transformers import Pipeline





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					np.max(UpperCamelCase__				,       axis=-1				,       keepdims=UpperCamelCase__    )
	A             =					np.exp(outputs - maxes    )
	return shifted_exp / shifted_exp.sum(axis=-1				,       keepdims=UpperCamelCase__    )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, **a__ )						->					Union[str, Any]:
					A             =					{}
					if "second_text" in kwargs:
						A             =					kwargs["""second_text"""]
					return preprocess_kwargs, {}, {}

				def   _UpperCAmelCase       (    self					, a__					, a__=None )						->					str:
					return self.tokenizer(a__					, text_pair=a__					, return_tensors=self.framework )

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					return self.model(**a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[int]:
					A             =					model_outputs.logits[0].numpy()
					A             =					softmax(a__ )

					A             =					np.argmax(a__ )
					A             =					self.model.config.idalabel[best_class]
					A             =					probabilities[best_class].item()
					A             =					logits.tolist()
					return {"label": label, "score": score, "logits": logits}



