


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


lowerCAmelCase	=							logging.get_logger(__name__)


class 	_a							(      lowerCamelCase__      ):
							_lowercase     :      Union[str, Any]  				=			['pixel_values']

							def __init__(							self:     Union[str, Any]   ,						UpperCamelCase_:     Optional[Any] = True   ,						UpperCamelCase_:     Union[str, Any] = None   ,						UpperCamelCase_:     List[str] = PILImageResampling.BICUBIC   ,						UpperCamelCase_:     Optional[Any] = True   ,						UpperCamelCase_:     Optional[Any] = 1 / 255   ,						UpperCamelCase_:     Optional[Any] = True   ,						UpperCamelCase_:     str = None   ,						UpperCamelCase_:     Dict = None   ,						UpperCamelCase_:     Optional[Any] = True   ,						**UpperCamelCase_:     List[Any]   ,						)      ->       Tuple:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)
									lowercase__						=				size if size is not None else {'''height''': 384, '''width''': 384}
									lowercase__						=				get_size_dict(UpperCamelCase_   ,						default_to_square=UpperCamelCase_							)

									lowercase__						=				do_resize
									lowercase__						=				size
									lowercase__						=				resample
									lowercase__						=				do_rescale
									lowercase__						=				rescale_factor
									lowercase__						=				do_normalize
									lowercase__						=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									lowercase__						=				image_std if image_std is not None else OPENAI_CLIP_STD
									lowercase__						=				do_convert_rgb

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     List[Any]   ,						UpperCamelCase_:     List[Any]   ,						UpperCamelCase_:     Optional[Any] = PILImageResampling.BICUBIC   ,						UpperCamelCase_:     Union[str, Any] = None   ,						**UpperCamelCase_:     Union[str, Any]   ,						)      ->       Any:





									"""simple docstring"""







									lowercase__						=				get_size_dict(UpperCamelCase_   ,						default_to_square=UpperCamelCase_							)
									if "height" not in size or "width" not in size:
											raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'							)
									lowercase__						=				(size['''height'''], size['''width'''])
									return resize(UpperCamelCase_   ,						size=UpperCamelCase_   ,						resample=UpperCamelCase_   ,						data_format=UpperCamelCase_   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Union[str, Any] = None   ,						**UpperCamelCase_:     Optional[int]   ,						)      ->       List[Any]:





									"""simple docstring"""







									return rescale(UpperCamelCase_   ,						scale=UpperCamelCase_   ,						data_format=UpperCamelCase_   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Dict = None   ,						**UpperCamelCase_:     Optional[Any]   ,						)      ->       Tuple:





									"""simple docstring"""







									return normalize(UpperCamelCase_   ,						mean=UpperCamelCase_   ,						std=UpperCamelCase_   ,						data_format=UpperCamelCase_   ,						**UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     List[str] = None   ,						UpperCamelCase_:     Any = None   ,						UpperCamelCase_:     Optional[int] = None   ,						UpperCamelCase_:     Tuple = None   ,						UpperCamelCase_:     Any = None   ,						UpperCamelCase_:     Optional[Any] = None   ,						UpperCamelCase_:     int = None   ,						UpperCamelCase_:     Optional[Any] = None   ,						UpperCamelCase_:     str = None   ,						UpperCamelCase_:     List[str] = None   ,						UpperCamelCase_:     List[Any] = ChannelDimension.FIRST   ,						**UpperCamelCase_:     Union[str, Any]   ,						)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				do_resize if do_resize is not None else self.do_resize
									lowercase__						=				resample if resample is not None else self.resample
									lowercase__						=				do_rescale if do_rescale is not None else self.do_rescale
									lowercase__						=				rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__						=				do_normalize if do_normalize is not None else self.do_normalize
									lowercase__						=				image_mean if image_mean is not None else self.image_mean
									lowercase__						=				image_std if image_std is not None else self.image_std
									lowercase__						=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									lowercase__						=				size if size is not None else self.size
									lowercase__						=				get_size_dict(UpperCamelCase_   ,						default_to_square=UpperCamelCase_							)

									lowercase__						=				make_list_of_images(UpperCamelCase_							)

									if not valid_images(UpperCamelCase_							):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.'''							)

									if do_resize and size is None or resample is None:
											raise ValueError('''Size and resample must be specified if do_resize is True.'''							)

									if do_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

									if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('''Image mean and std must be specified if do_normalize is True.'''							)

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
											lowercase__						=				[convert_to_rgb(UpperCamelCase_							) for image in images]

									# All transformations expect numpy arrays.
									lowercase__						=				[to_numpy_array(UpperCamelCase_							) for image in images]

									if do_resize:
											lowercase__						=				[self.resize(image=UpperCamelCase_   ,						size=UpperCamelCase_   ,						resample=UpperCamelCase_							) for image in images]

									if do_rescale:
											lowercase__						=				[self.rescale(image=UpperCamelCase_   ,						scale=UpperCamelCase_							) for image in images]

									if do_normalize:
											lowercase__						=				[self.normalize(image=UpperCamelCase_   ,						mean=UpperCamelCase_   ,						std=UpperCamelCase_							) for image in images]

									lowercase__						=				[to_channel_dimension_format(UpperCamelCase_   ,						UpperCamelCase_							) for image in images]

									lowercase__						=				BatchFeature(data={'''pixel_values''': images}   ,						tensor_type=UpperCamelCase_							)

									return encoded_outputs



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

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


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

lowerCAmelCase	=							'=======\n>>>>>>>\n'

lowerCAmelCase	=							[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

lowerCAmelCase	=							[
    # (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       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		return ConvertCommand(args.tfds_path						,    args.datasets_directory					)


class 	_a							(      UpperCamelCase__      ):

							@staticmethod
							def 				lowerCamelCase_			(							UpperCamelCase_:     ArgumentParser							)      ->       int:





									"""simple docstring"""







									lowercase__						=				parser.add_parser(
									    '''convert'''   ,						help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''   ,						)
									train_parser.add_argument(
									    '''--tfds_path'''   ,						type=UpperCamelCase_   ,						required=UpperCamelCase_   ,						help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''   ,						)
									train_parser.add_argument(
									    '''--datasets_directory'''   ,						type=UpperCamelCase_   ,						required=UpperCamelCase_   ,						help='''Path to the HuggingFace Datasets folder.'''							)
									train_parser.set_defaults(func=UpperCamelCase_							)

							def __init__(							self:     Dict   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     str   ,						*UpperCamelCase_:     Union[str, Any]							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				get_logger('''datasets-cli/converting'''							)

									lowercase__						=				tfds_path
									lowercase__						=				datasets_directory


							def 				lowerCamelCase_			(							self:     str							)      ->       Dict:





									"""simple docstring"""







									if os.path.isdir(self._tfds_path							):
											lowercase__						=				os.path.abspath(self._tfds_path							)
									elif os.path.isfile(self._tfds_path							):
											lowercase__						=				os.path.dirname(self._tfds_path							)
									else:
											raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.'''							)

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

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

									lowercase__						=				[]
									lowercase__						=				[]
									lowercase__						=				{}

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

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

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

											with open(UpperCamelCase_   ,						encoding='''utf-8'''							) as f:
													lowercase__						=				f.readlines()

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

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

											if is_builder or "wmt" in f_name:
													# We create a new directory for each dataset
													lowercase__						=				f_name.replace('''.py'''   ,						''''''							)
													lowercase__						=				os.path.join(UpperCamelCase_   ,						UpperCamelCase_							)
													lowercase__						=				os.path.join(UpperCamelCase_   ,						UpperCamelCase_							)
													os.makedirs(UpperCamelCase_   ,						exist_ok=UpperCamelCase_							)
													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(UpperCamelCase_							)

											if needs_manual_update:
													with_manual_update.append(UpperCamelCase_							)

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

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


