

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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


A_	:			Optional[Any]  	=      logging.get_logger(__name__)

def 						UpperCamelCase (lowercase_:			Optional[Any] )					->				List[List[ImageInput]]:
	if isinstance(lowercase_   ,				(list, tuple) ) and isinstance(videos[0]   ,				(list, tuple) ) and is_valid_image(videos[0][0] ):
		return videos

	elif isinstance(lowercase_   ,				(list, tuple) ) and is_valid_image(videos[0] ):
		return [videos]

	elif is_valid_image(lowercase_ ):
		return [[videos]]

	raise ValueError(f"""Could not make batched video from {videos}""" )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	['''pixel_values''']






					def __init__(			self   ,	A__ = True   ,	A__ = None   ,	A__ = PILImageResampling.BILINEAR   ,	A__ = True   ,	A__ = None   ,	A__ = True   ,	A__ = 1 / 255   ,	A__ = True   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						super().__init__(**A__      )
						A__							:   Optional[int]					       =		size if size is not None else {"""shortest_edge""": 224}
						A__							:   List[Any]					       =		get_size_dict(A__   ,	default_to_square=A__      )
						A__							:   Dict					       =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						A__							:   Any					       =		get_size_dict(A__   ,	param_name="""crop_size"""      )

						A__							:   Tuple					       =		do_resize
						A__							:   int					       =		size
						A__							:   str					       =		do_center_crop
						A__							:   Any					       =		crop_size
						A__							:   int					       =		resample
						A__							:   Dict					       =		do_rescale
						A__							:   Any					       =		rescale_factor
						A__							:   Optional[int]					       =		do_normalize
						A__							:   Union[str, Any]					       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A__							:   Optional[Any]					       =		image_std if image_std is not None else IMAGENET_STANDARD_STD






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = PILImageResampling.BILINEAR   ,	A__ = None   ,	**A__   ,	):
						A__							:   Optional[int]					       =		get_size_dict(A__   ,	default_to_square=A__      )
						if "shortest_edge" in size:
							A__							:   List[Any]					       =		get_resize_output_image_size(A__   ,	size["""shortest_edge"""]   ,	default_to_square=A__      )
						elif "height" in size and "width" in size:
							A__							:   List[Any]					       =		(size["""height"""], size["""width"""])
						else:
							raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
						return resize(A__   ,	size=A__   ,	resample=A__   ,	data_format=A__   ,	**A__      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = None   ,	**A__   ,	):
						A__							:   Union[str, Any]					       =		get_size_dict(A__      )
						if "height" not in size or "width" not in size:
							raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""      )
						return center_crop(A__   ,	size=(size["""height"""], size["""width"""])   ,	data_format=A__   ,	**A__      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = None   ,	**A__   ,	):
						return rescale(A__   ,	scale=A__   ,	data_format=A__   ,	**A__      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__ = None   ,	**A__   ,	):
						return normalize(A__   ,	mean=A__   ,	std=A__   ,	data_format=A__   ,	**A__      )






					def   __A      (			self   ,	A__   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = ChannelDimension.FIRST   ,	):
						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_center_crop and crop_size is None:
							raise ValueError("""Crop size must be specified if do_center_crop 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."""      )

						# All transformations expect numpy arrays.
						A__							:   int					       =		to_numpy_array(A__      )

						if do_resize:
							A__							:   str					       =		self.resize(image=A__   ,	size=A__   ,	resample=A__      )

						if do_center_crop:
							A__							:   List[Any]					       =		self.center_crop(A__   ,	size=A__      )

						if do_rescale:
							A__							:   Tuple					       =		self.rescale(image=A__   ,	scale=A__      )

						if do_normalize:
							A__							:   Tuple					       =		self.normalize(image=A__   ,	mean=A__   ,	std=A__      )

						A__							:   int					       =		to_channel_dimension_format(A__   ,	A__      )
						return image






					def   __A      (			self   ,	A__   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = ChannelDimension.FIRST   ,	**A__   ,	):
						A__							:   Union[str, Any]					       =		do_resize if do_resize is not None else self.do_resize
						A__							:   Tuple					       =		resample if resample is not None else self.resample
						A__							:   Optional[Any]					       =		do_center_crop if do_center_crop is not None else self.do_center_crop
						A__							:   Any					       =		do_rescale if do_rescale is not None else self.do_rescale
						A__							:   List[Any]					       =		rescale_factor if rescale_factor is not None else self.rescale_factor
						A__							:   Tuple					       =		do_normalize if do_normalize is not None else self.do_normalize
						A__							:   Optional[int]					       =		image_mean if image_mean is not None else self.image_mean
						A__							:   str					       =		image_std if image_std is not None else self.image_std

						A__							:   Any					       =		size if size is not None else self.size
						A__							:   List[Any]					       =		get_size_dict(A__   ,	default_to_square=A__      )
						A__							:   Optional[Any]					       =		crop_size if crop_size is not None else self.crop_size
						A__							:   List[str]					       =		get_size_dict(A__   ,	param_name="""crop_size"""      )

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

						A__							:   Any					       =		make_batched(A__      )

						A__							:   int					       =		[
						    [
						        self._preprocess_image(
						            image=A__   ,	do_resize=A__   ,	size=A__   ,	resample=A__   ,	do_center_crop=A__   ,	crop_size=A__   ,	do_rescale=A__   ,	rescale_factor=A__   ,	do_normalize=A__   ,	image_mean=A__   ,	image_std=A__   ,	data_format=A__   ,	)
						        for img in video
						    ]
						    for video in videos
						]

						A__							:   List[str]					       =		{"""pixel_values""": videos}
						return BatchFeature(data=A__   ,	tensor_type=A__      )






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

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


A_	:			Optional[int]  	=      '<<<<<<< This should probably be modified because it mentions: '

A_	:			int  	=      '=======\n>>>>>>>\n'

A_	:			Optional[Any]  	=      [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

A_	:			List[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 						UpperCamelCase (lowercase_:			Namespace )					->				str:
	return ConvertCommand(args.tfds_path   ,				args.datasets_directory )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		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__      ):
						A__							:   Optional[Any]					       =		get_logger("""datasets-cli/converting"""      )

						A__							:   Optional[Any]					       =		tfds_path
						A__							:   Dict					       =		datasets_directory






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

						A__							:   Optional[int]					       =		os.path.abspath(self._datasets_directory      )

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

						A__							:   Optional[Any]					       =		[]
						A__							:   List[Any]					       =		[]
						A__							:   List[Any]					       =		{}

						if os.path.isdir(self._tfds_path      ):
							A__							:   List[str]					       =		os.listdir(A__      )
						else:
							A__							:   Optional[Any]					       =		[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(A__   ,	A__      )
							A__							:   Optional[Any]					       =		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__							:   Dict					       =		f.readlines()

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

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



