
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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase							=       logging.get_logger(__name__)






class   UpperCAmelCase		(						A_ ):
   A__					:		int						     =     ["pixel_values"]



   def __init__(self		:					int    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[Dict[str, int]] = None    ,						snake_case__		:					PILImageResampling = PILImageResampling.BICUBIC    ,						snake_case__		:					bool = True    ,						snake_case__		:					bool = True    ,						snake_case__		:					Union[int, float] = 1 / 2_55    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	None:


          '''simple docstring'''


          super().__init__(**snake_case__ )
          snake_case       :   Union[str, Any]							=      size if size is not None else {"height": 2_24, "width": 2_24}
          snake_case       :   Dict							=      get_size_dict(snake_case__ )
          snake_case       :   Optional[Any]							=      crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          snake_case       :   List[Any]							=      get_size_dict(snake_case__    ,						default_to_square=snake_case__    ,						param_name="crop_size" )

          snake_case       :   Tuple							=      do_resize
          snake_case       :   Optional[Any]							=      do_rescale
          snake_case       :   str							=      do_normalize
          snake_case       :   int							=      do_center_crop
          snake_case       :   Tuple							=      crop_size
          snake_case       :   int							=      size
          snake_case       :   Union[str, Any]							=      resample
          snake_case       :   int							=      rescale_factor
          snake_case       :   Any							=      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          snake_case       :   List[str]							=      image_std if image_std is not None else IMAGENET_DEFAULT_STD



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					PILImageResampling = PILImageResampling.BILINEAR    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Any    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   List[str]							=      get_size_dict(snake_case__ )

          if "shortest_edge" in size:
                 snake_case       :   List[str]							=      get_resize_output_image_size(snake_case__    ,						size=size["shortest_edge"]    ,						default_to_square=snake_case__ )
                 # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
          elif "height" in size and "width" in size:
                 snake_case       :   List[Any]							=      (size["height"], size["width"])
          else:
                 raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}""" )
          return resize(snake_case__    ,						size=snake_case__    ,						resample=snake_case__    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					List[str]    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   Dict							=      get_size_dict(snake_case__ )
          if "height" not in size or "width" not in size:
                 raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
          return center_crop(snake_case__    ,						size=(size["height"], size["width"])    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					float    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					List[Any] )		->	np.ndarray:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Union[float, List[float]]    ,						snake_case__		:					Union[float, List[float]]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					List[str]    ,						)		->	np.ndarray:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					ImageInput    ,						snake_case__		:					Optional[bool] = None    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					PILImageResampling = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					int = None    ,						snake_case__		:					Optional[bool] = None    ,						snake_case__		:					Optional[float] = None    ,						snake_case__		:					Optional[bool] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[str, TensorType]] = None    ,						snake_case__		:					Union[str, ChannelDimension] = ChannelDimension.FIRST    ,						**snake_case__		:					Optional[Any]    ,						)		->	BatchFeature:


          '''simple docstring'''


          snake_case       :   int							=      do_resize if do_resize is not None else self.do_resize
          snake_case       :   int							=      do_rescale if do_rescale is not None else self.do_rescale
          snake_case       :   Union[str, Any]							=      do_normalize if do_normalize is not None else self.do_normalize
          snake_case       :   str							=      do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case       :   Tuple							=      crop_size if crop_size is not None else self.crop_size
          snake_case       :   Union[str, Any]							=      get_size_dict(snake_case__    ,						param_name="crop_size"    ,						default_to_square=snake_case__ )
          snake_case       :   List[str]							=      resample if resample is not None else self.resample
          snake_case       :   Dict							=      rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case       :   Optional[int]							=      image_mean if image_mean is not None else self.image_mean
          snake_case       :   Tuple							=      image_std if image_std is not None else self.image_std

          snake_case       :   str							=      size if size is not None else self.size
          snake_case       :   List[str]							=      get_size_dict(snake_case__ )

          if not is_batched(snake_case__ ):
                 snake_case       :   Optional[int]							=      [images]

          if not valid_images(snake_case__ ):
                 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:
                 raise ValueError("Size 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." )

          # All transformations expect numpy arrays.
          snake_case       :   List[Any]							=      [to_numpy_array(snake_case__ ) for image in images]

          if do_resize:
                 snake_case       :   Tuple							=      [self.resize(image=snake_case__    ,						size=snake_case__    ,						resample=snake_case__ ) for image in images]

          if do_center_crop:
                 snake_case       :   List[str]							=      [self.center_crop(image=snake_case__    ,						size=snake_case__ ) for image in images]

          if do_rescale:
                 snake_case       :   Union[str, Any]							=      [self.rescale(image=snake_case__    ,						scale=snake_case__ ) for image in images]

          if do_normalize:
                 snake_case       :   Optional[Any]							=      [self.normalize(image=snake_case__    ,						mean=snake_case__    ,						std=snake_case__ ) for image in images]

          snake_case       :   Union[str, Any]							=      [to_channel_dimension_format(snake_case__    ,						snake_case__ ) for image in images]

          snake_case       :   Tuple							=      {"pixel_values": images}
          return BatchFeature(data=snake_case__    ,						tensor_type=snake_case__ )












"""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


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

a						: Tuple     	=			'''=======
>>>>>>>
'''

a						: List[Any]     	=			[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

a						: 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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Namespace					) ->Any:






	'''simple docstring'''






	return ConvertCommand(args.tfds_path    ,       args.datasets_directory					)







class        __UpperCamelCase				(						a__						):






							@staticmethod
							def     __a   (   lowerCAmelCase__      )	-> int:
								a      :		Any	=				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      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					*lowerCAmelCase__      )	-> Tuple:
								a      :		List[str]	=				get_logger("datasets-cli/converting"      )

								a      :		Optional[int]	=				tfds_path
								a      :		str	=				datasets_directory






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

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

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

								a      :		str	=				[]
								a      :		Union[str, Any]	=				[]
								a      :		str	=				{}

								if os.path.isdir(self._tfds_path      ):
									a      :		Dict	=				os.listdir(lowerCAmelCase__      )
								else:
									a      :		Dict	=				[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(lowerCAmelCase__      ,					lowerCAmelCase__      )
									a      :		Dict	=				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:
										a      :		Any	=				f.readlines()

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

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

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


