


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


UpperCamelCase__    							= logging.get_logger(__name__)




def        lowerCAmelCase_			(					__A							)					->    List[List[ImageInput]]:







      '''simple docstring'''


      if isinstance(__A,  (list, tuple)							) and isinstance(videos[0],  (list, tuple)							) and is_valid_image(videos[0][0]							):
            return videos

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

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

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







class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[str]              =      ['pixel_values']


   def __init__(self					:						Dict	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						PILImageResampling = PILImageResampling.BILINEAR	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Union[int, float] = 1 / 2_5_5	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    None:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase		)
         UpperCAmelCase__								=  size if size is not None else {"shortest_edge": 2_2_4}
         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase	,  default_to_square=__UpperCAmelCase		)
         UpperCAmelCase__								=  crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase	,  param_name="crop_size"		)

         UpperCAmelCase__								=  do_resize
         UpperCAmelCase__								=  size
         UpperCAmelCase__								=  do_center_crop
         UpperCAmelCase__								=  crop_size
         UpperCAmelCase__								=  resample
         UpperCAmelCase__								=  do_rescale
         UpperCAmelCase__								=  rescale_factor
         UpperCAmelCase__								=  do_normalize
         UpperCAmelCase__								=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase__								=  image_std if image_std is not None else IMAGENET_STANDARD_STD


   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						np.ndarray	,  __UpperCAmelCase					:						Dict[str, int]	,  __UpperCAmelCase					:						PILImageResampling = PILImageResampling.BILINEAR	,  __UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase	,  default_to_square=__UpperCAmelCase		)
         if "shortest_edge" in size:
               UpperCAmelCase__								=  get_resize_output_image_size(__UpperCAmelCase	,  size["shortest_edge"]	,  default_to_square=__UpperCAmelCase		)
         elif "height" in size and "width" in size:
               UpperCAmelCase__								=  (size["height"], size["width"])
         else:
               raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""		)
         return resize(__UpperCAmelCase	,  size=__UpperCAmelCase	,  resample=__UpperCAmelCase	,  data_format=__UpperCAmelCase	,  **__UpperCAmelCase		)


   def       lowercase_				(self					:						Optional[Any]	,  __UpperCAmelCase					:						np.ndarray	,  __UpperCAmelCase					:						Dict[str, int]	,  __UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None	,  **__UpperCAmelCase					:						Optional[int]	,  )						->    np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase		)
         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(__UpperCAmelCase	,  size=(size["height"], size["width"])	,  data_format=__UpperCAmelCase	,  **__UpperCAmelCase		)


   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						np.ndarray	,  __UpperCAmelCase					:						Union[int, float]	,  __UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None	,  **__UpperCAmelCase					:						Union[str, Any]	,  )						->    List[Any]:
         """simple docstring"""



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


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						np.ndarray	,  __UpperCAmelCase					:						Union[float, List[float]]	,  __UpperCAmelCase					:						Union[float, List[float]]	,  __UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None	,  **__UpperCAmelCase					:						List[str]	,  )						->    np.ndarray:
         """simple docstring"""



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


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						ImageInput	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						PILImageResampling = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						float = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  __UpperCAmelCase					:						Optional[ChannelDimension] = ChannelDimension.FIRST	,  )						->    np.ndarray:
         """simple docstring"""



         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.
         UpperCAmelCase__								=  to_numpy_array(__UpperCAmelCase		)

         if do_resize:
               UpperCAmelCase__								=  self.resize(image=__UpperCAmelCase	,  size=__UpperCAmelCase	,  resample=__UpperCAmelCase		)

         if do_center_crop:
               UpperCAmelCase__								=  self.center_crop(__UpperCAmelCase	,  size=__UpperCAmelCase		)

         if do_rescale:
               UpperCAmelCase__								=  self.rescale(image=__UpperCAmelCase	,  scale=__UpperCAmelCase		)

         if do_normalize:
               UpperCAmelCase__								=  self.normalize(image=__UpperCAmelCase	,  mean=__UpperCAmelCase	,  std=__UpperCAmelCase		)

         UpperCAmelCase__								=  to_channel_dimension_format(__UpperCAmelCase	,  __UpperCAmelCase		)
         return image





   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						ImageInput	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						PILImageResampling = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						float = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  __UpperCAmelCase					:						Optional[Union[str, TensorType]] = None	,  __UpperCAmelCase					:						ChannelDimension = ChannelDimension.FIRST	,  **__UpperCAmelCase					:						int	,  )						->    PIL.Image.Image:
         """simple docstring"""



         UpperCAmelCase__								=  do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase__								=  resample if resample is not None else self.resample
         UpperCAmelCase__								=  do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase__								=  do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase__								=  rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase__								=  do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase__								=  image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase__								=  image_std if image_std is not None else self.image_std

         UpperCAmelCase__								=  size if size is not None else self.size
         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase	,  default_to_square=__UpperCAmelCase		)
         UpperCAmelCase__								=  crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase	,  param_name="crop_size"		)

         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."		)

         UpperCAmelCase__								=  make_batched(__UpperCAmelCase		)

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

         UpperCAmelCase__								=  {"pixel_values": videos}
         return BatchFeature(data=__UpperCAmelCase	,  tensor_type=__UpperCAmelCase		)




from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml





class 					A							(    yaml.SafeLoader					):


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						str		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  [self.constructed_objects[key_node] for key_node, _ in node.value]
         UpperCAmelCase__								=  [tuple(__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else key for key in keys]
         UpperCAmelCase__								=  Counter(__UpperCAmelCase		)
         UpperCAmelCase__								=  [key for key in counter if counter[key] > 1]
         if duplicate_keys:
               raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""		)





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Any=False		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  super().construct_mapping(__UpperCAmelCase	,  deep=__UpperCAmelCase		)
         self._check_no_duplicates_on_constructed_node(__UpperCAmelCase		)
         return mapping




def        lowerCAmelCase_			(					__A							)					->    Tuple[Optional[str], str]:







      '''simple docstring'''


      UpperCAmelCase__								=  list(readme_content.splitlines()							)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            UpperCAmelCase__								=  full_content[1:].index("---"							) + 1
            UpperCAmelCase__								=  "\n".join(full_content[1:sep_idx]							)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]							)

      return None, "\n".join(__A							)





class 					A							(    UpperCAmelCase_					):
   # class attributes
   __UpperCAmelCase			:			Optional[Any]              =      {'train_eval_index'}  # train-eval-index in the YAML metadata


   @classmethod
   def       lowercase_				(cls					:						List[str]	,  __UpperCAmelCase					:						Path		)						->    "DatasetMetadata":
         """simple docstring"""



         with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(readme_file.read()		)
         if yaml_string is not None:
               return cls.from_yaml_string(__UpperCAmelCase		)
         else:
               return cls()


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Path		)						->    str:
         """simple docstring"""



         if path.exists():
               with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
                     UpperCAmelCase__								=  readme_file.read()
         else:
               UpperCAmelCase__								=  None
         UpperCAmelCase__								=  self._to_readme(__UpperCAmelCase		)
         with open(__UpperCAmelCase	,  "w"	,  encoding="utf-8"		) as readme_file:
               readme_file.write(__UpperCAmelCase		)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    str:
         """simple docstring"""



         if readme_content is not None:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(__UpperCAmelCase		)
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n" + content
         else:
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n"
         return full_content


   @classmethod
   def       lowercase_				(cls					:						Optional[int]	,  __UpperCAmelCase					:						str		)						->    "DatasetMetadata":
         """simple docstring"""



         UpperCAmelCase__								=  yaml.load(__UpperCAmelCase	,  Loader=_NoDuplicateSafeLoader		) or {}

         # Convert the YAML keys to DatasetMetadata fields
         UpperCAmelCase__								=  {
             (key.replace("-"	,  "_"		) if key.replace("-"	,  "_"		) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**__UpperCAmelCase		)





   def       lowercase_				(self					:						List[str]		)						->    str:
         """simple docstring"""



         return yaml.safe_dump(
             {
                 (key.replace("_"	,  "-"		) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }	,  sort_keys=__UpperCAmelCase	,  allow_unicode=__UpperCAmelCase	,  encoding="utf-8"	,  ).decode("utf-8"		)




UpperCamelCase__    							= {
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      UpperCamelCase__    							= ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
      ap.add_argument('readme_filepath')
      UpperCamelCase__    							= ap.parse_args()

      UpperCamelCase__    							= Path(args.readme_filepath)
      UpperCamelCase__    							= DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)



