

'''simple docstring'''



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

import yaml







class 		lowerCAmelCase__  ( yaml.SafeLoader   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			List[str]     )    ->					str:


      """simple docstring"""




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


 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			str=False     )    ->					Dict:


      """simple docstring"""




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







def   a__			(    a__		):




     """simple docstring"""


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

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







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = {"train_eval_index"}  # train-eval-index in the YAML metadata
 @classmethod
 def  UpperCAmelCase__						(						cls   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Path     )    ->					"DatasetMetadata":


      """simple docstring"""




      with open(__SCREAMING_SNAKE_CASE   ,				encoding="""utf-8"""     ) as readme_file:
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = _split_yaml_from_readme(readme_file.read()     )
      if yaml_string is not None:
           return cls.from_yaml_string(__SCREAMING_SNAKE_CASE     )
      else:
           return cls()
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Path     )    ->					Dict:


      """simple docstring"""




      if path.exists():
           with open(__SCREAMING_SNAKE_CASE   ,				encoding="""utf-8"""     ) as readme_file:
                __SCREAMING_SNAKE_CASE      = readme_file.read()
      else:
           __SCREAMING_SNAKE_CASE      = None
      __SCREAMING_SNAKE_CASE      = self._to_readme(__SCREAMING_SNAKE_CASE     )
      with open(__SCREAMING_SNAKE_CASE   ,				"""w"""   ,				encoding="""utf-8"""     ) as readme_file:
           readme_file.write(__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[str] = None     )    ->					str:


      """simple docstring"""




      if readme_content is not None:
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = _split_yaml_from_readme(__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = """---\n""" + self.to_yaml_string() + """---\n""" + content
      else:
           __SCREAMING_SNAKE_CASE      = """---\n""" + self.to_yaml_string() + """---\n"""
      return full_content
 @classmethod
 def  UpperCAmelCase__						(						cls   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			str     )    ->					"DatasetMetadata":


      """simple docstring"""




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

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


 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					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=__SCREAMING_SNAKE_CASE   ,				allow_unicode=__SCREAMING_SNAKE_CASE   ,				encoding="""utf-8"""   ,				).decode("""utf-8"""     )


UpperCAmelCase					:			Any       =	{
    '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					:			Any       =	ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
  ap.add_argument('readme_filepath')
  UpperCAmelCase					:			Tuple       =	ap.parse_args()

  UpperCAmelCase					:			List[Any]       =	Path(args.readme_filepath)
  UpperCAmelCase					:			Union[str, Any]       =	DatasetMetadata.from_readme(readme_filepath)
  print(dataset_metadata)
  dataset_metadata.to_readme(readme_filepath)









'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "LayoutLMv2ImageProcessor"
 lowerCAmelCase__       = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
 def __init__(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					List[str]:


      """simple docstring"""




      if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError("""You need to specify an `image_processor`."""     )
      if tokenizer is None:
           raise ValueError("""You need to specify a `tokenizer`."""     )

      super().__init__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Union[List[List[int]], List[List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[List[int], List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, PaddingStrategy] = False   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, TruncationStrategy] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			int = 0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, TensorType]] = None   ,				**__SCREAMING_SNAKE_CASE   :			Any   ,				)    ->					BatchEncoding:


      """simple docstring"""




      if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               """You cannot provide bounding boxes """
               """if you initialized the image processor with apply_ocr set to True."""     )

      if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""     )

      if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""     )

      # first, apply the image processor
      __SCREAMING_SNAKE_CASE      = self.image_processor(images=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE     )

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
                __SCREAMING_SNAKE_CASE      = [text]  # add batch dimension (as the image processor always adds a batch dimension)
           __SCREAMING_SNAKE_CASE      = features["""words"""]

      __SCREAMING_SNAKE_CASE      = self.tokenizer(
          text=text if text is not None else features["""words"""]   ,				text_pair=text_pair if text_pair is not None else None   ,				boxes=boxes if boxes is not None else features["""boxes"""]   ,				word_labels=__SCREAMING_SNAKE_CASE   ,				add_special_tokens=__SCREAMING_SNAKE_CASE   ,				padding=__SCREAMING_SNAKE_CASE   ,				truncation=__SCREAMING_SNAKE_CASE   ,				max_length=__SCREAMING_SNAKE_CASE   ,				stride=__SCREAMING_SNAKE_CASE   ,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE   ,				return_token_type_ids=__SCREAMING_SNAKE_CASE   ,				return_attention_mask=__SCREAMING_SNAKE_CASE   ,				return_overflowing_tokens=__SCREAMING_SNAKE_CASE   ,				return_special_tokens_mask=__SCREAMING_SNAKE_CASE   ,				return_offsets_mapping=__SCREAMING_SNAKE_CASE   ,				return_length=__SCREAMING_SNAKE_CASE   ,				verbose=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE   ,				)

      # add pixel values
      __SCREAMING_SNAKE_CASE      = features.pop("""pixel_values"""     )
      if return_overflowing_tokens is True:
           __SCREAMING_SNAKE_CASE      = self.get_overflowing_images(__SCREAMING_SNAKE_CASE   ,				encoded_inputs["""overflow_to_sample_mapping"""]     )
      __SCREAMING_SNAKE_CASE      = images

      return encoded_inputs
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = []
      for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]     )

      if len(__SCREAMING_SNAKE_CASE     ) != len(__SCREAMING_SNAKE_CASE     ):
           raise ValueError(
               """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
               f' {len(__SCREAMING_SNAKE_CASE     )} and {len(__SCREAMING_SNAKE_CASE     )}'     )

      return images_with_overflow
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				*__SCREAMING_SNAKE_CASE   :			str   ,				**__SCREAMING_SNAKE_CASE   :			List[str]     )    ->					List[str]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				*__SCREAMING_SNAKE_CASE   :			Dict   ,				**__SCREAMING_SNAKE_CASE   :			Any     )    ->					Union[str, Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      return ["input_ids", "bbox", "attention_mask", "image"]
 @property
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
      return self.image_processor_class


 @property
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Any:


      """simple docstring"""




      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
      return self.image_processor






