



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 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	["""image_processor""", """tokenizer"""]
			UpperCamelCase__			      =	"""LayoutLMv2ImageProcessor"""
			UpperCamelCase__			      =	("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



			def __init__(		self     :    Any				,   __UpperCamelCase     :    Union[str, Any]=None				,   __UpperCamelCase     :    Any=None				,   **__UpperCamelCase     :    Any   )->str:
							if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				,   __UpperCamelCase				,   )
											_UpperCAmelCase					=    kwargs.pop('''feature_extractor'''   )

							_UpperCAmelCase					=    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__(__UpperCamelCase				,   __UpperCamelCase   )



			def __call__(		self     :    List[Any]				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,   __UpperCamelCase     :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				,   __UpperCamelCase     :    Union[List[List[int]], List[List[List[int]]]] = None				,   __UpperCamelCase     :    Optional[Union[List[int], List[List[int]]]] = None				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    Union[bool, str, PaddingStrategy] = False				,   __UpperCamelCase     :    Union[bool, str, TruncationStrategy] = None				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    Optional[bool] = None				,   __UpperCamelCase     :    Optional[bool] = None				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    Optional[Union[str, TensorType]] = None				,   **__UpperCamelCase     :    Union[str, Any]				,   )->BatchEncoding:
							# verify input
							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
							_UpperCAmelCase					=    self.image_processor(images=__UpperCamelCase				,   return_tensors=__UpperCamelCase   )

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

							_UpperCAmelCase					=    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=__UpperCamelCase				,   add_special_tokens=__UpperCamelCase				,   padding=__UpperCamelCase				,   truncation=__UpperCamelCase				,   max_length=__UpperCamelCase				,   stride=__UpperCamelCase				,   pad_to_multiple_of=__UpperCamelCase				,   return_token_type_ids=__UpperCamelCase				,   return_attention_mask=__UpperCamelCase				,   return_overflowing_tokens=__UpperCamelCase				,   return_special_tokens_mask=__UpperCamelCase				,   return_offsets_mapping=__UpperCamelCase				,   return_length=__UpperCamelCase				,   verbose=__UpperCamelCase				,   return_tensors=__UpperCamelCase				,   **__UpperCamelCase				,   )

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

							return encoded_inputs



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    Any   )->Union[str, Any]:
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							_UpperCAmelCase					=    []
							for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]   )

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

							return images_with_overflow



			def  lowercase__						(		self     :    Any				,   *__UpperCamelCase     :    Union[str, Any]				,   **__UpperCamelCase     :    List[str]   )->Union[str, Any]:
							return self.tokenizer.batch_decode(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Tuple				,   *__UpperCamelCase     :    List[str]				,   **__UpperCamelCase     :    List[str]   )->Optional[Any]:
							return self.tokenizer.decode(*__UpperCamelCase				,   **__UpperCamelCase   )



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



			@property
			def  lowercase__						(		self     :    Any   )->List[str]:
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,   __UpperCamelCase				,   )
							return self.image_processor_class



			@property
			def  lowercase__						(		self     :    str   )->Optional[int]:
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,   __UpperCamelCase				,   )
							return self.image_processor








"""simple docstring"""


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

import yaml
class 			_a (					yaml.SafeLoader):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Any   )->List[Any]:
							_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     :    List[str]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    str=False   )->Dict:
							_UpperCAmelCase					=    super().construct_mapping(__UpperCamelCase				,   deep=__UpperCamelCase   )
							self._check_no_duplicates_on_constructed_node(__UpperCamelCase   )
							return mapping







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						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(_SCREAMING_SNAKE_CASE				)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			# class attributes
			UpperCamelCase__			      =	{"""train_eval_index"""}  # train-eval-index in the YAML metadata



			@classmethod
			def  lowercase__						(		cls     :    List[Any]				,   __UpperCamelCase     :    Path   )->"DatasetMetadata":
							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     :    Tuple				,   __UpperCamelCase     :    Path   )->List[Any]:
							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     :    List[str]				,   __UpperCamelCase     :    Optional[str] = None   )->str:
							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     :    str				,   __UpperCamelCase     :    str   )->"DatasetMetadata":
							_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     :    str   )->str:
							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'''   )



__A   :							str										=   {
    "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

					__A   :							str										=   ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
					ap.add_argument("readme_filepath")
					__A   :							Union[str, Any]										=   ap.parse_args()

					__A   :							Dict										=   Path(args.readme_filepath)
					__A   :							Tuple										=   DatasetMetadata.from_readme(readme_filepath)
					print(dataset_metadata)
					dataset_metadata.to_readme(readme_filepath)



