



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					( _a      ):
   UpperCamelCase					:Optional[Any]     =			["""image_processor""", """tokenizer"""]
   UpperCamelCase					:Tuple     =			"""LayoutLMv2ImageProcessor"""
   UpperCamelCase					:Union[str, Any]     =			("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")





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

      lowerCamelCase__ :      List[str]								=		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__(snake_case_							,    snake_case_				)





   def __call__(self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = True							,    __magic_name__ = False							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = 0							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = True							,    __magic_name__ = None							,    **__magic_name__							,    ):
      # 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
      lowerCamelCase__ :      Any								=		self.image_processor(images=snake_case_							,    return_tensors=snake_case_				)

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

      lowerCamelCase__ :      int								=		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=snake_case_							,    add_special_tokens=snake_case_							,    padding=snake_case_							,    truncation=snake_case_							,    max_length=snake_case_							,    stride=snake_case_							,    pad_to_multiple_of=snake_case_							,    return_token_type_ids=snake_case_							,    return_attention_mask=snake_case_							,    return_overflowing_tokens=snake_case_							,    return_special_tokens_mask=snake_case_							,    return_offsets_mapping=snake_case_							,    return_length=snake_case_							,    verbose=snake_case_							,    return_tensors=snake_case_							,    **snake_case_							,    )

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

      return encoded_inputs





   def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      lowerCamelCase__ :      Dict								=		[]
      for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]				)

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

      return images_with_overflow





   def        _snake_case    (self							,    *__magic_name__							,    **__magic_name__				):
      return self.tokenizer.batch_decode(*snake_case_							,    **snake_case_				)





   def        _snake_case    (self							,    *__magic_name__							,    **__magic_name__				):
      return self.tokenizer.decode(*snake_case_							,    **snake_case_				)





   @property
   def        _snake_case    (self				):
      return ["input_ids", "bbox", "attention_mask", "image"]





   @property
   def        _snake_case    (self				):
      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							,    snake_case_							,    )
      return self.image_processor_class





   @property
   def        _snake_case    (self				):
      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							,    snake_case_							,    )
      return self.image_processor








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=A_      )
class 				__A					( A_      ):
			UpperCamelCase					:str     =			field(default='''automatic-speech-recognition'''					, metadata={'''include_in_asdict_even_if_is_default''': True}      )
			UpperCamelCase					:ClassVar[Features]     =			Features({'''audio''': Audio()}      )
			UpperCamelCase					:ClassVar[Features]     =			Features({'''transcription''': Value('''string'''      )}      )
			UpperCamelCase					:str     =			"audio"
			UpperCamelCase					:str     =			"transcription"





			def        _snake_case    (self							,    __magic_name__				):
						if self.audio_column not in features:
									raise ValueError(f"Column {self.audio_column} is not present in features."				)
						if not isinstance(features[self.audio_column]							,    __magic_name__				):
									raise ValueError(f"Column {self.audio_column} is not an Audio type."				)
						lowerCamelCase__ :      int								=		copy.deepcopy(self				)
						lowerCamelCase__ :      Tuple								=		self.input_schema.copy()
						lowerCamelCase__ :      Tuple								=		features[self.audio_column]
						lowerCamelCase__ :      List[str]								=		input_schema
						return task_template





			@property
			def        _snake_case    (self				):
						return {self.audio_column: "audio", self.transcription_column: "transcription"}



