




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      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Dict							=					["image_processor", "tokenizer"]
   __SCREAMING_SNAKE_CASE		:							Union[str, Any]							=					"LayoutLMv2ImageProcessor"
   __SCREAMING_SNAKE_CASE		:							int							=					("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






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

     _lowercase: 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__(UpperCamelCase_						,					UpperCamelCase_     )






   def __call__(	self   :       str						,					UpperCamelCase_   :       Optional[int]						,					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] = False						,					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_   :       int						,					)->			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
     _lowercase: Dict						     =    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_     ):
         _lowercase: Tuple						     =    [text]  # add batch dimension (as the image processor always adds a batch dimension)
       _lowercase: Dict						     =    features['words']

     _lowercase: Dict						     =    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
     _lowercase: List[Any]						     =    features.pop('pixel_values'     )
     if return_overflowing_tokens is True:
       _lowercase: Union[str, Any]						     =    self.get_overflowing_images(UpperCamelCase_						,					encoded_inputs['overflow_to_sample_mapping']     )
     _lowercase: Optional[Any]						     =    images

     return encoded_inputs






   def  _a						(	self   :       str						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       Dict     )->			Union[str, Any]:
     # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
     _lowercase: Tuple						     =    []
     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  _a						(	self   :       Any						,					*UpperCamelCase_   :       Optional[int]						,					**UpperCamelCase_   :       Optional[int]     )->			int:
     return self.tokenizer.batch_decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   def  _a						(	self   :       Optional[int]						,					*UpperCamelCase_   :       int						,					**UpperCamelCase_   :       Dict     )->			str:
     return self.tokenizer.decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   @property
   def  _a						(	self   :       Optional[int]     )->			int:
     return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






   @property
   def  _a						(	self   :       Tuple     )->			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  _a						(	self   :       List[str]     )->			List[Any]:
     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,					UpperCamelCase_						,					)
     return self.image_processor






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"AutoFeatureExtractor"
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"Speech2Text2Tokenizer"






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       List[Any]     )->			Dict:
     super().__init__(UpperCamelCase_						,					UpperCamelCase_     )
     _lowercase: Optional[int]						     =    self.feature_extractor
     _lowercase: int						     =    False






   def __call__(	self   :       List[Any]						,					*UpperCamelCase_   :       Dict						,					**UpperCamelCase_   :       Optional[int]     )->			str:
     # For backward compatibility
     if self._in_target_context_manager:
       return self.current_processor(*UpperCamelCase_						,					**UpperCamelCase_     )

     if "raw_speech" in kwargs:
       warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'     )
       _lowercase: Tuple						     =    kwargs.pop('raw_speech'     )
     else:
       _lowercase: Optional[Any]						     =    kwargs.pop('audio'						,					UpperCamelCase_     )
     _lowercase: int						     =    kwargs.pop('sampling_rate'						,					UpperCamelCase_     )
     _lowercase: Dict						     =    kwargs.pop('text'						,					UpperCamelCase_     )
     if len(UpperCamelCase_     ) > 0:
       _lowercase: List[Any]						     =    args[0]
       _lowercase: List[Any]						     =    args[1:]

     if audio is None and text is None:
       raise ValueError('You need to specify either an `audio` or `text` input to process.'     )

     if audio is not None:
       _lowercase: Optional[int]						     =    self.feature_extractor(UpperCamelCase_						,					*UpperCamelCase_						,					sampling_rate=UpperCamelCase_						,					**UpperCamelCase_     )
     if text is not None:
       _lowercase: str						     =    self.tokenizer(UpperCamelCase_						,					**UpperCamelCase_     )

     if text is None:
       return inputs
     elif audio is None:
       return encodings
     else:
       _lowercase: int						     =    encodings['input_ids']
       return inputs






   def  _a						(	self   :       Optional[int]						,					*UpperCamelCase_   :       Tuple						,					**UpperCamelCase_   :       Dict     )->			List[str]:
     return self.tokenizer.batch_decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   def  _a						(	self   :       Tuple						,					*UpperCamelCase_   :       List[Any]						,					**UpperCamelCase_   :       Union[str, Any]     )->			Optional[int]:
     return self.tokenizer.decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   @contextmanager
   def  _a						(	self   :       Tuple     )->			Union[str, Any]:
     warnings.warn(
         '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
         'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
         'your audio inputs, or in a separate call.'     )
     _lowercase: Any						     =    True
     _lowercase: Optional[Any]						     =    self.tokenizer
     yield
     _lowercase: Union[str, Any]						     =    self.feature_extractor
     _lowercase: str						     =    False
