




'''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   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       int       							=		["""image_processor""", """tokenizer"""]
     SCREAMING_SNAKE_CASE_				:       str       							=		"""LayoutLMv3ImageProcessor"""
     SCREAMING_SNAKE_CASE_				:       Dict       							=		("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,**_SCREAMING_SNAKE_CASE       )				->     List[Any]:
          _snake_case				= None
          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   ,)
               _snake_case				= kwargs.pop("feature_extractor"       )

          _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   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = True   ,_SCREAMING_SNAKE_CASE = False   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = 0   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = False   ,_SCREAMING_SNAKE_CASE = False   ,_SCREAMING_SNAKE_CASE = False   ,_SCREAMING_SNAKE_CASE = False   ,_SCREAMING_SNAKE_CASE = True   ,_SCREAMING_SNAKE_CASE = None   ,**_SCREAMING_SNAKE_CASE   ,)				->     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."       )

          # first, apply the image processor
          _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       ):
                    _snake_case				= [text]  # add batch dimension (as the image processor always adds a batch dimension)
               _snake_case				= features["words"]

          _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
          _snake_case				= features.pop("pixel_values"       )
          if return_overflowing_tokens is True:
               _snake_case				= self.get_overflowing_images(_SCREAMING_SNAKE_CASE   ,encoded_inputs["overflow_to_sample_mapping"]       )
          _snake_case				= images

          return encoded_inputs






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          _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 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     Tuple:
          return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     List[str]:
          return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     @property
     def 							_lowercase      (					self       )				->     List[str]:
          return ["input_ids", "bbox", "attention_mask", "pixel_values"]






     @property
     def 							_lowercase      (					self       )				->     List[str]:
          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 							_lowercase      (					self       )				->     List[str]:
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."   ,_SCREAMING_SNAKE_CASE   ,)
          return self.image_processor








'''simple docstring'''





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version





def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Optional[str] = None       )		->					str:

     """simple docstring"""




     if version.parse(hfh.__version__       ).release < version.parse("0.11.0"       ).release:
          # old versions of hfh don't url-encode the file path
          _snake_case				= quote(_UpperCamelCase       )
     return hfh.hf_hub_url(_UpperCamelCase   ,		_UpperCamelCase   ,		repo_type="dataset"   ,		revision=_UpperCamelCase       )


