





'''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 (   lowercase_			):
   __UpperCamelCase   : str	      =	['''image_processor''', '''tokenizer''']
   __UpperCamelCase   : Optional[Any]	      =	'''LayoutLMv3ImageProcessor'''
   __UpperCamelCase   : Tuple	      =	('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')





   def __init__(					self				:			List[str]				,lowerCamelCase				:			Tuple=None				,lowerCamelCase				:			Union[str, Any]=None				,**lowerCamelCase				:			str    ):

      '''simple docstring'''

      __SCREAMING_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."""				,lowerCamelCase				,)
         __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__(lowerCamelCase				,lowerCamelCase    )





   def __call__(					self				:			Optional[Any]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			List[str] = None				,lowerCamelCase				:			str = None				,lowerCamelCase				:			Optional[Any] = None				,lowerCamelCase				:			str = None				,lowerCamelCase				:			Union[str, Any] = True				,lowerCamelCase				:			str = False				,lowerCamelCase				:			Tuple = None				,lowerCamelCase				:			Dict = None				,lowerCamelCase				:			Dict = 0				,lowerCamelCase				:			Tuple = None				,lowerCamelCase				:			Dict = None				,lowerCamelCase				:			Tuple = None				,lowerCamelCase				:			Union[str, Any] = False				,lowerCamelCase				:			Tuple = False				,lowerCamelCase				:			int = False				,lowerCamelCase				:			Any = False				,lowerCamelCase				:			List[Any] = True				,lowerCamelCase				:			Union[str, Any] = None				,**lowerCamelCase				:			List[Any]				,):

      '''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."""    )

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

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
         if isinstance(lowerCamelCase				,lowerCamelCase    ):
            __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=lowerCamelCase				,add_special_tokens=lowerCamelCase				,padding=lowerCamelCase				,truncation=lowerCamelCase				,max_length=lowerCamelCase				,stride=lowerCamelCase				,pad_to_multiple_of=lowerCamelCase				,return_token_type_ids=lowerCamelCase				,return_attention_mask=lowerCamelCase				,return_overflowing_tokens=lowerCamelCase				,return_special_tokens_mask=lowerCamelCase				,return_offsets_mapping=lowerCamelCase				,return_length=lowerCamelCase				,verbose=lowerCamelCase				,return_tensors=lowerCamelCase				,**lowerCamelCase				,)

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

      return encoded_inputs





   def      UpperCAmelCase__     (					self				:			Union[str, Any]				,lowerCamelCase				:			Optional[Any]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

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

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

      return images_with_overflow





   def      UpperCAmelCase__     (					self				:			Optional[int]				,*lowerCamelCase				:			Tuple				,**lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      return self.tokenizer.batch_decode(*lowerCamelCase				,**lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Optional[int]				,*lowerCamelCase				:			Tuple				,**lowerCamelCase				:			Union[str, Any]    ):

      '''simple docstring'''

      return self.tokenizer.decode(*lowerCamelCase				,**lowerCamelCase    )





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

      '''simple docstring'''

      return ["input_ids", "bbox", "attention_mask", "pixel_values"]





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

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





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

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












'''simple docstring'''

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   def wrapper(*__UpperCAmelCase							,	**__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer()
      __SCREAMING_SNAKE_CASE      							=       func(*__UpperCAmelCase							,	**__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer() - starttime
      return delta

   __SCREAMING_SNAKE_CASE      							=       func.__name__

   return wrapper

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []
   __SCREAMING_SNAKE_CASE      							=       seq_shapes or {}
   for i in range(__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       {}
      for col_id, (k, v) in enumerate(features.items()      ):
         if isinstance(__UpperCAmelCase							,	_ArrayXD      ):
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*v.shape      ).astype(v.dtype      )
         elif isinstance(__UpperCAmelCase							,	datasets.Value      ):
            if v.dtype == "string":
               __SCREAMING_SNAKE_CASE      							=       """The small grey turtle was surprisingly fast when challenged."""
            else:
               __SCREAMING_SNAKE_CASE      							=       np.random.randint(10							,	size=1      ).astype(v.dtype      ).item()
         elif isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
            while isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
               __SCREAMING_SNAKE_CASE      							=       v.feature
            __SCREAMING_SNAKE_CASE      							=       seq_shapes[k]
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*__UpperCAmelCase      ).astype(v.dtype      )
         __SCREAMING_SNAKE_CASE      							=       data

      dummy_data.append((i, example)      )

   return dummy_data







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       generate_examples(__UpperCAmelCase							,	num_examples=__UpperCAmelCase							,	seq_shapes=__UpperCAmelCase      )

   with ArrowWriter(features=__UpperCAmelCase							,	path=__UpperCAmelCase      ) as writer:
      for key, record in dummy_data:
         __SCREAMING_SNAKE_CASE      							=       features.encode_example(__UpperCAmelCase      )
         writer.write(__UpperCAmelCase      )

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       writer.finalize()

   if not num_final_examples == num_examples:
      raise ValueError(
          f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""      )

   __SCREAMING_SNAKE_CASE      							=       datasets.Dataset.from_file(filename=__UpperCAmelCase							,	info=datasets.DatasetInfo(features=__UpperCAmelCase      )      )

   return dataset





