



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    (						_snake_case		):



 '''simple docstring'''







 __lowerCamelCase					: Union[str, Any]    = ["""image_processor""", """tokenizer"""]
 __lowerCamelCase					: Dict    = """LayoutLMv2ImageProcessor"""
 __lowerCamelCase					: List[Any]    = ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



 def __init__(self				,     A=None				,     A=None				,     **A )					->  Tuple:


      """simple docstring"""



      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_				,     )
           _a											=						kwargs.pop('''feature_extractor''' )

      _a											=						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				,     A				,     A = None				,     A = None				,     A = None				,     A = None				,     A = True				,     A = False				,     A = None				,     A = None				,     A = 0				,     A = None				,     A = None				,     A = None				,     A = False				,     A = False				,     A = False				,     A = False				,     A = True				,     A = None				,     **A				,     )					->  str:


      """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.''' )

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

      _a											=						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
      _a											=						features.pop('''pixel_values''' )
      if return_overflowing_tokens is True:
           _a											=						self.get_overflowing_images(snake_case_				,     encoded_inputs['''overflow_to_sample_mapping'''] )
      _a											=						images

      return encoded_inputs



 def       a__						(self				,     A				,     A )					->  int:


      """simple docstring"""



      _a											=						[]
      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       a__						(self				,     *A				,     **A )					->  Optional[Any]:


      """simple docstring"""



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



 def       a__						(self				,     *A				,     **A )					->  Optional[Any]:


      """simple docstring"""



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



 @property
 def       a__						(self )					->  Any:


      """simple docstring"""



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



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      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       a__						(self )					->  int:


      """simple docstring"""



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








'''simple docstring'''





from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

lowercase_	 =						typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
lowercase_	 =						typing.Union[np.floataa, int, float]  # noqa: UP007




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     return np.sqrt(np.sum((np.asarray(__A) - np.asarray(__A)) ** 2))




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     return sum((va - va) ** 2 for va, va in zip(__A						,			__A)) ** (1 / 2)


if __name__ == "__main__":




  def lowerCAmelCase						():



       """simple docstring"""



       from timeit import timeit

       print('''Without Numpy''')
       print(
           timeit(
               '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''						,			number=10_000						,			globals=globals()						,			))
       print('''With Numpy''')
       print(
           timeit(
               '''euclidean_distance([1, 2, 3], [4, 5, 6])'''						,			number=10_000						,			globals=globals()						,			))
  benchmark()



