



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    (						SCREAMING_SNAKE_CASE__		):



 '''simple docstring'''







 __lowerCamelCase					: Tuple    = ['image_processor', 'tokenizer']
 __lowerCamelCase					: Tuple    = 'Pix2StructImageProcessor'
 __lowerCamelCase					: List[str]    = ('T5Tokenizer', 'T5TokenizerFast')



 def __init__(self				,     A				,     A )					->  Optional[int]:


      """simple docstring"""



      _a											=						False
      super().__init__(_lowercase				,     _lowercase )



 def __call__(self				,     A=None				,     A = None				,     A = True				,     A = False				,     A = None				,     A = None				,     A = 2_048				,     A = 0				,     A = None				,     A = None				,     A = False				,     A = False				,     A = False				,     A = False				,     A = False				,     A = True				,     A = None				,     **A				,     )					->  BatchEncoding:


      """simple docstring"""



      if images is None and text is None:
           raise ValueError('''You have to specify either images or text.''' )

      # Get only text
      if images is None and not self.image_processor.is_vqa:
           _a											=						self.tokenizer
           _a											=						self.tokenizer(
               text=_lowercase				,     add_special_tokens=_lowercase				,     padding=_lowercase				,     truncation=_lowercase				,     max_length=_lowercase				,     stride=_lowercase				,     pad_to_multiple_of=_lowercase				,     return_attention_mask=_lowercase				,     return_overflowing_tokens=_lowercase				,     return_special_tokens_mask=_lowercase				,     return_offsets_mapping=_lowercase				,     return_token_type_ids=_lowercase				,     return_length=_lowercase				,     verbose=_lowercase				,     return_tensors=_lowercase				,     **_lowercase				,     )
           return text_encoding

      if not self.image_processor.is_vqa:
           # add pixel_values
           _a											=						self.image_processor(
               _lowercase				,     return_tensors=_lowercase				,     max_patches=_lowercase				,     **_lowercase )
      else:
           # add pixel_values and bbox
           _a											=						self.image_processor(
               _lowercase				,     return_tensors=_lowercase				,     max_patches=_lowercase				,     header_text=_lowercase				,     **_lowercase )

      if text is not None and not self.image_processor.is_vqa:
           _a											=						self.tokenizer(
               text=_lowercase				,     add_special_tokens=_lowercase				,     padding=_lowercase				,     truncation=_lowercase				,     max_length=_lowercase				,     stride=_lowercase				,     pad_to_multiple_of=_lowercase				,     return_attention_mask=_lowercase				,     return_overflowing_tokens=_lowercase				,     return_special_tokens_mask=_lowercase				,     return_offsets_mapping=_lowercase				,     return_token_type_ids=_lowercase				,     return_length=_lowercase				,     verbose=_lowercase				,     return_tensors=_lowercase				,     **_lowercase				,     )

           if "attention_mask" in text_encoding:
                _a											=						text_encoding.pop('''attention_mask''' )
           if "input_ids" in text_encoding:
                _a											=						text_encoding.pop('''input_ids''' )
      else:
           _a											=						None

      if text_encoding is not None:
           encoding_image_processor.update(_lowercase )

      return encoding_image_processor



 def       a__						(self				,     *A				,     **A )					->  Tuple:


      """simple docstring"""



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



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


      """simple docstring"""



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



 @property
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						self.tokenizer.model_input_names
      _a											=						self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )








'''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()



