'''simple docstring'''





from __future__ import annotations







def     _a				(      lowercase__  :			list[int | float]     ,  lowercase__  :			int     ,  lowercase__  :			int ):







     '''simple docstring'''


     if len(lowercase__ ) == 0:
          raise ValueError('find_max() arg is an empty sequence' )
     if (
         left >= len(lowercase__ )
         or left < -len(lowercase__ )
         or right >= len(lowercase__ )
         or right < -len(lowercase__ )
     ):
          raise IndexError('list index out of range' )
     if left == right:
          return nums[left]
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(left + right) >> 1  # the middle
     SCREAMING_SNAKE_CASE__				:				int			=		find_max(lowercase__     ,  lowercase__     ,  lowercase__ )  # find max in range[left, mid]
     SCREAMING_SNAKE_CASE__				:				Tuple			=		find_max(lowercase__     ,  mid + 1     ,  lowercase__ )  # find max in range[mid + 1, right]

     return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)




import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     ['image_processor', 'tokenizer']
  lowercase_			=     'OwlViTImageProcessor'
  lowercase_			=     ('CLIPTokenizer', 'CLIPTokenizerFast')




  def __init__(      self     :	List[str]     , a_     :	List[Any]=None     , a_     :	str=None     , **a_     :	Any				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		None
       if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.'     , a_     , )
            SCREAMING_SNAKE_CASE__				:				Tuple			=		kwargs.pop('feature_extractor'				)

       SCREAMING_SNAKE_CASE__				:				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__(a_     , a_				)




  def __call__(      self     :	Any     , a_     :	Optional[int]=None     , a_     :	Tuple=None     , a_     :	List[Any]=None     , a_     :	Tuple="max_length"     , a_     :	str="np"     , **a_     :	Any				)->       int:




       """simple docstring"""

       if text is None and query_images is None and images is None:
            raise ValueError(
                'You have to specify at least one text or query image or image. All three cannot be none.'				)

       if text is not None:
            if isinstance(a_     , a_				) or (isinstance(a_     , a_				) and not isinstance(text[0]     , a_				)):
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		[self.tokenizer(a_     , padding=a_     , return_tensors=a_     , **a_				)]

            elif isinstance(a_     , a_				) and isinstance(text[0]     , a_				):
                 SCREAMING_SNAKE_CASE__				:				Any			=		[]

                 # Maximum number of queries across batch
                 SCREAMING_SNAKE_CASE__				:				str			=		max([len(a_				) for t in text]				)

                 # Pad all batch samples to max number of text queries
                 for t in text:
                      if len(a_				) != max_num_queries:
                           SCREAMING_SNAKE_CASE__				:				Tuple			=		t + [' '] * (max_num_queries - len(a_				))

                      SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.tokenizer(a_     , padding=a_     , return_tensors=a_     , **a_				)
                      encodings.append(a_				)
            else:
                 raise TypeError('Input text should be a string, a list of strings or a nested list of strings'				)

            if return_tensors == "np":
                 SCREAMING_SNAKE_CASE__				:				Dict			=		np.concatenate([encoding['input_ids'] for encoding in encodings]     , axis=0				)
                 SCREAMING_SNAKE_CASE__				:				List[Any]			=		np.concatenate([encoding['attention_mask'] for encoding in encodings]     , axis=0				)

            elif return_tensors == "jax" and is_flax_available():
                 import jax.numpy as jnp

                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		jnp.concatenate([encoding['input_ids'] for encoding in encodings]     , axis=0				)
                 SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		jnp.concatenate([encoding['attention_mask'] for encoding in encodings]     , axis=0				)

            elif return_tensors == "pt" and is_torch_available():
                 import torch

                 SCREAMING_SNAKE_CASE__				:				int			=		torch.cat([encoding['input_ids'] for encoding in encodings]     , dim=0				)
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.cat([encoding['attention_mask'] for encoding in encodings]     , dim=0				)

            elif return_tensors == "tf" and is_tf_available():
                 import tensorflow as tf

                 SCREAMING_SNAKE_CASE__				:				str			=		tf.stack([encoding['input_ids'] for encoding in encodings]     , axis=0				)
                 SCREAMING_SNAKE_CASE__				:				Dict			=		tf.stack([encoding['attention_mask'] for encoding in encodings]     , axis=0				)

            else:
                 raise ValueError('Target return tensor type could not be returned'				)

            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		BatchEncoding()
            SCREAMING_SNAKE_CASE__				:				List[str]			=		input_ids
            SCREAMING_SNAKE_CASE__				:				Tuple			=		attention_mask

       if query_images is not None:
            SCREAMING_SNAKE_CASE__				:				Any			=		BatchEncoding()
            SCREAMING_SNAKE_CASE__				:				Dict			=		self.image_processor(
                a_     , return_tensors=a_     , **a_				).pixel_values
            SCREAMING_SNAKE_CASE__				:				Dict			=		query_pixel_values

       if images is not None:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processor(a_     , return_tensors=a_     , **a_				)

       if text is not None and images is not None:
            SCREAMING_SNAKE_CASE__				:				Dict			=		image_features.pixel_values
            return encoding
       elif query_images is not None and images is not None:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_features.pixel_values
            return encoding
       elif text is not None or query_images is not None:
            return encoding
       else:
            return BatchEncoding(data=dict(**a_				)     , tensor_type=a_				)




  def 	__lowercase(      self     :	str     , *a_     :	List[str]     , **a_     :	int				)->       List[Any]:




       """simple docstring"""
       return self.image_processor.post_process(*a_     , **a_				)




  def 	__lowercase(      self     :	Tuple     , *a_     :	List[str]     , **a_     :	str				)->       Union[str, Any]:




       """simple docstring"""
       return self.image_processor.post_process_object_detection(*a_     , **a_				)




  def 	__lowercase(      self     :	Optional[Any]     , *a_     :	str     , **a_     :	Dict				)->       Optional[int]:




       """simple docstring"""
       return self.image_processor.post_process_image_guided_detection(*a_     , **a_				)




  def 	__lowercase(      self     :	Optional[int]     , *a_     :	Tuple     , **a_     :	Tuple				)->       Optional[Any]:




       """simple docstring"""
       return self.tokenizer.batch_decode(*a_     , **a_				)




  def 	__lowercase(      self     :	Tuple     , *a_     :	Tuple     , **a_     :	Tuple				)->       List[str]:




       """simple docstring"""
       return self.tokenizer.decode(*a_     , **a_				)




  @property
  def 	__lowercase(      self     :	Tuple				)->       Any:




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




  @property
  def 	__lowercase(      self     :	List[Any]				)->       List[str]:




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



