



'''simple docstring'''





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 __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Dict		=			['image_processor', 'tokenizer']
 __lowerCamelCase       :			Union[str, Any]		=			'OwlViTImageProcessor'
 __lowerCamelCase       :			Optional[int]		=			('CLIPTokenizer', 'CLIPTokenizerFast')



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


      """simple docstring"""



      _a           =    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					,  )
           _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__(A					,  A			)



 def __call__(self					,  A=None					,  A=None					,  A=None					,  A="max_length"					,  A="np"					,  **A			)       ->	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			)):
                _a           =    [self.tokenizer(A					,  padding=A					,  return_tensors=A					,  **A			)]

           elif isinstance(A					,  A			) and isinstance(text[0]					,  A			):
                _a           =    []

                # Maximum number of queries across batch
                _a           =    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:
                          _a           =    t + [''' '''] * (max_num_queries - len(A			))

                     _a           =    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":
                _a           =    np.concatenate([encoding['''input_ids'''] for encoding in encodings]					,  axis=0			)
                _a           =    np.concatenate([encoding['''attention_mask'''] for encoding in encodings]					,  axis=0			)

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

                _a           =    jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]					,  axis=0			)
                _a           =    jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]					,  axis=0			)

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

                _a           =    torch.cat([encoding['''input_ids'''] for encoding in encodings]					,  dim=0			)
                _a           =    torch.cat([encoding['''attention_mask'''] for encoding in encodings]					,  dim=0			)

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

                _a           =    tf.stack([encoding['''input_ids'''] for encoding in encodings]					,  axis=0			)
                _a           =    tf.stack([encoding['''attention_mask'''] for encoding in encodings]					,  axis=0			)

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

           _a           =    BatchEncoding()
           _a           =    input_ids
           _a           =    attention_mask

      if query_images is not None:
           _a           =    BatchEncoding()
           _a           =    self.image_processor(
               A					,  return_tensors=A					,  **A			).pixel_values
           _a           =    query_pixel_values

      if images is not None:
           _a           =    self.image_processor(A					,  return_tensors=A					,  **A			)

      if text is not None and images is not None:
           _a           =    image_features.pixel_values
           return encoding
      elif query_images is not None and images is not None:
           _a           =    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    a__    (self					,  *A					,  **A			)       ->	Any:


      """simple docstring"""



      return self.image_processor.post_process(*A					,  **A			)



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


      """simple docstring"""



      return self.image_processor.post_process_object_detection(*A					,  **A			)



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


      """simple docstring"""



      return self.image_processor.post_process_image_guided_detection(*A					,  **A			)



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


      """simple docstring"""



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



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


      """simple docstring"""



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



 @property
 def    a__    (self			)       ->	Dict:


      """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    a__    (self			)       ->	List[Any]:


      """simple docstring"""



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







'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A = 1_000			)			->			int:






       """simple docstring"""







       return sum(2 * a * ((a - 1) // 2) for a in range(3    ,					n + 1			)			)


if __name__ == "__main__":
 print(solution())




