




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    :	Union[str, Any]       		=  ['image_processor', 'tokenizer']
     lowerCAmelCase    :	Optional[Any]       		=  'OwlViTImageProcessor'
     lowerCAmelCase    :	List[str]       		=  ('CLIPTokenizer', 'CLIPTokenizerFast')
     def __init__(     self       :      List[str] ,   __snake_case       :      List[str]=None ,   __snake_case       :      int=None ,   **__snake_case       :      Tuple    ):
         UpperCAmelCase_										=					None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.''' ,   lowercase_ ,   )
             UpperCAmelCase_										=					kwargs.pop('''feature_extractor'''    )

         UpperCAmelCase_										=					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__(lowercase_ ,   lowercase_    )
     def __call__(     self       :      Union[str, Any] ,   __snake_case       :      Dict=None ,   __snake_case       :      Tuple=None ,   __snake_case       :      str=None ,   __snake_case       :      Optional[int]="max_length" ,   __snake_case       :      Union[str, Any]="np" ,   **__snake_case       :      List[Any]    ):







         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(lowercase_ ,   lowercase_    ) or (isinstance(lowercase_ ,   lowercase_    ) and not isinstance(text[0] ,   lowercase_    )):
                 UpperCAmelCase_										=					[self.tokenizer(lowercase_ ,   padding=lowercase_ ,   return_tensors=lowercase_ ,   **lowercase_    )]

             elif isinstance(lowercase_ ,   lowercase_    ) and isinstance(text[0] ,   lowercase_    ):
                 UpperCAmelCase_										=					[]

                 # Maximum number of queries across batch
                 UpperCAmelCase_										=					max([len(lowercase_    ) for t in text]    )

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

                     UpperCAmelCase_										=					self.tokenizer(lowercase_ ,   padding=lowercase_ ,   return_tensors=lowercase_ ,   **lowercase_    )
                     encodings.append(lowercase_    )
             else:
                 raise TypeError('''Input text should be a string, a list of strings or a nested list of strings'''    )

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

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

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

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

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

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

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

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

             UpperCAmelCase_										=					BatchEncoding()
             UpperCAmelCase_										=					input_ids
             UpperCAmelCase_										=					attention_mask

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

         if images is not None:
             UpperCAmelCase_										=					self.image_processor(lowercase_ ,   return_tensors=lowercase_ ,   **lowercase_    )

         if text is not None and images is not None:
             UpperCAmelCase_										=					image_features.pixel_values
             return encoding
         elif query_images is not None and images is not None:
             UpperCAmelCase_										=					image_features.pixel_values
             return encoding
         elif text is not None or query_images is not None:
             return encoding
         else:
             return BatchEncoding(data=dict(**lowercase_    ) ,   tensor_type=lowercase_    )
     def   lowerCamelCase_       (     self       :      List[Any] ,   *__snake_case       :      str ,   **__snake_case       :      int    ):
         return self.image_processor.post_process(*lowercase_ ,   **lowercase_    )
     def   lowerCamelCase_       (     self       :      Dict ,   *__snake_case       :      str ,   **__snake_case       :      List[str]    ):
         return self.image_processor.post_process_object_detection(*lowercase_ ,   **lowercase_    )
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   *__snake_case       :      Dict ,   **__snake_case       :      Dict    ):
         return self.image_processor.post_process_image_guided_detection(*lowercase_ ,   **lowercase_    )
     def   lowerCamelCase_       (     self       :      str ,   *__snake_case       :      int ,   **__snake_case       :      List[str]    ):
         return self.tokenizer.batch_decode(*lowercase_ ,   **lowercase_    )
     def   lowerCamelCase_       (     self       :      List[str] ,   *__snake_case       :      Optional[int] ,   **__snake_case       :      Optional[int]    ):
         return self.tokenizer.decode(*lowercase_ ,   **lowercase_    )
     @property
     def   lowerCamelCase_       (     self       :      Dict    ):
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,   lowercase_ ,   )
         return self.image_processor_class




     @property
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,   lowercase_ ,   )
         return self.image_processor











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						float	,					__UpperCamelCase      :						float				)  ->							float:

    if density <= 0:
        raise ValueError('''Impossible fluid density'''				)
    if bulk_modulus <= 0:
        raise ValueError('''Impossible bulk modulus'''				)

    return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()





