





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  _lowerCamelCase       (			lowercase__			):



      """simple docstring"""






      UpperCAmelCase_  :					Any      			=["image_processor", "tokenizer"]
      UpperCAmelCase_  :					Optional[int]      			="OwlViTImageProcessor"
      UpperCAmelCase_  :					int      			=("CLIPTokenizer", "CLIPTokenizerFast")







      def __init__(       self ,  UpperCAmelCase=None ,  UpperCAmelCase=None ,  **UpperCAmelCase      )				->      Union[str, Any]:






        '''simple docstring'''






        __snake_case      : Tuple						=				None
        if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead." ,  __lowerCamelCase ,  )
          __snake_case      : List[str]						=				kwargs.pop("feature_extractor"      )

        __snake_case      : Tuple						=				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__(__lowerCamelCase ,  __lowerCamelCase      )







      def __call__(       self ,  UpperCAmelCase=None ,  UpperCAmelCase=None ,  UpperCAmelCase=None ,  UpperCAmelCase="max_length" ,  UpperCAmelCase="np" ,  **UpperCAmelCase      )				->      Optional[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(__lowerCamelCase ,  __lowerCamelCase      ) or (isinstance(__lowerCamelCase ,  __lowerCamelCase      ) and not isinstance(text[0] ,  __lowerCamelCase      )):
            __snake_case      : int						=				[self.tokenizer(__lowerCamelCase ,  padding=__lowerCamelCase ,  return_tensors=__lowerCamelCase ,  **__lowerCamelCase      )]

          elif isinstance(__lowerCamelCase ,  __lowerCamelCase      ) and isinstance(text[0] ,  __lowerCamelCase      ):
            __snake_case      : Any						=				[]

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

            # Pad all batch samples to max number of text queries
            for t in text:
              if len(__lowerCamelCase      ) != max_num_queries:
                __snake_case      : List[str]						=				t + [" "] * (max_num_queries - len(__lowerCamelCase      ))

              __snake_case      : Optional[int]						=				self.tokenizer(__lowerCamelCase ,  padding=__lowerCamelCase ,  return_tensors=__lowerCamelCase ,  **__lowerCamelCase      )
              encodings.append(__lowerCamelCase      )
          else:
            raise TypeError("Input text should be a string, a list of strings or a nested list of strings"      )

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

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

            __snake_case      : List[Any]						=				jnp.concatenate([encoding["input_ids"] for encoding in encodings] ,  axis=0      )
            __snake_case      : str						=				jnp.concatenate([encoding["attention_mask"] for encoding in encodings] ,  axis=0      )

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

            __snake_case      : List[str]						=				torch.cat([encoding["input_ids"] for encoding in encodings] ,  dim=0      )
            __snake_case      : Tuple						=				torch.cat([encoding["attention_mask"] for encoding in encodings] ,  dim=0      )

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

            __snake_case      : Union[str, Any]						=				tf.stack([encoding["input_ids"] for encoding in encodings] ,  axis=0      )
            __snake_case      : List[str]						=				tf.stack([encoding["attention_mask"] for encoding in encodings] ,  axis=0      )

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

          __snake_case      : List[Any]						=				BatchEncoding()
          __snake_case      : List[str]						=				input_ids
          __snake_case      : Any						=				attention_mask

        if query_images is not None:
          __snake_case      : List[str]						=				BatchEncoding()
          __snake_case      : Dict						=				self.image_processor(
              __lowerCamelCase ,  return_tensors=__lowerCamelCase ,  **__lowerCamelCase      ).pixel_values
          __snake_case      : List[str]						=				query_pixel_values

        if images is not None:
          __snake_case      : List[Any]						=				self.image_processor(__lowerCamelCase ,  return_tensors=__lowerCamelCase ,  **__lowerCamelCase      )

        if text is not None and images is not None:
          __snake_case      : List[Any]						=				image_features.pixel_values
          return encoding
        elif query_images is not None and images is not None:
          __snake_case      : Tuple						=				image_features.pixel_values
          return encoding
        elif text is not None or query_images is not None:
          return encoding
        else:
          return BatchEncoding(data=dict(**__lowerCamelCase      ) ,  tensor_type=__lowerCamelCase      )







      def   UpperCAmelCase						(       self ,  *UpperCAmelCase ,  **UpperCAmelCase      )				->      Any:






        '''simple docstring'''






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







      def   UpperCAmelCase						(       self ,  *UpperCAmelCase ,  **UpperCAmelCase      )				->      List[str]:






        '''simple docstring'''






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







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






        '''simple docstring'''






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







      def   UpperCAmelCase						(       self ,  *UpperCAmelCase ,  **UpperCAmelCase      )				->      str:






        '''simple docstring'''






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







      def   UpperCAmelCase						(       self ,  *UpperCAmelCase ,  **UpperCAmelCase      )				->      Dict:






        '''simple docstring'''






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







      @property
      def   UpperCAmelCase						(       self      )				->      Dict:






        '''simple docstring'''






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







      @property
      def   UpperCAmelCase						(       self      )				->      Optional[int]:






        '''simple docstring'''






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





def       lowercase_		(		_A			:    int    ,					_A			:    int						):

      """simple docstring"""




      while a != 0:
            lowerCamelCase__    ,   lowerCamelCase__		:				Optional[Any]    =  b % a, a
      return b


def       lowercase_		(		_A			:    int    ,					_A			:    int						):

      """simple docstring"""




      if gcd(_A    ,					_A						) != 1:
            lowerCamelCase__		:				List[str]    =  F"mod inverse of {a!r} and {m!r} does not exist"
            raise ValueError(_A						)
      lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  1, 0, a
      lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  0, 1, m
      while va != 0:
            lowerCamelCase__		:				Tuple    =  ua // va
            lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				str    =  (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
      return ua % m


