






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       (					lowercase__  ):



      """simple docstring"""


      a    :					Tuple							=      ['image_processor', 'tokenizer']
      a    :					List[str]							=      'OwlViTImageProcessor'
      a    :					str							=      ('CLIPTokenizer', 'CLIPTokenizerFast')
      def __init__( self   :     str						, __lowercase   :     Dict=None						, __lowercase   :     int=None						, **__lowercase   :     Any   )				-> Tuple:
        __UpperCAmelCase				:      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."""						, __lowercase						, )
          __UpperCAmelCase				:      List[str]	 =    kwargs.pop("""feature_extractor"""   )

        __UpperCAmelCase				:      Any	 =    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   :     int						, __lowercase   :     Optional[int]=None						, __lowercase   :     int=None						, __lowercase   :     Any=None						, __lowercase   :     Tuple="max_length"						, __lowercase   :     Any="np"						, **__lowercase   :     Optional[int]   )				-> Union[str, 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				:      Union[str, Any]	 =    [self.tokenizer(__lowercase						, padding=__lowercase						, return_tensors=__lowercase						, **__lowercase   )]

          elif isinstance(__lowercase						, __lowercase   ) and isinstance(text[0]						, __lowercase   ):
            __UpperCAmelCase				:      Optional[int]	 =    []

            # Maximum number of queries across batch
            __UpperCAmelCase				:      str	 =    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				:      Tuple	 =    t + [""" """] * (max_num_queries - len(__lowercase   ))

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

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

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

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

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

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

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

        if query_images is not None:
          __UpperCAmelCase				:      Dict	 =    BatchEncoding()
          __UpperCAmelCase				:      Dict	 =    self.image_processor(
              __lowercase						, return_tensors=__lowercase						, **__lowercase   ).pixel_values
          __UpperCAmelCase				:      List[Any]	 =    query_pixel_values

        if images is not None:
          __UpperCAmelCase				:      Tuple	 =    self.image_processor(__lowercase						, return_tensors=__lowercase						, **__lowercase   )

        if text is not None and images is not None:
          __UpperCAmelCase				:      Dict	 =    image_features.pixel_values
          return encoding
        elif query_images is not None and images is not None:
          __UpperCAmelCase				:      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(**__lowercase   )						, tensor_type=__lowercase   )
      def       UpperCAmelCase ( self   :     Any						, *__lowercase   :     str						, **__lowercase   :     Optional[int]   )				-> int:
        return self.image_processor.post_process(*__lowercase						, **__lowercase   )
      def       UpperCAmelCase ( self   :     Tuple						, *__lowercase   :     Optional[Any]						, **__lowercase   :     Optional[Any]   )				-> Dict:
        return self.image_processor.post_process_object_detection(*__lowercase						, **__lowercase   )
      def       UpperCAmelCase ( self   :     Union[str, Any]						, *__lowercase   :     int						, **__lowercase   :     Optional[Any]   )				-> List[str]:
        return self.image_processor.post_process_image_guided_detection(*__lowercase						, **__lowercase   )
      def       UpperCAmelCase ( self   :     Optional[int]						, *__lowercase   :     int						, **__lowercase   :     List[str]   )				-> Dict:
        return self.tokenizer.batch_decode(*__lowercase						, **__lowercase   )
      def       UpperCAmelCase ( self   :     List[str]						, *__lowercase   :     Dict						, **__lowercase   :     Union[str, Any]   )				-> Tuple:
        return self.tokenizer.decode(*__lowercase						, **__lowercase   )
      @property
      def       UpperCAmelCase ( self   :     List[str]   )				-> List[str]:
        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       UpperCAmelCase ( self   :     Tuple   )				-> List[Any]:
        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""						, __lowercase						, )
        return self.image_processor









from __future__ import annotations

_UpperCAmelCase							:			Tuple        =							"""#"""

class   lowerCAmelCase				:
 def __init__( self    : Optional[Any]      )     ->      None:
     lowerCamelCase__       : dict   					=						{}
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : str      )     ->      None:
     lowerCamelCase__       : int   					=						self._trie
     for char in text:
         if char not in trie:
             lowerCamelCase__       : Tuple   					=						{}
         lowerCamelCase__       : Dict   					=						trie[char]
     lowerCamelCase__       : Optional[int]   					=						True
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : str      )     ->      tuple | list:
     lowerCamelCase__       : Tuple   					=						self._trie
     for char in prefix:
         if char in trie:
             lowerCamelCase__       : List[Any]   					=						trie[char]
         else:
             return []
     return self._elements(UpperCAmelCase      )







 def 	A_      ( self    : List[str]     ,	UpperCAmelCase    : dict      )     ->      tuple:
     lowerCamelCase__       : Optional[Any]   					=						[]
     for c, v in d.items():
         lowerCamelCase__       : Optional[Any]   					=						[' '] if c == END else [(c + s) for s in self._elements(UpperCAmelCase      )]
         result.extend(UpperCAmelCase      )
     return tuple(UpperCAmelCase      )


_UpperCAmelCase							:			str        =							Trie()
_UpperCAmelCase							:			Tuple        =							("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
    trie.insert_word(word)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	tuple:
    lowerCamelCase__       : str   					=						trie.find_word(_UpperCAmelCase					)
    return tuple(string + word for word in suffixes					)


def 			SCREAMING_SNAKE_CASE (			)     ->	None:
    print(autocomplete_using_trie('de'					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





