





'''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 			_lowercase						(       __snake_case					):
     _UpperCAmelCase										=					['''image_processor''', '''tokenizer''']
     _UpperCAmelCase										=					'''OwlViTImageProcessor'''
     _UpperCAmelCase										=					('''CLIPTokenizer''', '''CLIPTokenizerFast''')







     def __init__( self    ,   A__=None    ,   A__=None    ,   **A__    ) ->	Tuple:
      snake_case				=			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    ,   )
       snake_case				=			kwargs.pop('''feature_extractor'''    )

      snake_case				=			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    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__="max_length"    ,   A__="np"    ,   **A__    ) ->	int:

      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    )):
        snake_case				=			[self.tokenizer(_lowercase    ,   padding=_lowercase    ,   return_tensors=_lowercase    ,   **_lowercase    )]

       elif isinstance(_lowercase    ,   _lowercase    ) and isinstance(text[0]    ,   _lowercase    ):
        snake_case				=			[]

        # Maximum number of queries across batch
        snake_case				=			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:
          snake_case				=			t + [""" """] * (max_num_queries - len(_lowercase    ))

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

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

        snake_case				=			torch.cat([encoding['''input_ids'''] for encoding in encodings]    ,   dim=0    )
        snake_case				=			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				=			tf.stack([encoding['''input_ids'''] for encoding in encodings]    ,   axis=0    )
        snake_case				=			tf.stack([encoding['''attention_mask'''] for encoding in encodings]    ,   axis=0    )

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

       snake_case				=			BatchEncoding()
       snake_case				=			input_ids
       snake_case				=			attention_mask

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

      if images is not None:
       snake_case				=			self.image_processor(_lowercase    ,   return_tensors=_lowercase    ,   **_lowercase    )

      if text is not None and images is not None:
       snake_case				=			image_features.pixel_values
       return encoding
      elif query_images is not None and images is not None:
       snake_case				=			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    ,   *A__    ,   **A__    ) ->	int:
      return self.image_processor.post_process(*_lowercase    ,   **_lowercase    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	int:
      return self.image_processor.post_process_object_detection(*_lowercase    ,   **_lowercase    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	Any:
      return self.image_processor.post_process_image_guided_detection(*_lowercase    ,   **_lowercase    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	Any:
      return self.tokenizer.batch_decode(*_lowercase    ,   **_lowercase    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	Union[str, Any]:
      return self.tokenizer.decode(*_lowercase    ,   **_lowercase    )







     @property
     def     UpperCamelCase			( self    ) ->	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    ) ->	Any:
      warnings.warn(
          '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,   _lowercase    ,   )
      return self.image_processor








'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			value
      snake_case				=			None
      snake_case				=			None




class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			tree







     def     UpperCamelCase			( self    ,   A__    ) ->	int:
      if node is None:
       return 0
      return node.value + (
          self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
      )







     def __iter__( self    ) ->	Iterator[int]:
      yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

