
"""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 __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):

     '''simple docstring'''





     lowercase		     =		["image_processor", "tokenizer"]
     lowercase		     =		"OwlViTImageProcessor"
     lowercase		     =		("CLIPTokenizer", "CLIPTokenizerFast")





     def __init__(			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      __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.'			,  __UpperCAmelCase			,  )
       __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__(__UpperCAmelCase			,  __UpperCAmelCase   )





     def __call__(			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase="max_length"			,  __UpperCAmelCase="np"			,  **__UpperCAmelCase   ):

      '''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(__UpperCAmelCase			,  __UpperCAmelCase   ) or (isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ) and not isinstance(text[0]			,  __UpperCAmelCase   )):
        __UpperCamelCase       						= [self.tokenizer(__UpperCAmelCase			,  padding=__UpperCAmelCase			,  return_tensors=__UpperCAmelCase			,  **__UpperCAmelCase   )]

       elif isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ) and isinstance(text[0]			,  __UpperCAmelCase   ):
        __UpperCamelCase       						= []

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

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

         __UpperCamelCase       						= self.tokenizer(__UpperCAmelCase			,  padding=__UpperCAmelCase			,  return_tensors=__UpperCAmelCase			,  **__UpperCAmelCase   )
         encodings.append(__UpperCAmelCase   )
       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(
           __UpperCAmelCase			,  return_tensors=__UpperCAmelCase			,  **__UpperCAmelCase   ).pixel_values
       __UpperCamelCase       						= query_pixel_values

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

      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(**__UpperCAmelCase   )			,  tensor_type=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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







     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





"""simple docstring"""

def 							A		(       snake_case   :int	)   ->			bool:
 return str(snake_case	) == str(snake_case	)[::-1]






def 							A		(       snake_case   :int	)   ->			int:
 return int(snake_case	) + int(str(snake_case	)[::-1]	)






def 							A		(       snake_case   :int = 1_0_0_0_0	)   ->			int:
 __UpperCamelCase       						= []
 for num in range(1       ,      snake_case	):
  __UpperCamelCase       						= 0
  __UpperCamelCase       						= num
  while iterations < 5_0:
   __UpperCamelCase       						= sum_reverse(snake_case	)
   iterations += 1
   if is_palindrome(snake_case	):
    break
  else:
   lychrel_nums.append(snake_case	)
 return len(snake_case	)


if __name__ == "__main__":
      print(f'''{solution() = }''')



