





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        SCREAMING_SNAKE_CASE     (__a   ):
   _UpperCamelCase					:	Any				      = ["image_processor", "tokenizer"]
   _UpperCamelCase					:	Optional[Any]				      = "OwlViTImageProcessor"
   _UpperCamelCase					:	Optional[Any]				      = ("CLIPTokenizer", "CLIPTokenizerFast")


   def __init__(					self    :       Union[str, Any]    ,  a    :       Optional[int]=None    ,  a    :       Dict=None    ,  **a    :       List[str] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						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_    ,  )
       lowercase__			    =						kwargs.pop('feature_extractor' )

     lowercase__			    =						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    :       Dict    ,  a    :       int=None    ,  a    :       Optional[int]=None    ,  a    :       str=None    ,  a    :       Optional[Any]="max_length"    ,  a    :       Union[str, Any]="np"    ,  **a    :       Any )->     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_ )):
         lowercase__			    =						[self.tokenizer(lowerCAmelCase_    ,  padding=lowerCAmelCase_    ,  return_tensors=lowerCAmelCase_    ,  **lowerCAmelCase_ )]

       elif isinstance(lowerCAmelCase_    ,  lowerCAmelCase_ ) and isinstance(text[0]    ,  lowerCAmelCase_ ):
         lowercase__			    =						[]

         # Maximum number of queries across batch
         lowercase__			    =						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:
             lowercase__			    =						t + [' '] * (max_num_queries - len(lowerCAmelCase_ ))

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

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

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

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

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

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

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

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

       lowercase__			    =						BatchEncoding()
       lowercase__			    =						input_ids
       lowercase__			    =						attention_mask

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

     if images is not None:
       lowercase__			    =						self.image_processor(lowerCAmelCase_    ,  return_tensors=lowerCAmelCase_    ,  **lowerCAmelCase_ )

     if text is not None and images is not None:
       lowercase__			    =						image_features.pixel_values
       return encoding
     elif query_images is not None and images is not None:
       lowercase__			    =						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  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  *a    :       Optional[int]    ,  **a    :       Any )->     int:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  *a    :       Any    ,  **a    :       Dict )->     int:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  *a    :       Any    ,  **a    :       str )->     Dict:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  *a    :       Union[str, Any]    ,  **a    :       Optional[Any] )->     Optional[int]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int]    ,  *a    :       str    ,  **a    :       int )->     Tuple:





     """simple docstring"""







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


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     Union[str, Any]:





     """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  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     List[Any]:





     """simple docstring"""







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









def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE = 50	)       ->     int:
  lowercase__			    =						[1] * (length + 1)

  for row_length in range(3	,		length + 1	):
    for block_length in range(3	,		row_length + 1	):
      for block_start in range(row_length - block_length	):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


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


