


'''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_		(				lowercase_ ):

 '''simple docstring'''

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



 def __init__(			self	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   **_UpperCAmelCase      ):
       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.'''	,   _UpperCAmelCase	,   )
             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__(_UpperCAmelCase	,   _UpperCAmelCase      )



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

       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      )):
                   snake_case_ =   [self.tokenizer(_UpperCAmelCase	,   padding=_UpperCAmelCase	,   return_tensors=_UpperCAmelCase	,   **_UpperCAmelCase      )]

             elif isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ) and isinstance(text[0]	,   _UpperCAmelCase      ):
                   snake_case_ =   []

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

                         snake_case_ =   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":
                   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(
                 _UpperCAmelCase	,   return_tensors=_UpperCAmelCase	,   **_UpperCAmelCase      ).pixel_values
             snake_case_ =   query_pixel_values

       if images is not None:
             snake_case_ =   self.image_processor(_UpperCAmelCase	,   return_tensors=_UpperCAmelCase	,   **_UpperCAmelCase      )

       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(**_UpperCAmelCase      )	,   tensor_type=_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
       return self.image_processor.post_process(*_UpperCAmelCase	,   **_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
       return self.image_processor.post_process_object_detection(*_UpperCAmelCase	,   **_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
       return self.image_processor.post_process_image_guided_detection(*_UpperCAmelCase	,   **_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
       return self.tokenizer.batch_decode(*_UpperCAmelCase	,   **_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
       return self.tokenizer.decode(*_UpperCAmelCase	,   **_UpperCAmelCase      )



 @property
 def 		UpperCamelCase__  (			self      ):
       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      ):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,   _UpperCAmelCase	,   )
       return self.image_processor



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 3      ,      SCREAMING_SNAKE_CASE = 7      ,      SCREAMING_SNAKE_CASE = 100_0000      )->						int:




      """simple docstring"""

      snake_case_ =   0
      snake_case_ =   1

      for current_denominator in range(1      ,      limit + 1      ):
            snake_case_ =   current_denominator * numerator // denominator
            if current_denominator % denominator == 0:
                  current_numerator -= 1
            if current_numerator * max_denominator > current_denominator * max_numerator:
                  snake_case_ =   current_numerator
                  snake_case_ =   current_denominator
      return max_numerator


if __name__ == "__main__":
   print(solution(numerator=3, denominator=7, limit=100_0000))