


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 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		["""image_processor""", """tokenizer"""]
      UpperCAmelCase_        =		"""OwlViTImageProcessor"""
      UpperCAmelCase_        =		("""CLIPTokenizer""", """CLIPTokenizerFast""")
      def __init__(			self   :Optional[Any] ,      lowerCamelCase   :Dict=None ,      lowerCamelCase   :Any=None ,      **lowerCamelCase   :Optional[Any]      )     ->			int:
       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." ,      lowerCamelCase ,      )
        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__(lowerCamelCase ,      lowerCamelCase      )
      def __call__(			self   :Tuple ,      lowerCamelCase   :Any=None ,      lowerCamelCase   :int=None ,      lowerCamelCase   :List[Any]=None ,      lowerCamelCase   :Any="max_length" ,      lowerCamelCase   :str="np" ,      **lowerCamelCase   :Optional[Any]      )     ->			Dict:

       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      )):
         UpperCAmelCase__							 =						[self.tokenizer(lowerCamelCase ,      padding=lowerCamelCase ,      return_tensors=lowerCamelCase ,      **lowerCamelCase      )]

        elif isinstance(lowerCamelCase ,      lowerCamelCase      ) and isinstance(text[0] ,      lowerCamelCase      ):
         UpperCAmelCase__							 =						[]

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

          UpperCAmelCase__							 =						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":
         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(
            lowerCamelCase ,      return_tensors=lowerCamelCase ,      **lowerCamelCase      ).pixel_values
        UpperCAmelCase__							 =						query_pixel_values

       if images is not None:
        UpperCAmelCase__							 =						self.image_processor(lowerCamelCase ,      return_tensors=lowerCamelCase ,      **lowerCamelCase      )

       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(**lowerCamelCase      ) ,      tensor_type=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[Any] ,      *lowerCamelCase   :Tuple ,      **lowerCamelCase   :Union[str, Any]      )     ->			Dict:
       return self.image_processor.post_process(*lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple ,      *lowerCamelCase   :Dict ,      **lowerCamelCase   :Any      )     ->			List[Any]:
       return self.image_processor.post_process_object_detection(*lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :Union[str, Any]      )     ->			Dict:
       return self.image_processor.post_process_image_guided_detection(*lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[Any] ,      *lowerCamelCase   :Optional[int] ,      **lowerCamelCase   :str      )     ->			Dict:
       return self.tokenizer.batch_decode(*lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple ,      *lowerCamelCase   :Tuple ,      **lowerCamelCase   :List[Any]      )     ->			str:
       return self.tokenizer.decode(*lowerCamelCase ,      **lowerCamelCase      )
      @property
      def 	UpperCAmelCase_						(			self   :Any      )     ->			Union[str, Any]:
       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 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			str:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,      lowerCamelCase ,      )
       return self.image_processor











import math



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int	):




 """simple docstring"""







 UpperCAmelCase__							 =						math.loga(math.sqrt(4 * positive_integer + 1	) / 2 + 1 / 2	)

 return exponent == int(_lowerCAmelCase	)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   float = 1 / 1_2345	):




 """simple docstring"""







 UpperCAmelCase__							 =						0
 UpperCAmelCase__							 =						0

 UpperCAmelCase__							 =						3
 while True:
  UpperCAmelCase__							 =						(integer**2 - 1) / 4
  # if candidate is an integer, then there is a partition for k
  if partition_candidate == int(_lowerCAmelCase	):
   UpperCAmelCase__							 =						int(_lowerCAmelCase	)
   total_partitions += 1
   if check_partition_perfect(_lowerCAmelCase	):
    perfect_partitions += 1
  if perfect_partitions > 0:
   if perfect_partitions / total_partitions < max_proportion:
    return int(_lowerCAmelCase	)
  integer += 1


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





