





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							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	["""image_processor""", """tokenizer"""]
 UpperCAmelCase__									=	"""OwlViTImageProcessor"""
 UpperCAmelCase__									=	("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(   self						:   Optional[int]    ,  UpperCAmelCase						:   str=None    ,  UpperCAmelCase						:   Any=None    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			Dict:
     lowerCamelCase__					:   Any      				=	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    ,  )
         lowerCamelCase__					:   Optional[Any]      				=	kwargs.pop('feature_extractor'			)

     lowerCamelCase__					:   Dict      				=	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						:   List[Any]    ,  UpperCAmelCase						:   List[Any]=None    ,  UpperCAmelCase						:   Tuple=None    ,  UpperCAmelCase						:   int=None    ,  UpperCAmelCase						:   Optional[int]="max_length"    ,  UpperCAmelCase						:   Dict="np"    ,  **UpperCAmelCase						:   Union[str, Any]			)    ->			Union[str, Any]:

     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			)):
             lowerCamelCase__					:   Any      				=	[self.tokenizer(UpperCAmelCase    ,  padding=UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)]

         elif isinstance(UpperCAmelCase    ,  UpperCAmelCase			) and isinstance(text[0]    ,  UpperCAmelCase			):
             lowerCamelCase__					:   List[str]      				=	[]

             # Maximum number of queries across batch
             lowerCamelCase__					:   List[Any]      				=	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:
                     lowerCamelCase__					:   int      				=	t + [' '] * (max_num_queries - len(UpperCAmelCase			))

                 lowerCamelCase__					:   str      				=	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":
             lowerCamelCase__					:   Optional[Any]      				=	np.concatenate([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   int      				=	np.concatenate([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

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

             lowerCamelCase__					:   Optional[int]      				=	jnp.concatenate([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   Any      				=	jnp.concatenate([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

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

             lowerCamelCase__					:   Union[str, Any]      				=	torch.cat([encoding['input_ids'] for encoding in encodings]    ,  dim=0			)
             lowerCamelCase__					:   Dict      				=	torch.cat([encoding['attention_mask'] for encoding in encodings]    ,  dim=0			)

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

             lowerCamelCase__					:   Any      				=	tf.stack([encoding['input_ids'] for encoding in encodings]    ,  axis=0			)
             lowerCamelCase__					:   Optional[int]      				=	tf.stack([encoding['attention_mask'] for encoding in encodings]    ,  axis=0			)

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

         lowerCamelCase__					:   str      				=	BatchEncoding()
         lowerCamelCase__					:   int      				=	input_ids
         lowerCamelCase__					:   Any      				=	attention_mask

     if query_images is not None:
         lowerCamelCase__					:   Union[str, Any]      				=	BatchEncoding()
         lowerCamelCase__					:   Optional[Any]      				=	self.image_processor(
             UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			).pixel_values
         lowerCamelCase__					:   List[Any]      				=	query_pixel_values

     if images is not None:
         lowerCamelCase__					:   List[Any]      				=	self.image_processor(UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  **UpperCAmelCase			)

     if text is not None and images is not None:
         lowerCamelCase__					:   Tuple      				=	image_features.pixel_values
         return encoding
     elif query_images is not None and images is not None:
         lowerCamelCase__					:   Dict      				=	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   A_			(   self						:   str    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   List[Any]			)    ->			Any:
     return self.image_processor.post_process(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   int    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Tuple			)    ->			str:
     return self.image_processor.post_process_object_detection(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Tuple    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[Any]:
     return self.image_processor.post_process_image_guided_detection(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Optional[Any]    ,  *UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   List[Any]			)    ->			Tuple:
     return self.tokenizer.batch_decode(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   List[Any]			)    ->			List[str]:
     return self.tokenizer.decode(*UpperCAmelCase    ,  **UpperCAmelCase			)
 @property
 def   A_			(   self						:   List[str]			)    ->			Optional[Any]:
     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   A_			(   self						:   int			)    ->			int:
     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,  UpperCAmelCase    ,  )
     return self.image_processor












from collections import defaultdict
from math import ceil, sqrt


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 100_0000		,			_UpperCAmelCase = 10				)    ->  int:
    lowerCamelCase__					:   defaultdict      				=	defaultdict(_UpperCAmelCase				)

    for outer_width in range(3		,			(t_limit // 4) + 2				):
        if outer_width * outer_width > t_limit:
            lowerCamelCase__					:   List[str]      				=	max(
                ceil(sqrt(outer_width * outer_width - t_limit				)				)		,			1				)
        else:
            lowerCamelCase__					:   int      				=	1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(_UpperCAmelCase		,			outer_width - 1		,			2				):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10				)


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





