

'''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 							_snake_case (       _a						):
   _A       :     Any       =					['''image_processor''', '''tokenizer''']
   _A       :     str       =					'''OwlViTImageProcessor'''
   _A       :     List[str]       =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')
   def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Tuple=None     ,SCREAMING_SNAKE_CASE__			:      List[str]=None     ,**SCREAMING_SNAKE_CASE__			:      Dict							):
     SCREAMING_SNAKE_CASE:Tuple						      =					None
     if "feature_extractor" in kwargs:
       warnings.warn(
           "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
           " instead."     ,SCREAMING_SNAKE_CASE__     ,)
       SCREAMING_SNAKE_CASE:List[Any]						      =					kwargs.pop("feature_extractor"							)

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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__(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
   def __call__( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      List[Any]=None     ,SCREAMING_SNAKE_CASE__			:      List[str]=None     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=None     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]="max_length"     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]="np"     ,**SCREAMING_SNAKE_CASE__			:      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(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) or (isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) and not isinstance(text[0]     ,SCREAMING_SNAKE_CASE__							)):
         SCREAMING_SNAKE_CASE:Tuple						      =					[self.tokenizer(SCREAMING_SNAKE_CASE__     ,padding=SCREAMING_SNAKE_CASE__     ,return_tensors=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)]

       elif isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) and isinstance(text[0]     ,SCREAMING_SNAKE_CASE__							):
         SCREAMING_SNAKE_CASE:Any						      =					[]

         # Maximum number of queries across batch
         SCREAMING_SNAKE_CASE:Optional[int]						      =					max([len(SCREAMING_SNAKE_CASE__							) for t in text]							)

         # Pad all batch samples to max number of text queries
         for t in text:
           if len(SCREAMING_SNAKE_CASE__							) != max_num_queries:
             SCREAMING_SNAKE_CASE:int						      =					t + [" "] * (max_num_queries - len(SCREAMING_SNAKE_CASE__							))

           SCREAMING_SNAKE_CASE:Dict						      =					self.tokenizer(SCREAMING_SNAKE_CASE__     ,padding=SCREAMING_SNAKE_CASE__     ,return_tensors=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
           encodings.append(SCREAMING_SNAKE_CASE__							)
       else:
         raise TypeError("Input text should be a string, a list of strings or a nested list of strings"							)

       if return_tensors == "np":
         SCREAMING_SNAKE_CASE:Optional[Any]						      =					np.concatenate([encoding["input_ids"] for encoding in encodings]     ,axis=0							)
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					np.concatenate([encoding["attention_mask"] for encoding in encodings]     ,axis=0							)

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

         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					jnp.concatenate([encoding["input_ids"] for encoding in encodings]     ,axis=0							)
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					jnp.concatenate([encoding["attention_mask"] for encoding in encodings]     ,axis=0							)

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

         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.cat([encoding["input_ids"] for encoding in encodings]     ,dim=0							)
         SCREAMING_SNAKE_CASE:List[Any]						      =					torch.cat([encoding["attention_mask"] for encoding in encodings]     ,dim=0							)

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

         SCREAMING_SNAKE_CASE:List[str]						      =					tf.stack([encoding["input_ids"] for encoding in encodings]     ,axis=0							)
         SCREAMING_SNAKE_CASE:List[str]						      =					tf.stack([encoding["attention_mask"] for encoding in encodings]     ,axis=0							)

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

       SCREAMING_SNAKE_CASE:int						      =					BatchEncoding()
       SCREAMING_SNAKE_CASE:List[str]						      =					input_ids
       SCREAMING_SNAKE_CASE:List[Any]						      =					attention_mask

     if query_images is not None:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					BatchEncoding()
       SCREAMING_SNAKE_CASE:Dict						      =					self.image_processor(
           SCREAMING_SNAKE_CASE__     ,return_tensors=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							).pixel_values
       SCREAMING_SNAKE_CASE:Dict						      =					query_pixel_values

     if images is not None:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					self.image_processor(SCREAMING_SNAKE_CASE__     ,return_tensors=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)

     if text is not None and images is not None:
       SCREAMING_SNAKE_CASE:List[Any]						      =					image_features.pixel_values
       return encoding
     elif query_images is not None and images is not None:
       SCREAMING_SNAKE_CASE:int						      =					image_features.pixel_values
       return encoding
     elif text is not None or query_images is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__							)     ,tensor_type=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Optional[int]     ,*SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      Tuple							):
     return self.image_processor.post_process(*SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      str     ,*SCREAMING_SNAKE_CASE__			:      Any     ,**SCREAMING_SNAKE_CASE__			:      Tuple							):
     return self.image_processor.post_process_object_detection(*SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      str     ,*SCREAMING_SNAKE_CASE__			:      Tuple     ,**SCREAMING_SNAKE_CASE__			:      List[str]							):
     return self.image_processor.post_process_image_guided_detection(*SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      int     ,*SCREAMING_SNAKE_CASE__			:      List[str]     ,**SCREAMING_SNAKE_CASE__			:      Dict							):
     return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Dict     ,*SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   @property
   def     __UpperCamelCase						( self			:      Any							):
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,SCREAMING_SNAKE_CASE__     ,)
     return self.image_processor_class







   @property
   def     __UpperCamelCase						( self			:      Any							):
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,SCREAMING_SNAKE_CASE__     ,)
     return self.image_processor









'''simple docstring'''

def    A_  ( snake_case = 100 ):
  SCREAMING_SNAKE_CASE:Dict						      =					0
  SCREAMING_SNAKE_CASE:Optional[int]						      =					0
  for i in range(1      ,       n + 1 ):
    sum_of_squares += i**2
    sum_of_ints += i
  return sum_of_ints**2 - sum_of_squares


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






