
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		(  A_  ):
     UpperCAmelCase__    :							List[str]									=		["image_processor", "tokenizer"]
     UpperCAmelCase__    :							Tuple									=		"OwlViTImageProcessor"
     UpperCAmelCase__    :							Any									=		("CLIPTokenizer", "CLIPTokenizerFast")





     def __init__(self				:   Dict    ,   _A				:   Optional[Any]=None    ,   _A				:   Any=None    ,   **_A				:   Dict       )						->						Union[str, Any]:
         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."    ,   _A    ,   )
             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__(_A    ,   _A       )





     def __call__(self				:   List[str]    ,   _A				:   Optional[Any]=None    ,   _A				:   int=None    ,   _A				:   Optional[int]=None    ,   _A				:   Optional[int]="max_length"    ,   _A				:   Optional[Any]="np"    ,   **_A				:   int       )						->						str:

         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(_A    ,   _A       ) or (isinstance(_A    ,   _A       ) and not isinstance(text[0]    ,   _A       )):
                 snake_case								= [self.tokenizer(_A    ,   padding=_A    ,   return_tensors=_A    ,   **_A       )]

             elif isinstance(_A    ,   _A       ) and isinstance(text[0]    ,   _A       ):
                 snake_case								= []

                 # Maximum number of queries across batch
                 snake_case								= max([len(_A       ) for t in text]       )

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

                     snake_case								= self.tokenizer(_A    ,   padding=_A    ,   return_tensors=_A    ,   **_A       )
                     encodings.append(_A       )
             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(
                 _A    ,   return_tensors=_A    ,   **_A       ).pixel_values
             snake_case								= query_pixel_values

         if images is not None:
             snake_case								= self.image_processor(_A    ,   return_tensors=_A    ,   **_A       )

         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(**_A       )    ,   tensor_type=_A       )





     def 		UpperCAmelCase(self				:   List[Any]    ,   *_A				:   Optional[int]    ,   **_A				:   List[Any]       )						->						Any:
         return self.image_processor.post_process(*_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   List[Any]    ,   *_A				:   Tuple    ,   **_A				:   Any       )						->						Tuple:
         return self.image_processor.post_process_object_detection(*_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   Dict    ,   *_A				:   str    ,   **_A				:   Optional[int]       )						->						int:
         return self.image_processor.post_process_image_guided_detection(*_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   Any    ,   *_A				:   Any    ,   **_A				:   List[str]       )						->						int:
         return self.tokenizer.batch_decode(*_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   int    ,   *_A				:   Tuple    ,   **_A				:   List[Any]       )						->						str:
         return self.tokenizer.decode(*_A    ,   **_A       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						List[Any]:
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,   _A    ,   )
         return self.image_processor_class





     @property
     def 		UpperCAmelCase(self				:   int       )						->						Optional[int]:
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,   _A    ,   )
         return self.image_processor



def 						lowercase_      (       A__ = 1000		)							->							int:
    """simple docstring"""

    return sum(2 * a * ((a - 1) // 2) for a in range(3			,		n + 1		)		)


if __name__ == "__main__":
   print(solution())

