






"""simple docstring"""



from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__A		:       List[str]  				=		TypeVar('''T''')





class 					_UpperCAmelCase       ( Generic[T]							):
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   42  # Cache store of keys
					SCREAMING_SNAKE_CASE_		:				Any							    =   42  # References of the keys in cache
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   10  # Maximum capacity of cache




					def __init__(				self     :						Any    ,						A     :						List[Any]   )  ->  None:
							lowercase_ : List[Any]							       =   deque()
							lowercase_ : Union[str, Any]							       =   set()
							if not n:
									lowercase_ : List[Any]							       =   sys.maxsize
							elif n < 0:
									raise ValueError('''n should be an integer greater than 0.'''   )
							else:
									lowercase_ : str							       =   n




					def   A  (				self     :						List[Any]    ,						A     :						List[Any]   )  ->  None:
							if x not in self.key_reference:
									if len(self.dq_store   ) == LRUCache._MAX_CAPACITY:
											lowercase_ : Optional[Any]							       =   self.dq_store.pop()
											self.key_reference.remove(__lowerCamelCase   )
							else:
									self.dq_store.remove(__lowerCamelCase   )

							self.dq_store.appendleft(__lowerCamelCase   )
							self.key_reference.add(__lowerCamelCase   )




					def   A  (				self     :						Any   )  ->  None:
							for k in self.dq_store:
									print(__lowerCamelCase   )






					def __repr__(				self     :						Any   )  ->  str:
							return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store   )}'''


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						__A		:       Union[str, Any]  				=		LRUCache(4)
						lru_cache.refer('''A''')
						lru_cache.refer(2)
						lru_cache.refer(3)
						lru_cache.refer('''A''')
						lru_cache.refer(4)
						lru_cache.refer(5)
						lru_cache.display()

						print(lru_cache)
						assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"







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):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ["image_processor", "tokenizer"]
     __SCREAMING_SNAKE_CASE  			= "OwlViTImageProcessor"
     __SCREAMING_SNAKE_CASE  			= ("CLIPTokenizer", "CLIPTokenizerFast")


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase)			->				Union[str, Any]:
            _A      :       int				    = 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 ,						)
                   _A      :       List[Any]				    = kwargs.pop("feature_extractor")

            _A      :       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__(__lowerCamelCase ,						__lowerCamelCase)


     def __call__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="max_length" ,						__lowerCamelCase="np" ,						**__lowerCamelCase)			->				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(__lowerCamelCase ,						__lowerCamelCase) or (isinstance(__lowerCamelCase ,						__lowerCamelCase) and not isinstance(text[0] ,						__lowerCamelCase)):
                          _A      :       Union[str, Any]				    = [self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)]

                   elif isinstance(__lowerCamelCase ,						__lowerCamelCase) and isinstance(text[0] ,						__lowerCamelCase):
                          _A      :       Optional[Any]				    = []

                          # Maximum number of queries across batch
                          _A      :       str				    = 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:
                                        _A      :       Optional[int]				    = t + [" "] * (max_num_queries - len(__lowerCamelCase))

                                 _A      :       List[Any]				    = 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":
                          _A      :       Tuple				    = np.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[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

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

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

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

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

                          _A      :       Any				    = tf.stack([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Tuple				    = tf.stack([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

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

                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       Tuple				    = input_ids
                   _A      :       Dict				    = attention_mask

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

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

            if text is not None and images is not None:
                   _A      :       Tuple				    = image_features.pixel_values
                   return encoding
            elif query_images is not None and images is not None:
                   _A      :       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(**__lowerCamelCase) ,						tensor_type=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				str:
            return self.image_processor.post_process(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				List[str]:
            return self.image_processor.post_process_object_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				int:
            return self.tokenizer.batch_decode(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.tokenizer.decode(*__lowerCamelCase ,						**__lowerCamelCase)


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