






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      ["""image_processor""", """tokenizer"""]
						snake_case__        =      """ViTImageProcessor"""
						snake_case__        =      ("""CLIPTokenizer""", """CLIPTokenizerFast""")



						def __init__(self:					str ,					__UpperCAmelCase:					List[str]=None ,					__UpperCAmelCase:					int=None ,					**__UpperCAmelCase:					str   )    ->     str:




											'''simple docstring'''



											__a					:						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 ,					)
																__a					:						Dict      =					kwargs.pop("feature_extractor"   )

											__a					:						Optional[int]      =					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:					Union[str, Any] ,					__UpperCAmelCase:					Any=None ,					__UpperCAmelCase:					Tuple=None ,					__UpperCAmelCase:					Optional[Any]=None ,					__UpperCAmelCase:					Optional[Any]=None ,					**__UpperCAmelCase:					Tuple   )    ->     Union[str, Any]:




											'''simple docstring'''



											if text is None and visual_prompt is None and images is None:
																raise ValueError("You have to specify either text, visual prompt or images."   )

											if text is not None and visual_prompt is not None:
																raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."   )

											if text is not None:
																__a					:						int      =					self.tokenizer(__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					**__UpperCAmelCase   )

											if visual_prompt is not None:
																__a					:						int      =					self.image_processor(__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					**__UpperCAmelCase   )

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

											if visual_prompt is not None and images is not None:
																__a					:						str      =					{
																    "pixel_values": image_features.pixel_values,
																    "conditional_pixel_values": prompt_features.pixel_values,
																}
																return encoding
											elif text is not None and images is not None:
																__a					:						Tuple      =					image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											elif visual_prompt is not None:
																__a					:						List[Any]      =					{
																    "conditional_pixel_values": prompt_features.pixel_values,
																}
																return encoding
											else:
																return BatchEncoding(data=dict(**__UpperCAmelCase   ) ,					tensor_type=__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					int ,					*__UpperCAmelCase:					List[Any] ,					**__UpperCAmelCase:					List[Any]   )    ->     Any:




											'''simple docstring'''



											return self.tokenizer.batch_decode(*__UpperCAmelCase ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[int] ,					*__UpperCAmelCase:					Any ,					**__UpperCAmelCase:					List[str]   )    ->     Dict:




											'''simple docstring'''



											return self.tokenizer.decode(*__UpperCAmelCase ,					**__UpperCAmelCase   )



						@property
						def        UpperCAmelCase__					(self:					Any   )    ->     Optional[int]:




											'''simple docstring'''



											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        UpperCAmelCase__					(self:					Optional[int]   )    ->     str:




											'''simple docstring'''



											warnings.warn(
											    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,					__UpperCAmelCase ,					)
											return self.image_processor













from __future__ import annotations







def     a_   (__A      )				->						int:



					"""simple docstring"""
					__a					:						List[Any]      =					len(__A      ) // 2

					# choose the middle 3 elements
					__a					:						Any      =					lst[m - 1 : m + 2]

					# if middle element is peak
					if three[1] > three[0] and three[1] > three[2]:
										return three[1]

					# if increasing, recurse on right
					elif three[0] < three[2]:
										if len(lst[:m]      ) == 2:
															m -= 1
										return peak(lst[m:]      )

					# decreasing
					else:
										if len(lst[:m]      ) == 2:
															m += 1
										return peak(lst[:m]      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





