



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        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			str      =  ['''image_processor''', '''tokenizer''']
						UpperCamelCase__      :			List[Any]      =  '''OwlViTImageProcessor'''
						UpperCamelCase__      :			str      =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






						def __init__(   self				,							_A=None				,							_A=None				,							**_A		):







													'''simple docstring'''

													__SCREAMING_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				,							)
																				__SCREAMING_SNAKE_CASE									= kwargs.pop('feature_extractor'		)

													__SCREAMING_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				,							_A=None				,							_A=None				,							_A=None				,							_A="max_length"				,							_A="np"				,							**_A		):







													'''simple docstring'''

													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		)):
																											__SCREAMING_SNAKE_CASE									= [self.tokenizer(_A				,							padding=_A				,							return_tensors=_A				,							**_A		)]

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

																											# Maximum number of queries across batch
																											__SCREAMING_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:
																																									__SCREAMING_SNAKE_CASE									= t + [' '] * (max_num_queries - len(_A		))

																																		__SCREAMING_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":
																											__SCREAMING_SNAKE_CASE									= np.concatenate([encoding['input_ids'] for encoding in encodings]				,							axis=0		)
																											__SCREAMING_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

																											__SCREAMING_SNAKE_CASE									= jnp.concatenate([encoding['input_ids'] for encoding in encodings]				,							axis=0		)
																											__SCREAMING_SNAKE_CASE									= jnp.concatenate([encoding['attention_mask'] for encoding in encodings]				,							axis=0		)

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

																											__SCREAMING_SNAKE_CASE									= torch.cat([encoding['input_ids'] for encoding in encodings]				,							dim=0		)
																											__SCREAMING_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

																											__SCREAMING_SNAKE_CASE									= tf.stack([encoding['input_ids'] for encoding in encodings]				,							axis=0		)
																											__SCREAMING_SNAKE_CASE									= 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									= BatchEncoding()
																				__SCREAMING_SNAKE_CASE									= input_ids
																				__SCREAMING_SNAKE_CASE									= attention_mask

													if query_images is not None:
																				__SCREAMING_SNAKE_CASE									= BatchEncoding()
																				__SCREAMING_SNAKE_CASE									= self.image_processor(
																				    _A				,							return_tensors=_A				,							**_A		).pixel_values
																				__SCREAMING_SNAKE_CASE									= query_pixel_values

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

													if text is not None and images is not None:
																				__SCREAMING_SNAKE_CASE									= image_features.pixel_values
																				return encoding
													elif query_images is not None and images is not None:
																				__SCREAMING_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 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

													return self.image_processor.post_process(*_A				,							**_A		)






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

													return self.image_processor.post_process_object_detection(*_A				,							**_A		)






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

													return self.image_processor.post_process_image_guided_detection(*_A				,							**_A		)






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

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






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													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 	_A     (   self		):







													'''simple docstring'''

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











lowerCAmelCase__		:					Optional[int]							=9.80_665






def      __lowercase       (			a__							,       a__							,       a__ = g       )    ->			float:

							if fluid_density <= 0:
														raise ValueError('Impossible fluid density'       )
							if volume < 0:
														raise ValueError('Impossible Object volume'       )
							if gravity <= 0:
														raise ValueError('Impossible Gravity'       )

							return fluid_density * gravity * volume


if __name__ == "__main__":
			import doctest

			# run doctest
			doctest.testmod()






