

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  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Dict       			=			['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			'''OwlViTImageProcessor'''
				__SCREAMING_SNAKE_CASE					: Any       			=			('''CLIPTokenizer''', '''CLIPTokenizerFast''')




				def __init__(					self      ,   snake_case=None      ,   snake_case=None      ,   **snake_case       ):
											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.'      ,   snake_case      ,   )
																		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__(snake_case      ,   snake_case       )




				def __call__(					self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case="max_length"      ,   snake_case="np"      ,   **snake_case       ):

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

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

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

																									# Pad all batch samples to max number of text queries
																									for t in text:
																																if len(snake_case       ) != max_num_queries:
																																							snake_case_							=    t + [' '] * (max_num_queries - len(snake_case       ))

																																snake_case_							=    self.tokenizer(snake_case      ,   padding=snake_case      ,   return_tensors=snake_case      ,   **snake_case       )
																																encodings.append(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":
																									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(
																		    snake_case      ,   return_tensors=snake_case      ,   **snake_case       ).pixel_values
																		snake_case_							=    query_pixel_values

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

											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(**snake_case       )      ,   tensor_type=snake_case       )




				def     a							(					self      ,   *snake_case      ,   **snake_case       ):
											return self.image_processor.post_process(*snake_case      ,   **snake_case       )




				def     a							(					self      ,   *snake_case      ,   **snake_case       ):
											return self.image_processor.post_process_object_detection(*snake_case      ,   **snake_case       )




				def     a							(					self      ,   *snake_case      ,   **snake_case       ):
											return self.image_processor.post_process_image_guided_detection(*snake_case      ,   **snake_case       )




				def     a							(					self      ,   *snake_case      ,   **snake_case       ):
											return self.tokenizer.batch_decode(*snake_case      ,   **snake_case       )




				def     a							(					self      ,   *snake_case      ,   **snake_case       ):
											return self.tokenizer.decode(*snake_case      ,   **snake_case       )




				@property
				def     a							(					self       ):
											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,   snake_case      ,   )
											return self.image_processor_class




				@property
				def     a							(					self       ):
											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,   snake_case      ,   )
											return self.image_processor







def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




