

'''simple docstring'''





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      :				Union[str, Any]     =    ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    '''OwlViTImageProcessor'''
				__SCREAMING_SNAKE_CASE      :				Dict     =    ('''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







_UpperCAmelCase	:     str		       = [0, 2, 4, 6, 8]
_UpperCAmelCase	:     Any		       = [1, 3, 5, 7, 9]





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							if remaining_length == 0:
														if digits[0] == 0 or digits[-1] == 0:
																					return 0

														for i in range(length // 2 - 1     ,							-1     ,							-1   ):
																					remainder += digits[i] + digits[length - i - 1]

																					if remainder % 2 == 0:
																												return 0

																					remainder //= 10

														return 1

							if remaining_length == 1:
														if remainder % 2 == 0:
																					return 0

														snake_case_           =					0
														for digit in range(10   ):
																					snake_case_           =					digit
																					result += reversible_numbers(
																					    0     ,							(remainder + 2 * digit) // 10     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														return result

							snake_case_           =					0
							for digita in range(10   ):
														snake_case_           =					digita

														if (remainder + digita) % 2 == 0:
																					snake_case_           =					ODD_DIGITS
														else:
																					snake_case_           =					EVEN_DIGITS

														for digita in other_parity_digits:
																					snake_case_           =					digita
																					result += reversible_numbers(
																					    remaining_length - 2     ,							(remainder + digita + digita) // 10     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							)
							return result





def       __lowerCamelCase			(				UpperCamelCase__ = 9   ):







							'''simple docstring'''






							snake_case_           =					0
							for length in range(1     ,							max_power + 1   ):
														result += reversible_numbers(UpperCamelCase__     ,							0     ,							[0] * length     ,							UpperCamelCase__   )
							return result


if __name__ == "__main__":
					print(F'''{solution() = }''')




