


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      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		str        =	['''image_processor''', '''tokenizer''']
	_lowerCAmelCase    :		Tuple        =	'''OwlViTImageProcessor'''
	_lowerCAmelCase    :		Optional[int]        =	('''CLIPTokenizer''', '''CLIPTokenizerFast''')


	def __init__(						self							,    lowercase__=None							,    lowercase__=None							,    **lowercase__):
							__UpperCAmelCase :       Tuple  						=  None
							if "feature_extractor" in kwargs:
													warnings.warn(
													    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
													    ''' instead.'''							,    lowercase__							,    )
													__UpperCAmelCase :       Tuple  						=  kwargs.pop('''feature_extractor''')

							__UpperCAmelCase :       str  						=  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__(lowercase__							,    lowercase__)


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

							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(lowercase__							,    lowercase__) or (isinstance(lowercase__							,    lowercase__) and not isinstance(text[0]							,    lowercase__)):
																			__UpperCAmelCase :       Dict  						=  [self.tokenizer(lowercase__							,    padding=lowercase__							,    return_tensors=lowercase__							,    **lowercase__)]

													elif isinstance(lowercase__							,    lowercase__) and isinstance(text[0]							,    lowercase__):
																			__UpperCAmelCase :       Union[str, Any]  						=  []

																			# Maximum number of queries across batch
																			__UpperCAmelCase :       Tuple  						=  max([len(lowercase__) for t in text])

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

																									__UpperCAmelCase :       List[str]  						=  self.tokenizer(lowercase__							,    padding=lowercase__							,    return_tensors=lowercase__							,    **lowercase__)
																									encodings.append(lowercase__)
													else:
																			raise TypeError('''Input text should be a string, a list of strings or a nested list of strings''')

													if return_tensors == "np":
																			__UpperCAmelCase :       Union[str, Any]  						=  np.concatenate([encoding['''input_ids'''] for encoding in encodings]							,    axis=0)
																			__UpperCAmelCase :       int  						=  np.concatenate([encoding['''attention_mask'''] for encoding in encodings]							,    axis=0)

													elif return_tensors == "jax" and is_flax_available():
																			import jax.numpy as jnp

																			__UpperCAmelCase :       int  						=  jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]							,    axis=0)
																			__UpperCAmelCase :       Any  						=  jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]							,    axis=0)

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

																			__UpperCAmelCase :       List[str]  						=  torch.cat([encoding['''input_ids'''] for encoding in encodings]							,    dim=0)
																			__UpperCAmelCase :       Any  						=  torch.cat([encoding['''attention_mask'''] for encoding in encodings]							,    dim=0)

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

																			__UpperCAmelCase :       Dict  						=  tf.stack([encoding['''input_ids'''] for encoding in encodings]							,    axis=0)
																			__UpperCAmelCase :       List[Any]  						=  tf.stack([encoding['''attention_mask'''] for encoding in encodings]							,    axis=0)

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

													__UpperCAmelCase :       List[Any]  						=  BatchEncoding()
													__UpperCAmelCase :       Tuple  						=  input_ids
													__UpperCAmelCase :       List[Any]  						=  attention_mask

							if query_images is not None:
													__UpperCAmelCase :       Any  						=  BatchEncoding()
													__UpperCAmelCase :       str  						=  self.image_processor(
													    lowercase__							,    return_tensors=lowercase__							,    **lowercase__).pixel_values
													__UpperCAmelCase :       Tuple  						=  query_pixel_values

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

							if text is not None and images is not None:
													__UpperCAmelCase :       List[Any]  						=  image_features.pixel_values
													return encoding
							elif query_images is not None and images is not None:
													__UpperCAmelCase :       Union[str, Any]  						=  image_features.pixel_values
													return encoding
							elif text is not None or query_images is not None:
													return encoding
							else:
													return BatchEncoding(data=dict(**lowercase__)							,    tensor_type=lowercase__)


	def    A(						self							,    *lowercase__							,    **lowercase__):
							return self.image_processor.post_process(*lowercase__							,    **lowercase__)


	def    A(						self							,    *lowercase__							,    **lowercase__):
							return self.image_processor.post_process_object_detection(*lowercase__							,    **lowercase__)


	def    A(						self							,    *lowercase__							,    **lowercase__):
							return self.image_processor.post_process_image_guided_detection(*lowercase__							,    **lowercase__)


	def    A(						self							,    *lowercase__							,    **lowercase__):
							return self.tokenizer.batch_decode(*lowercase__							,    **lowercase__)


	def    A(						self							,    *lowercase__							,    **lowercase__):
							return self.tokenizer.decode(*lowercase__							,    **lowercase__)


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





from __future__ import annotations




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    int:




						'''simple docstring'''







						if not nums:
												return 0
						__UpperCAmelCase :       int  						=  nums[0]
						__UpperCAmelCase :       Optional[Any]  						=  0
						for num in nums[1:]:
												__UpperCAmelCase						,			__UpperCAmelCase :       int  						=  (
												    max_excluding + num,
												    max(lowercase_			,					lowercase_			),
												)
						return max(lowercase_			,					lowercase_			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

