






'''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					(__lowerCamelCase    ):
	_lowerCamelCase              =  ['''image_processor''', '''tokenizer''']
	_lowerCamelCase              =  '''OwlViTImageProcessor'''
	_lowerCamelCase              =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

	def __init__(      self   :						Optional[int]  ,					UpperCAmelCase_   :						str=None  ,					UpperCAmelCase_   :						List[str]=None  ,					**UpperCAmelCase_   :						List[Any]):
			UpperCamelCase__				:					str					=					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_  ,					)
					UpperCamelCase__				:					str					=					kwargs.pop('feature_extractor')

			UpperCamelCase__				:					Any					=					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   :						Tuple  ,					UpperCAmelCase_   :						Optional[int]=None  ,					UpperCAmelCase_   :						str=None  ,					UpperCAmelCase_   :						int=None  ,					UpperCAmelCase_   :						Optional[Any]="max_length"  ,					UpperCAmelCase_   :						str="np"  ,					**UpperCAmelCase_   :						Optional[Any]):

			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(UpperCAmelCase_  ,					UpperCAmelCase_) or (isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) and not isinstance(text[0]  ,					UpperCAmelCase_)):
							UpperCamelCase__				:					Union[str, Any]					=					[self.tokenizer(UpperCAmelCase_  ,					padding=UpperCAmelCase_  ,					return_tensors=UpperCAmelCase_  ,					**UpperCAmelCase_)]

					elif isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) and isinstance(text[0]  ,					UpperCAmelCase_):
							UpperCamelCase__				:					Union[str, Any]					=					[]

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

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

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

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

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

							UpperCamelCase__				:					Dict					=					torch.cat([encoding['input_ids'] for encoding in encodings]  ,					dim=0)
							UpperCamelCase__				:					Union[str, 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__				:					List[str]					=					tf.stack([encoding['input_ids'] for encoding in encodings]  ,					axis=0)
							UpperCamelCase__				:					Union[str, Any]					=					tf.stack([encoding['attention_mask'] for encoding in encodings]  ,					axis=0)

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

					UpperCamelCase__				:					Optional[Any]					=					BatchEncoding()
					UpperCamelCase__				:					str					=					input_ids
					UpperCamelCase__				:					Optional[Any]					=					attention_mask

			if query_images is not None:
					UpperCamelCase__				:					Tuple					=					BatchEncoding()
					UpperCamelCase__				:					Tuple					=					self.image_processor(
					    UpperCAmelCase_  ,					return_tensors=UpperCAmelCase_  ,					**UpperCAmelCase_).pixel_values
					UpperCamelCase__				:					Union[str, Any]					=					query_pixel_values

			if images is not None:
					UpperCamelCase__				:					List[str]					=					self.image_processor(UpperCAmelCase_  ,					return_tensors=UpperCAmelCase_  ,					**UpperCAmelCase_)

			if text is not None and images is not None:
					UpperCamelCase__				:					Tuple					=					image_features.pixel_values
					return encoding
			elif query_images is not None and images is not None:
					UpperCamelCase__				:					int					=					image_features.pixel_values
					return encoding
			elif text is not None or query_images is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**UpperCAmelCase_)  ,					tensor_type=UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict  ,					*UpperCAmelCase_   :						Optional[int]  ,					**UpperCAmelCase_   :						Union[str, Any]):
			return self.image_processor.post_process(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int  ,					*UpperCAmelCase_   :						List[Any]  ,					**UpperCAmelCase_   :						Optional[Any]):
			return self.image_processor.post_process_object_detection(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					*UpperCAmelCase_   :						str  ,					**UpperCAmelCase_   :						int):
			return self.image_processor.post_process_image_guided_detection(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int  ,					*UpperCAmelCase_   :						Union[str, Any]  ,					**UpperCAmelCase_   :						Dict):
			return self.tokenizer.batch_decode(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					*UpperCAmelCase_   :						int  ,					**UpperCAmelCase_   :						str):
			return self.tokenizer.decode(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						str):
			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   :						List[Any]):
			warnings.warn(
			    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,					UpperCAmelCase_  ,					)
			return self.image_processor












'''simple docstring'''




def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     list[str]:
		return [sentence[i : i + ngram_size] for i in range(len(lowerCamelCase_) - ngram_size + 1)]


if __name__ == "__main__":
		from doctest import testmod

		testmod()




