



'''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      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Any		      = ['image_processor', 'tokenizer']
	__SCREAMING_SNAKE_CASE       :		int		      = 'OwlViTImageProcessor'
	__SCREAMING_SNAKE_CASE       :		Any		      = ('CLIPTokenizer', 'CLIPTokenizerFast')


	def __init__(self	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				A_     :						List[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."""	,				lowercase	,				)
							A_     :						List[Any]     =					kwargs.pop("""feature_extractor"""							)

				A_     :						Tuple     =					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							)):
										A_     :						Any     =					[self.tokenizer(lowercase	,				padding=lowercase	,				return_tensors=lowercase	,				**lowercase							)]

							elif isinstance(lowercase	,				lowercase							) and isinstance(text[0]	,				lowercase							):
										A_     :						List[Any]     =					[]

										# Maximum number of queries across batch
										A_     :						Optional[int]     =					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:
																A_     :						Tuple     =					t + [""" """] * (max_num_queries - len(lowercase							))

													A_     :						Any     =					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":
										A_     :						Any     =					np.concatenate([encoding["""input_ids"""] for encoding in encodings]	,				axis=0							)
										A_     :						Optional[Any]     =					np.concatenate([encoding["""attention_mask"""] for encoding in encodings]	,				axis=0							)

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

										A_     :						Union[str, Any]     =					jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]	,				axis=0							)
										A_     :						Optional[int]     =					jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]	,				axis=0							)

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

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

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

										A_     :						Dict     =					tf.stack([encoding["""input_ids"""] for encoding in encodings]	,				axis=0							)
										A_     :						Optional[Any]     =					tf.stack([encoding["""attention_mask"""] for encoding in encodings]	,				axis=0							)

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

							A_     :						Any     =					BatchEncoding()
							A_     :						Any     =					input_ids
							A_     :						Optional[int]     =					attention_mask

				if query_images is not None:
							A_     :						str     =					BatchEncoding()
							A_     :						List[str]     =					self.image_processor(
							    lowercase	,				return_tensors=lowercase	,				**lowercase							).pixel_values
							A_     :						Tuple     =					query_pixel_values

				if images is not None:
							A_     :						str     =					self.image_processor(lowercase	,				return_tensors=lowercase	,				**lowercase							)

				if text is not None and images is not None:
							A_     :						Dict     =					image_features.pixel_values
							return encoding
				elif query_images is not None and images is not None:
							A_     :						List[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




'''simple docstring'''




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(input_str.split()[::-1]   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()