


"""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     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[Any]							 =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase		:       List[str]							 ="""OwlViTImageProcessor"""
							__UpperCAmelCase		:       Optional[int]							 =("""CLIPTokenizer""", """CLIPTokenizerFast""")

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

										__lowerCAmelCase							       =     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__(__a       ,  __a      )

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

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

													elif isinstance(__a       ,  __a      ) and isinstance(text[0]       ,  __a      ):
																__lowerCAmelCase							       =     []

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

																# Pad all batch samples to max number of text queries
																for t in text:
																			if len(__a      ) != max_num_queries:
																						__lowerCAmelCase							       =     t + [" "] * (max_num_queries - len(__a      ))

																			__lowerCAmelCase							       =     self.tokenizer(__a       ,  padding=__a       ,  return_tensors=__a       ,  **__a      )
																			encodings.append(__a      )
													else:
																raise TypeError("Input text should be a string, a list of strings or a nested list of strings"      )

													if return_tensors == "np":
																__lowerCAmelCase							       =     np.concatenate([encoding["input_ids"] for encoding in encodings]       ,  axis=0      )
																__lowerCAmelCase							       =     np.concatenate([encoding["attention_mask"] for encoding in encodings]       ,  axis=0      )

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

																__lowerCAmelCase							       =     jnp.concatenate([encoding["input_ids"] for encoding in encodings]       ,  axis=0      )
																__lowerCAmelCase							       =     jnp.concatenate([encoding["attention_mask"] for encoding in encodings]       ,  axis=0      )

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

																__lowerCAmelCase							       =     torch.cat([encoding["input_ids"] for encoding in encodings]       ,  dim=0      )
																__lowerCAmelCase							       =     torch.cat([encoding["attention_mask"] for encoding in encodings]       ,  dim=0      )

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

																__lowerCAmelCase							       =     tf.stack([encoding["input_ids"] for encoding in encodings]       ,  axis=0      )
																__lowerCAmelCase							       =     tf.stack([encoding["attention_mask"] for encoding in encodings]       ,  axis=0      )

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

													__lowerCAmelCase							       =     BatchEncoding()
													__lowerCAmelCase							       =     input_ids
													__lowerCAmelCase							       =     attention_mask

										if query_images is not None:
													__lowerCAmelCase							       =     BatchEncoding()
													__lowerCAmelCase							       =     self.image_processor(
													    __a       ,  return_tensors=__a       ,  **__a      ).pixel_values
													__lowerCAmelCase							       =     query_pixel_values

										if images is not None:
													__lowerCAmelCase							       =     self.image_processor(__a       ,  return_tensors=__a       ,  **__a      )

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

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

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

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

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

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

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



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








"""simple docstring"""


from __future__ import annotations
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a=None      ):
										__lowerCAmelCase							       =     data
										__lowerCAmelCase							       =     None



							def __repr__(	self      ):
										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     self
										while temp:
													string_rep.append(f"{temp.data}"      )
													__lowerCAmelCase							       =     temp.next
										return "->".join(__a      )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if not elements_list:
						raise Exception("The Elements List is empty" )

			__lowerCAmelCase							       =     __lowerCAmelCase							       =     Node(elements_list[0] )
			for i in range(1       ,					len(_UpperCamelCase ) ):
						__lowerCAmelCase							       =     Node(elements_list[i] )
						__lowerCAmelCase							       =     current.next
			return head


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if head_node is not None and isinstance(_UpperCamelCase       ,					_UpperCamelCase ):
						print_reverse(head_node.next )
						print(head_node.data )


def _lowerCamelCase      (						):

			'''simple docstring'''




			from doctest import testmod

			testmod()

			__lowerCAmelCase							       =     make_linked_list([14, 52, 14, 12, 43] )
			print("Linked List:" )
			print(_UpperCamelCase )
			print("Elements in Reverse:" )
			print_reverse(_UpperCamelCase )


if __name__ == "__main__":
	main()




