





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class        _a(    __A    ):
		lowerCamelCase__     :Dict   				=		['image_processor', 'tokenizer']
		lowerCamelCase__     :Any   				=		'AutoImageProcessor'
		lowerCamelCase__     :Tuple   				=		'AutoTokenizer'







		def __init__(    self ,						__snake_case=None ,						__snake_case=None ,						**__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   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       :					Optional[int]				       =   kwargs.pop("feature_extractor"				)

							_snake_case       :					Optional[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__(__snake_case ,						__snake_case				)
							_snake_case       :					Optional[Any]				       =   self.image_processor
							_snake_case       :					List[Any]				       =   False







		def __call__(    self ,						*__snake_case ,						**__snake_case				)      ->      str:


							'''simple docstring'''

							if self._in_target_context_manager:
												return self.current_processor(*__snake_case ,						**__snake_case				)

							_snake_case       :					Any				       =   kwargs.pop("images" ,						__snake_case				)
							_snake_case       :					List[str]				       =   kwargs.pop("text" ,						__snake_case				)
							if len(__snake_case				) > 0:
												_snake_case       :					Union[str, Any]				       =   args[0]
												_snake_case       :					Optional[int]				       =   args[1:]

							if images is None and text is None:
												raise ValueError("You need to specify either an `images` or `text` input to process."				)

							if images is not None:
												_snake_case       :					int				       =   self.image_processor(__snake_case ,						*__snake_case ,						**__snake_case				)
							if text is not None:
												_snake_case       :					Union[str, Any]				       =   self.tokenizer(__snake_case ,						**__snake_case				)

							if text is None:
												return inputs
							elif images is None:
												return encodings
							else:
												_snake_case       :					Union[str, Any]				       =   encodings["input_ids"]
												return inputs







		def        lowercase       (    self ,						*__snake_case ,						**__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

							return self.tokenizer.batch_decode(*__snake_case ,						**__snake_case				)







		def        lowercase       (    self ,						*__snake_case ,						**__snake_case				)      ->      List[Any]:


							'''simple docstring'''

							return self.tokenizer.decode(*__snake_case ,						**__snake_case				)







		@contextmanager
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."				)
							_snake_case       :					List[str]				       =   True
							_snake_case       :					Optional[int]				       =   self.tokenizer
							yield
							_snake_case       :					Optional[int]				       =   self.image_processor
							_snake_case       :					Optional[Any]				       =   False







		def        lowercase       (    self ,						__snake_case ,						__snake_case=False ,						__snake_case=None				)      ->      Tuple:


							'''simple docstring'''

							if added_vocab is None:
												_snake_case       :					Optional[Any]				       =   self.tokenizer.get_added_vocab()

							_snake_case       :					Union[str, Any]				       =   {}

							while tokens:
												_snake_case       :					List[Any]				       =   re.search(r"<s_(.*?)>" ,						__snake_case ,						re.IGNORECASE				)
												if start_token is None:
																	break
												_snake_case       :					str				       =   start_token.group(1				)
												_snake_case       :					Union[str, Any]				       =   re.search(rf"""</s_{key}>""" ,						__snake_case ,						re.IGNORECASE				)
												_snake_case       :					Optional[int]				       =   start_token.group()
												if end_token is None:
																	_snake_case       :					Union[str, Any]				       =   tokens.replace(__snake_case ,						""				)
												else:
																	_snake_case       :					Optional[Any]				       =   end_token.group()
																	_snake_case       :					List[str]				       =   re.escape(__snake_case				)
																	_snake_case       :					int				       =   re.escape(__snake_case				)
																	_snake_case       :					Tuple				       =   re.search(f"""{start_token_escaped}(.*?){end_token_escaped}""" ,						__snake_case ,						re.IGNORECASE				)
																	if content is not None:
																						_snake_case       :					Union[str, Any]				       =   content.group(1				).strip()
																						if r"<s_" in content and r"</s_" in content:  # non-leaf node
																											_snake_case       :					Dict				       =   self.tokenajson(__snake_case ,						is_inner_value=__snake_case ,						added_vocab=__snake_case				)
																											if value:
																																if len(__snake_case				) == 1:
																																					_snake_case       :					str				       =   value[0]
																																_snake_case       :					Optional[int]				       =   value
																						else:  # leaf nodes
																											_snake_case       :					Optional[Any]				       =   []
																											for leaf in content.split(r"<sep/>"				):
																																_snake_case       :					Optional[Any]				       =   leaf.strip()
																																if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																					_snake_case       :					List[str]				       =   leaf[1:-2]  # for categorical special tokens
																																output[key].append(__snake_case				)
																											if len(output[key]				) == 1:
																																_snake_case       :					Dict				       =   output[key][0]

																	_snake_case       :					str				       =   tokens[tokens.find(__snake_case				) + len(__snake_case				) :].strip()
																	if tokens[:6] == r"<sep/>":  # non-leaf nodes
																						return [output] + self.tokenajson(tokens[6:] ,						is_inner_value=__snake_case ,						added_vocab=__snake_case				)

							if len(__snake_case				):
												return [output] if is_inner_value else output
							else:
												return [] if is_inner_value else {"text_sequence": tokens}







		@property
		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							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        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,						__snake_case ,						)
							return self.image_processor






from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__lowerCAmelCase :Union[str, Any]     =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__lowerCAmelCase :Optional[Any]     =    typing.Union[np.floataa, int, float]  # noqa: UP007







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return np.sqrt(np.sum((np.asarray(UpperCAmelCase				) - np.asarray(UpperCAmelCase				)) ** 2				)				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return sum((va - va) ** 2 for va, va in zip(UpperCAmelCase  ,  UpperCAmelCase				)				) ** (1 / 2)


if __name__ == "__main__":







							def    A				(							):
												from timeit import timeit

												print("Without Numpy"				)
												print(
												    timeit(
												        "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)
												print("With Numpy"				)
												print(
												    timeit(
												        "euclidean_distance([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)

							benchmark()