




import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__A						:							Optional[int]						      =  logging.get_logger(__name__)


__A						:							List[Any]						      =  r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							@add_start_docstrings(a			)
							def __call__(					self       :	int       ,     a       :	torch.LongTensor       ,     a       :	torch.FloatTensor       ,     **a       :	Tuple			)			->					Optional[Any]:
										raise NotImplementedError("""StoppingCriteria needs to be subclassed"""			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def __init__(					self       :	int       ,     a       :	int       ,     a       :	Optional[int] = None			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					max_length
										SCREAMING_SNAKE_CASE           =					max_position_embeddings







							@add_start_docstrings(a			)
							def __call__(					self       :	Dict       ,     a       :	torch.LongTensor       ,     a       :	torch.FloatTensor       ,     **a       :	List[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					input_ids.shape[-1]
										SCREAMING_SNAKE_CASE           =					cur_len >= self.max_length
										if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
													logger.warning_once(
													    """This is a friendly reminder - the current text generation call will exceed the model\'s predefined """
													    f"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
													    """exceptions, performance degradation, or nothing at all."""			)
										return is_done





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def __init__(					self       :	List[Any]       ,     a       :	int       ,     a       :	int			)			->					str:
										warnings.warn(
										    """The class `MaxNewTokensCriteria` is deprecated. """
										    f"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
										    """with `max_length = start_length + max_new_tokens` instead."""       ,     a       ,     )
										SCREAMING_SNAKE_CASE           =					start_length
										SCREAMING_SNAKE_CASE           =					max_new_tokens
										SCREAMING_SNAKE_CASE           =					start_length + max_new_tokens







							@add_start_docstrings(a			)
							def __call__(					self       :	Any       ,     a       :	torch.LongTensor       ,     a       :	torch.FloatTensor       ,     **a       :	str			)			->					int:
										return input_ids.shape[-1] >= self.max_length





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def __init__(					self       :	Union[str, Any]       ,     a       :	float       ,     a       :	Optional[float] = None			)			->					Any:
										SCREAMING_SNAKE_CASE           =					max_time
										SCREAMING_SNAKE_CASE           =					time.time() if initial_timestamp is None else initial_timestamp







							@add_start_docstrings(a			)
							def __call__(					self       :	Optional[Any]       ,     a       :	torch.LongTensor       ,     a       :	torch.FloatTensor       ,     **a       :	str			)			->					Union[str, Any]:
										return time.time() - self.initial_timestamp > self.max_time





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							@add_start_docstrings(a			)
							def __call__(					self       :	str       ,     a       :	torch.LongTensor       ,     a       :	torch.FloatTensor       ,     **a       :	List[Any]			)			->					Optional[Any]:
										return any(criteria(a       ,     a			) for criteria in self			)







							@property
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					List[Any]:







										for stopping_criterium in self:
													if isinstance(a       ,     a			):
																return stopping_criterium.max_length
													elif isinstance(a       ,     a			):
																return stopping_criterium.max_length
										return None





def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					stopping_criteria.max_length
			SCREAMING_SNAKE_CASE           =					deepcopy(SCREAMING_SNAKE_CASE_   )
			if stopping_max_length is not None and stopping_max_length != max_length:
						warnings.warn("""You set different `max_length` for stopping criteria and `max_length` parameter"""					,	SCREAMING_SNAKE_CASE_   )
			elif stopping_max_length is None:
						new_stopping_criteria.append(MaxLengthCriteria(max_length=SCREAMING_SNAKE_CASE_   )   )
			return new_stopping_criteria












from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__A						:							Dict							=  typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__A						:							Tuple							=  typing.Union[np.floataa, int, float]  # noqa: UP007
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return np.sqrt(np.sum((np.asarray(SCREAMING_SNAKE_CASE   ) - np.asarray(SCREAMING_SNAKE_CASE   )) ** 2   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return sum((va - va) ** 2 for va, va in zip(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )   ) ** (1 / 2)


if __name__ == "__main__":
		def 					lowerCamelCase_    (    ):
					'''simple docstring'''
					from timeit import timeit

					print("""Without Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )
					print("""With Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )




		benchmark()






