





'''simple docstring'''

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


__lowerCamelCase : List[str]											=			logging.get_logger(__name__)


__lowerCamelCase : List[Any]											=			r"\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"

class       UpperCAmelCase (						_lowercase  ):




		@add_start_docstrings(A__				)
		def __call__(self      :	int							,			A__      :	torch.LongTensor							,			A__      :	torch.FloatTensor							,			**A__      :	int				)					-> bool:
						raise NotImplementedError("StoppingCriteria needs to be subclassed"				)




class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Dict							,			A__      :	int							,			A__      :	Optional[int] = None				)					-> str:
						lowercase         = max_length
						lowercase         = max_position_embeddings





		@add_start_docstrings(A__				)
		def __call__(self      :	Tuple							,			A__      :	torch.LongTensor							,			A__      :	torch.FloatTensor							,			**A__      :	Optional[int]				)					-> bool:
						lowercase         = input_ids.shape[-1]
						lowercase         = 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 (						_lowercase  ):




		def __init__(self      :	int							,			A__      :	int							,			A__      :	int				)					-> Optional[Any]:
						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__							,			)
						lowercase         = start_length
						lowercase         = max_new_tokens
						lowercase         = start_length + max_new_tokens





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




class       UpperCAmelCase (						_lowercase  ):




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





		@add_start_docstrings(A__				)
		def __call__(self      :	List[Any]							,			A__      :	torch.LongTensor							,			A__      :	torch.FloatTensor							,			**A__      :	Dict				)					-> bool:
						return time.time() - self.initial_timestamp > self.max_time




class       UpperCAmelCase (						_lowercase  ):




		@add_start_docstrings(A__				)
		def __call__(self      :	str							,			A__      :	torch.LongTensor							,			A__      :	torch.FloatTensor							,			**A__      :	Tuple				)					-> bool:
						return any(criteria(A__							,			A__				) for criteria in self				)





		@property
		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Optional[int]:
						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 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = stopping_criteria.max_length
				lowercase         = deepcopy(lowerCAmelCase_							)
				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"		,							lowerCAmelCase_							)
				elif stopping_max_length is None:
								new_stopping_criteria.append(MaxLengthCriteria(max_length=lowerCAmelCase_							)							)
				return new_stopping_criteria











'''simple docstring'''

__lowerCamelCase : str											=			8.314_4598







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				if temperature < 0:
								raise Exception("Temperature cannot be less than 0 K"							)
				if molar_mass <= 0:
								raise Exception("Molar mass cannot be less than or equal to 0 kg/mol"							)
				else:
								return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
			import doctest

			# run doctest
			doctest.testmod()

			# example
			__lowerCamelCase : Optional[Any]											=			300
			__lowerCamelCase : Tuple											=			28
			__lowerCamelCase : str											=			rms_speed_of_molecule(temperature, molar_mass)
			print(f"Vrms of Nitrogen gas at 300 K is {vrms} m/s")




