






"""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


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


A__	:    Dict			 = 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        lowercase__      (							a_       ):


			@add_start_docstrings(snake_case__ )
			def __call__(				self					:  Any       ,	snake_case__					:  str       ,	snake_case__					:  Any       ,	**snake_case__					:  Optional[Any] ):
										raise NotImplementedError("StoppingCriteria needs to be subclassed" )







class        lowercase__      (							a_       ):


			def __init__(				self					:  Optional[int]       ,	snake_case__					:  str       ,	snake_case__					:  List[Any] = None ):
										lowerCamelCase_	:							Optional[int]									=max_length
										lowerCamelCase_	:							List[str]									=max_position_embeddings


			@add_start_docstrings(snake_case__ )
			def __call__(				self					:  Optional[int]       ,	snake_case__					:  Tuple       ,	snake_case__					:  Union[str, Any]       ,	**snake_case__					:  Dict ):
										lowerCamelCase_	:							Tuple									=input_ids.shape[-1]
										lowerCamelCase_	:							Optional[Any]									=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        lowercase__      (							a_       ):


			def __init__(				self					:  str       ,	snake_case__					:  str       ,	snake_case__					:  Tuple ):
										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."       ,	snake_case__       ,	)
										lowerCamelCase_	:							List[Any]									=start_length
										lowerCamelCase_	:							Any									=max_new_tokens
										lowerCamelCase_	:							Tuple									=start_length + max_new_tokens


			@add_start_docstrings(snake_case__ )
			def __call__(				self					:  List[str]       ,	snake_case__					:  List[str]       ,	snake_case__					:  Dict       ,	**snake_case__					:  int ):
										return input_ids.shape[-1] >= self.max_length







class        lowercase__      (							a_       ):


			def __init__(				self					:  List[Any]       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  int = None ):
										lowerCamelCase_	:							Optional[Any]									=max_time
										lowerCamelCase_	:							Tuple									=time.time() if initial_timestamp is None else initial_timestamp


			@add_start_docstrings(snake_case__ )
			def __call__(				self					:  str       ,	snake_case__					:  str       ,	snake_case__					:  Optional[Any]       ,	**snake_case__					:  Optional[Any] ):
										return time.time() - self.initial_timestamp > self.max_time







class        lowercase__      (							a_       ):


			@add_start_docstrings(snake_case__ )
			def __call__(				self					:  Tuple       ,	snake_case__					:  Any       ,	snake_case__					:  Union[str, Any]       ,	**snake_case__					:  List[Any] ):
										return any(criteria(snake_case__       ,	snake_case__ ) for criteria in self )


			@property
			def      UpperCAmelCase__						(				self					:  Optional[int] ):







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


def 			_snake_case    (					lowerCamelCase__     :   StoppingCriteriaList    ,						lowerCamelCase__     :   int  )    ->     str:
							lowerCamelCase_	:							str									=stopping_criteria.max_length
							lowerCamelCase_	:							Tuple									=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"""



from __future__ import annotations

from collections import namedtuple

def 			_snake_case    (					lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float  )    ->     tuple:
							lowerCamelCase_	:							Optional[Any]									=namedtuple("result"    ,						"name value"  )
							if (voltage, current, power).count(0  ) != 1:
														raise ValueError("Only one argument must be 0"  )
							elif power < 0:
														raise ValueError(
														    "Power cannot be negative in any electrical/electronics system"  )
							elif voltage == 0:
														return result("voltage"    ,						power / current  )
							elif current == 0:
														return result("current"    ,						power / voltage  )
							elif power == 0:
														return result("power"    ,						float(round(abs(voltage * current  )    ,						2  )  )  )
							else:
														raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



