
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_  :	Dict 				=					logging.get_logger(__name__)


lowerCamelCase_  :	Dict 				=					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(snake_case_ )
			def __call__(    self	:    Union[str, Any]  ,		snake_case_	:    torch.LongTensor  ,		snake_case_	:    torch.FloatTensor  ,		**snake_case_	:    Union[str, Any] ):
							raise NotImplementedError("""StoppingCriteria needs to be subclassed""" )




class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			def __init__(    self	:    Optional[int]  ,		snake_case_	:    int  ,		snake_case_	:    Optional[int] = None ):
							UpperCamelCase_:							Optional[Any]		=  max_length
							UpperCamelCase_:							Any		=  max_position_embeddings




			@add_start_docstrings(snake_case_ )
			def __call__(    self	:    int  ,		snake_case_	:    torch.LongTensor  ,		snake_case_	:    torch.FloatTensor  ,		**snake_case_	:    List[str] ):
							UpperCamelCase_:							Optional[int]		=  input_ids.shape[-1]
							UpperCamelCase_:							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     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			def __init__(    self	:    Optional[int]  ,		snake_case_	:    int  ,		snake_case_	:    int ):
							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_  ,		)
							UpperCamelCase_:							str		=  start_length
							UpperCamelCase_:							Optional[Any]		=  max_new_tokens
							UpperCamelCase_:							Optional[Any]		=  start_length + max_new_tokens




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




class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			def __init__(    self	:    Any  ,		snake_case_	:    float  ,		snake_case_	:    Optional[float] = None ):
							UpperCamelCase_:							Optional[Any]		=  max_time
							UpperCamelCase_:							Dict		=  time.time() if initial_timestamp is None else initial_timestamp




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




class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			@add_start_docstrings(snake_case_ )
			def __call__(    self	:    List[str]  ,		snake_case_	:    torch.LongTensor  ,		snake_case_	:    torch.FloatTensor  ,		**snake_case_	:    List[str] ):
							return any(criteria(snake_case_  ,		snake_case_ ) for criteria in self )




			@property
			def  lowerCAmelCase__		(    self	:    Any ):
							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     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		StoppingCriteriaList:
				UpperCamelCase_:							Tuple		=  stopping_criteria.max_length
				UpperCamelCase_:							Optional[int]		=  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








def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		float:
				if mass < 0:
								raise ValueError("""The mass of a body cannot be negative"""  )
				return 0.5 * mass * abs(lowerCamelCase  ) * abs(lowerCamelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)






