

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


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


UpperCAmelCase_							:					Union[str, 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      lowerCamelCase_      (	_lowercase	):




	@add_start_docstrings(__A							)
	def __call__(     self						:     str					,      __A						:     torch.LongTensor					,      __A						:     torch.FloatTensor					,      **__A						:     Optional[Any]							):
		raise NotImplementedError("""StoppingCriteria needs to be subclassed"""							)






class      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     Union[str, Any]					,      __A						:     int					,      __A						:     Optional[int] = None							):
		__A :			Optional[int]		    =			max_length
		__A :			Optional[int]		    =			max_position_embeddings




	@add_start_docstrings(__A							)
	def __call__(     self						:     Union[str, Any]					,      __A						:     torch.LongTensor					,      __A						:     torch.FloatTensor					,      **__A						:     Optional[int]							):
		__A :			Optional[Any]		    =			input_ids.shape[-1]
		__A :			Union[str, 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      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     List[str]					,      __A						:     int					,      __A						:     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."""					,      __A					,      )
		__A :			Dict		    =			start_length
		__A :			Optional[int]		    =			max_new_tokens
		__A :			Tuple		    =			start_length + max_new_tokens




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






class      lowerCamelCase_      (	_lowercase	):




	def __init__(     self						:     int					,      __A						:     float					,      __A						:     Optional[float] = None							):
		__A :			Optional[int]		    =			max_time
		__A :			int		    =			time.time() if initial_timestamp is None else initial_timestamp




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






class      lowerCamelCase_      (	_lowercase	):




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




	@property
	def        lowerCAmelCase_       (     self						:     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 							__SCREAMING_SNAKE_CASE     (   a__    :   StoppingCriteriaList			,a__    :   int       )					->				StoppingCriteriaList:
	__A :			int		    =			stopping_criteria.max_length
	__A :			Optional[int]		    =			deepcopy(a__       )
	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"""			,a__       )
	elif stopping_max_length is None:
		new_stopping_criteria.append(MaxLengthCriteria(max_length=a__       )       )
	return new_stopping_criteria








from __future__ import annotations
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]			,a__    :   Dict			,a__    :   Union[str, Any]			,a__    :   Any       )					->				Optional[int]:  # noqa: E741
	while r - l > 1:
		__A :			Any		    =			(l + r) // 2
		if v[m] >= key:
			__A :			Optional[int]		    =			m
		else:
			__A :			List[Any]		    =			m  # noqa: E741
	return r
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]       )					->				int:
	if len(a__       ) == 0:
		return 0

	__A :			str		    =			[0] * len(a__       )
	__A :			List[str]		    =			1

	__A :			List[Any]		    =			v[0]

	for i in range(1			,len(a__       )       ):
		if v[i] < tail[0]:
			__A :			int		    =			v[i]
		elif v[i] > tail[length - 1]:
			__A :			Union[str, Any]		    =			v[i]
			length += 1
		else:
			__A :			Any		    =			v[i]

	return length


if __name__ == "__main__":
			import doctest

			doctest.testmod()





