


'''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


__UpperCAmelCase       =							logging.get_logger(__name__)


__UpperCAmelCase       =							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 		a__    (							a__  ):





							'''simple docstring'''






							@add_start_docstrings(lowerCamelCase_       )
							def __call__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							bool:
														raise NotImplementedError('''StoppingCriteria needs to be subclassed'''       )




class 		a__    (							a__  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     max_length
														lowerCAmelCase__	       =     max_position_embeddings







							@add_start_docstrings(lowerCamelCase_       )
							def __call__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							bool:
														lowerCAmelCase__	       =     input_ids.shape[-1]
														lowerCAmelCase__	       =     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 		a__    (							a__  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							List[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.''' ,   lowerCamelCase_ ,   )
														lowerCAmelCase__	       =     start_length
														lowerCAmelCase__	       =     max_new_tokens
														lowerCAmelCase__	       =     start_length + max_new_tokens







							@add_start_docstrings(lowerCamelCase_       )
							def __call__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							bool:
														return input_ids.shape[-1] >= self.max_length




class 		a__    (							a__  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     max_time
														lowerCAmelCase__	       =     time.time() if initial_timestamp is None else initial_timestamp







							@add_start_docstrings(lowerCamelCase_       )
							def __call__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							bool:
														return time.time() - self.initial_timestamp > self.max_time




class 		a__    (							a__  ):





							'''simple docstring'''






							@add_start_docstrings(lowerCamelCase_       )
							def __call__(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       )	->							bool:
														return any(criteria(lowerCamelCase_ ,   lowerCamelCase_       ) for criteria in self       )







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														for stopping_criterium in self:
																					if isinstance(lowerCamelCase_ ,   lowerCamelCase_       ):
																												return stopping_criterium.max_length
																					elif isinstance(lowerCamelCase_ ,   lowerCamelCase_       ):
																												return stopping_criterium.max_length
														return None


def  _snake_case       (					A			,   A	)						->   StoppingCriteriaList:
							lowerCAmelCase__	       =     stopping_criteria.max_length
							lowerCAmelCase__	       =     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



'''simple docstring'''






from __future__ import annotations




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     order

														# a_{0} ... a_{k}
														lowerCAmelCase__	       =     [1.0] + [0.0] * order
														# b_{0} ... b_{k}
														lowerCAmelCase__	       =     [1.0] + [0.0] * order

														# x[n-1] ... x[n-k]
														lowerCAmelCase__	       =     [0.0] * self.order
														# y[n-1] ... y[n-k]
														lowerCAmelCase__	       =     [0.0] * self.order







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if len(lowerCamelCase_       ) < self.order:
																					lowerCAmelCase__	       =     [1.0, *a_coeffs]

														if len(lowerCamelCase_       ) != self.order + 1:
																					lowerCAmelCase__	       =     (
																					    F"""Expected a_coeffs to have {self.order + 1} elements """
																					    F"""for {self.order}-order filter, got {len(lowerCamelCase_       )}"""
																					)
																					raise ValueError(lowerCamelCase_       )

														if len(lowerCamelCase_       ) != self.order + 1:
																					lowerCAmelCase__	       =     (
																					    F"""Expected b_coeffs to have {self.order + 1} elements """
																					    F"""for {self.order}-order filter, got {len(lowerCamelCase_       )}"""
																					)
																					raise ValueError(lowerCamelCase_       )

														lowerCAmelCase__	       =     a_coeffs
														lowerCAmelCase__	       =     b_coeffs







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							float:
														lowerCAmelCase__	       =     0.0

														# Start at index 1 and do index 0 at the end.
														for i in range(1 ,   self.order + 1       ):
																					result += (
																					    self.b_coeffs[i] * self.input_history[i - 1]
																					    - self.a_coeffs[i] * self.output_history[i - 1]
																					)

														lowerCAmelCase__	       =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

														lowerCAmelCase__	       =     self.input_history[:-1]
														lowerCAmelCase__	       =     self.output_history[:-1]

														lowerCAmelCase__	       =     sample
														lowerCAmelCase__	       =     result

														return result