





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class  A__       (	__snake_case      ):
      _UpperCAmelCase     :torch.FloatTensor
      _UpperCAmelCase     :torch.FloatTensor
      _UpperCAmelCase     :Optional[torch.FloatTensor]						   =       None



class  A__       (	__snake_case	,       __snake_case      ):
      _UpperCAmelCase     :Optional[Any]						   =       2





      @register_to_config
      def __init__(			self	,							A_ = 0.02	,							A_ = 100	,							A_ = 1.0_07	,							A_ = 80	,							A_ = 0.05	,							A_ = 50	,							):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       sigma_max

       # setable values
       UpperCamelCase     : int	     =       None
       UpperCamelCase     : np.IntTensor	     =       None
       UpperCamelCase     : torch.FloatTensor	     =       None  # sigma(t_i)





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       return sample





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       num_inference_steps
       UpperCamelCase     : Union[str, Any]	     =       np.arange(0	,							self.num_inference_steps       )[::-1].copy()
       UpperCamelCase     : int	     =       torch.from_numpy(A_       ).to(A_       )
       UpperCamelCase     : List[str]	     =       [
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       UpperCamelCase     : List[str]	     =       torch.tensor(A_	,							dtype=torch.floataa	,							device=A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_ = None       ):
       '''simple docstring'''




       if self.config.s_min <= sigma <= self.config.s_max:
        UpperCamelCase     : List[str]	     =       min(self.config.s_churn / self.num_inference_steps	,							2**0.5 - 1       )
       else:
        UpperCamelCase     : List[Any]	     =       0

       # sample eps ~ N(0, S_noise^2 * I)
       UpperCamelCase     : Union[str, Any]	     =       self.config.s_noise * randn_tensor(sample.shape	,							generator=A_       ).to(sample.device       )
       UpperCamelCase     : List[Any]	     =       sigma + gamma * sigma
       UpperCamelCase     : int	     =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_	,							A_ = True	,							):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       sample_hat + sigma_hat * model_output
       UpperCamelCase     : Any	     =       (sample_hat - pred_original_sample) / sigma_hat
       UpperCamelCase     : Tuple	     =       sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
        return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_	,							derivative=A_	,							pred_original_sample=A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_ = True	,							):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       sample_prev + sigma_prev * model_output
       UpperCamelCase     : str	     =       (sample_prev - pred_original_sample) / sigma_prev
       UpperCamelCase     : str	     =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
        return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_	,							derivative=A_	,							pred_original_sample=A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       raise NotImplementedError()







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	:					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  A__       (	__snake_case      ):





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       raise NotImplementedError("StoppingCriteria needs to be subclassed"       )



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       max_length
       UpperCamelCase     : Dict	     =       max_position_embeddings





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       input_ids.shape[-1]
       UpperCamelCase     : str	     =       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__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_       ):
       '''simple docstring'''




       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_	,							)
       UpperCamelCase     : Union[str, Any]	     =       start_length
       UpperCamelCase     : List[str]	     =       max_new_tokens
       UpperCamelCase     : Tuple	     =       start_length + max_new_tokens





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return input_ids.shape[-1] >= self.max_length



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       max_time
       UpperCamelCase     : Dict	     =       time.time() if initial_timestamp is None else initial_timestamp





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return time.time() - self.initial_timestamp > self.max_time



class  A__       (	__snake_case      ):





      @add_start_docstrings(A_       )
      def __call__(			self	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       return any(criteria(A_	,							A_       ) for criteria in self       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       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 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			StoppingCriteriaList:
 UpperCamelCase     : Tuple	     =       stopping_criteria.max_length
 UpperCamelCase     : Union[str, Any]	     =       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
