

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 SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  @add_start_docstrings(lowerCAmelCase   )
  def __call__(      self			:   Dict	,							lowerCAmelCase			:   torch.LongTensor	,							lowerCAmelCase			:   torch.FloatTensor	,							**lowerCAmelCase			:   List[Any]   )							->     bool:






     """simple docstring"""







     raise NotImplementedError("""StoppingCriteria needs to be subclassed"""   )






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   Tuple	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Optional[int] = None   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						max_length
     __lowerCAmelCase  :      Union[str, Any]    =						max_position_embeddings







  @add_start_docstrings(lowerCAmelCase   )
  def __call__(      self			:   Optional[Any]	,							lowerCAmelCase			:   torch.LongTensor	,							lowerCAmelCase			:   torch.FloatTensor	,							**lowerCAmelCase			:   str   )							->     bool:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						input_ids.shape[-1]
     __lowerCAmelCase  :      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 SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   str	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   int   )							->     int:






     """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."""	,							lowerCAmelCase	,							)
     __lowerCAmelCase  :      List[str]    =						start_length
     __lowerCAmelCase  :      Optional[int]    =						max_new_tokens
     __lowerCAmelCase  :      List[str]    =						start_length + max_new_tokens







  @add_start_docstrings(lowerCAmelCase   )
  def __call__(      self			:   Optional[int]	,							lowerCAmelCase			:   torch.LongTensor	,							lowerCAmelCase			:   torch.FloatTensor	,							**lowerCAmelCase			:   Optional[int]   )							->     bool:






     """simple docstring"""







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






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   str	,							lowerCAmelCase			:   float	,							lowerCAmelCase			:   Optional[float] = None   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						max_time
     __lowerCAmelCase  :      Optional[Any]    =						time.time() if initial_timestamp is None else initial_timestamp







  @add_start_docstrings(lowerCAmelCase   )
  def __call__(      self			:   Tuple	,							lowerCAmelCase			:   torch.LongTensor	,							lowerCAmelCase			:   torch.FloatTensor	,							**lowerCAmelCase			:   Tuple   )							->     bool:






     """simple docstring"""







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






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  @add_start_docstrings(lowerCAmelCase   )
  def __call__(      self			:   Any	,							lowerCAmelCase			:   torch.LongTensor	,							lowerCAmelCase			:   torch.FloatTensor	,							**lowerCAmelCase			:   Optional[int]   )							->     bool:






     """simple docstring"""







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







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Optional[int]:






     """simple docstring"""







     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					:						StoppingCriteriaList ,       __A					:						int					)      ->     StoppingCriteriaList:
   __lowerCAmelCase  :      List[Any]    =						stopping_criteria.max_length
   __lowerCAmelCase  :      str    =						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





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Dict										=(EulerDiscreteScheduler,)
  lowerCamelCase   :      Dict										=10







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							**lowerCAmelCase			:   Union[str, Any]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						{
         """num_train_timesteps""": 11_00,
         """beta_start""": 0.0001,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
     }

     config.update(**lowerCAmelCase   )
     return config







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Tuple:






     """simple docstring"""







     for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,							[0.0002, 0.002, 0.02]   ):
        self.check_over_configs(beta_start=lowerCAmelCase	,							beta_end=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     str:






     """simple docstring"""







     for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[Any]:






     """simple docstring"""







     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      int    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Any    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      List[Any]    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Tuple    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      str    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						output.prev_sample

     __lowerCAmelCase  :      str    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      List[Any]    =						self.get_scheduler_config(prediction_type="""v_prediction"""   )
     __lowerCAmelCase  :      List[str]    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps   )

     __lowerCAmelCase  :      Any    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      int    =						self.dummy_model()
     __lowerCAmelCase  :      List[str]    =						self.dummy_sample_deter * scheduler.init_noise_sigma
     __lowerCAmelCase  :      List[Any]    =						sample.to(lowerCAmelCase   )

     for i, t in enumerate(scheduler.timesteps   ):
        __lowerCAmelCase  :      Any    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Any    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						output.prev_sample

     __lowerCAmelCase  :      List[str]    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Optional[int]    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 0.0002   ) < 1e-2
     assert abs(result_mean.item() - 2.2676e-06   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Dict    =						self.get_scheduler_config()
     __lowerCAmelCase  :      Optional[int]    =						scheduler_class(**lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps	,							device=lowerCAmelCase   )

     __lowerCAmelCase  :      Union[str, Any]    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      Dict    =						self.dummy_model()
     __lowerCAmelCase  :      Dict    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      Dict    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      Union[str, Any]    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      List[str]    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      int    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      Any    =						output.prev_sample

     __lowerCAmelCase  :      int    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      str    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 10.0807   ) < 1e-2
     assert abs(result_mean.item() - 0.0131   ) < 1e-3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.scheduler_classes[0]
     __lowerCAmelCase  :      Optional[int]    =						self.get_scheduler_config()
     __lowerCAmelCase  :      List[Any]    =						scheduler_class(**lowerCAmelCase	,							use_karras_sigmas=lowerCAmelCase   )

     scheduler.set_timesteps(self.num_inference_steps	,							device=lowerCAmelCase   )

     __lowerCAmelCase  :      str    =						torch.manual_seed(0   )

     __lowerCAmelCase  :      str    =						self.dummy_model()
     __lowerCAmelCase  :      str    =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __lowerCAmelCase  :      int    =						sample.to(lowerCAmelCase   )

     for t in scheduler.timesteps:
        __lowerCAmelCase  :      int    =						scheduler.scale_model_input(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							lowerCAmelCase   )

        __lowerCAmelCase  :      Tuple    =						scheduler.step(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							generator=lowerCAmelCase   )
        __lowerCAmelCase  :      List[Any]    =						output.prev_sample

     __lowerCAmelCase  :      Tuple    =						torch.sum(torch.abs(lowerCAmelCase   )   )
     __lowerCAmelCase  :      Any    =						torch.mean(torch.abs(lowerCAmelCase   )   )

     assert abs(result_sum.item() - 124.52_2994_9951_1719   ) < 1e-2
     assert abs(result_mean.item() - 0.1_6213_9326_3339_9963   ) < 1e-3


