


import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)




def 	a_      (    SCREAMING_SNAKE_CASE__					:      str     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		=torch.exp(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Optional[Any]		=torch.sum(SCREAMING_SNAKE_CASE__		,			dim=1     )  # sum of exp(x_i)
       _lowerCamelCase       :		Optional[Any]		=torch.sum(x * exp_x		,			dim=1     )  # sum of x_i * exp(x_i)
       return torch.log(SCREAMING_SNAKE_CASE__     ) - B / A




class A  (   nn.Module     ):






      def __init__(					self						:     Any			,   lowercase_						:     Dict )					->						Tuple:
             """simple docstring"""




             super().__init__()
             _lowerCamelCase       :		Optional[int]		=config.output_attentions
             _lowerCamelCase       :		List[Any]		=config.output_hidden_states
             _lowerCamelCase       :		Tuple		=nn.ModuleList([BertLayer(lowercase_ ) for _ in range(config.num_hidden_layers )] )
             _lowerCamelCase       :		Union[str, Any]		=nn.ModuleList([BertHighway(lowercase_ ) for _ in range(config.num_hidden_layers )] )

             _lowerCamelCase       :		str		=[-1 for _ in range(config.num_hidden_layers )]






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     Optional[int] )					->						str:
             """simple docstring"""




             if (type(lowercase_ ) is float) or (type(lowercase_ ) is int):
                    for i in range(len(self.early_exit_entropy ) ):
                           _lowerCamelCase       :		Dict		=x
             else:
                    _lowerCamelCase       :		List[str]		=x






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Tuple )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=pooler.state_dict()
             for highway in self.highway:
                    for name, param in highway.pooler.state_dict().items():
                           param.copy_(loaded_model[name] )






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Optional[int]=None			,   lowercase_						:     Any=None			,   lowercase_						:     Optional[Any]=None			,   lowercase_						:     str=None			,   )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=()
             _lowerCamelCase       :		List[str]		=()
             _lowerCamelCase       :		int		=()
             for i, layer_module in enumerate(self.layer ):
                    if self.output_hidden_states:
                           _lowerCamelCase       :		List[Any]		=all_hidden_states + (hidden_states,)

                    _lowerCamelCase       :		int		=layer_module(
                        lowercase_			,   lowercase_			,   head_mask[i]			,   lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=layer_outputs[0]

                    if self.output_attentions:
                           _lowerCamelCase       :		Union[str, Any]		=all_attentions + (layer_outputs[1],)

                    _lowerCamelCase       :		int		=(hidden_states,)
                    if self.output_hidden_states:
                           _lowerCamelCase       :		List[str]		=current_outputs + (all_hidden_states,)
                    if self.output_attentions:
                           _lowerCamelCase       :		Optional[int]		=current_outputs + (all_attentions,)

                    _lowerCamelCase       :		Tuple		=self.highway[i](lowercase_ )
                    # logits, pooled_output

                    if not self.training:
                           _lowerCamelCase       :		Any		=highway_exit[0]
                           _lowerCamelCase       :		str		=entropy(lowercase_ )
                           _lowerCamelCase       :		Optional[Any]		=highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                           _lowerCamelCase       :		Optional[int]		=all_highway_exits + (highway_exit,)

                           if highway_entropy < self.early_exit_entropy[i]:
                                  _lowerCamelCase       :		str		=(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                                  raise HighwayException(lowercase_			,   i + 1 )
                    else:
                           _lowerCamelCase       :		Any		=all_highway_exits + (highway_exit,)

        # Add last layer
             if self.output_hidden_states:
                    _lowerCamelCase       :		Optional[int]		=all_hidden_states + (hidden_states,)

             _lowerCamelCase       :		Dict		=(hidden_states,)
             if self.output_hidden_states:
                    _lowerCamelCase       :		Dict		=outputs + (all_hidden_states,)
             if self.output_attentions:
                    _lowerCamelCase       :		List[Any]		=outputs + (all_attentions,)

             _lowerCamelCase       :		int		=outputs + (all_highway_exits,)
             return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits




@add_start_docstrings(
    'The Bert Model transformer with early exiting (DeeBERT). '    ,							UpperCamelCase_    ,							)
class A  (   UpperCamelCase_     ):






      def __init__(					self						:     Tuple			,   lowercase_						:     Union[str, Any] )					->						List[Any]:
             """simple docstring"""




             super().__init__(lowercase_ )
             _lowerCamelCase       :		str		=config

             _lowerCamelCase       :		Any		=BertEmbeddings(lowercase_ )
             _lowerCamelCase       :		Tuple		=DeeBertEncoder(lowercase_ )
             _lowerCamelCase       :		str		=BertPooler(lowercase_ )

             self.init_weights()






      def        lowerCamelCase       (					self						:     Tuple )					->						Dict:
             """simple docstring"""




             self.encoder.init_highway_pooler(self.pooler )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             return self.embeddings.word_embeddings






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     Optional[Any] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=value






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     List[Any] )					->						List[Any]:
             """simple docstring"""




             for layer, heads in heads_to_prune.items():
                    self.encoder.layer[layer].attention.prune_heads(lowercase_ )






      @add_start_docstrings_to_model_forward(lowercase_ )
      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     int=None			,   lowercase_						:     List[str]=None			,   lowercase_						:     int=None			,   lowercase_						:     Union[str, Any]=None			,   lowercase_						:     Dict=None			,   lowercase_						:     Any=None			,   lowercase_						:     Optional[Any]=None			,   lowercase_						:     Union[str, Any]=None			,   )					->						Any:
             """simple docstring"""




             if input_ids is not None and inputs_embeds is not None:
                    raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
             elif input_ids is not None:
                    _lowerCamelCase       :		Tuple		=input_ids.size()
             elif inputs_embeds is not None:
                    _lowerCamelCase       :		List[str]		=inputs_embeds.size()[:-1]
             else:
                    raise ValueError('You have to specify either input_ids or inputs_embeds' )

             _lowerCamelCase       :		List[Any]		=input_ids.device if input_ids is not None else inputs_embeds.device

             if attention_mask is None:
                    _lowerCamelCase       :		int		=torch.ones(lowercase_			,   device=lowercase_ )
             if encoder_attention_mask is None:
                    _lowerCamelCase       :		Dict		=torch.ones(lowercase_			,   device=lowercase_ )
             if token_type_ids is None:
                    _lowerCamelCase       :		Any		=torch.zeros(lowercase_			,   dtype=torch.long			,   device=lowercase_ )

             # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
             # ourselves in which case we just need to make it broadcastable to all heads.
             _lowerCamelCase       :		torch.Tensor		=self.get_extended_attention_mask(lowercase_			,   lowercase_			,   lowercase_ )

             # If a 2D ou 3D attention mask is provided for the cross-attention
             # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
             if encoder_attention_mask.dim() == 3:
                    _lowerCamelCase       :		Dict		=encoder_attention_mask[:, None, :, :]
             if encoder_attention_mask.dim() == 2:
                    _lowerCamelCase       :		str		=encoder_attention_mask[:, None, None, :]

             _lowerCamelCase       :		Optional[int]		=encoder_extended_attention_mask.to(
                 dtype=next(self.parameters() ).dtype )  # fp16 compatibility
             _lowerCamelCase       :		int		=(1.0 - encoder_extended_attention_mask) * -10000.0

             # Prepare head mask if needed
             # 1.0 in head_mask indicate we keep the head
             # attention_probs has shape bsz x n_heads x N x N
             # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
             # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
             _lowerCamelCase       :		List[Any]		=self.get_head_mask(lowercase_			,   self.config.num_hidden_layers )

             _lowerCamelCase       :		Tuple		=self.embeddings(
                 input_ids=lowercase_			,   position_ids=lowercase_			,   token_type_ids=lowercase_			,   inputs_embeds=lowercase_ )
             _lowerCamelCase       :		List[str]		=self.encoder(
                 lowercase_			,   attention_mask=lowercase_			,   head_mask=lowercase_			,   encoder_hidden_states=lowercase_			,   encoder_attention_mask=lowercase_			,   )
             _lowerCamelCase       :		Optional[Any]		=encoder_outputs[0]
             _lowerCamelCase       :		Optional[int]		=self.pooler(lowercase_ )

             _lowerCamelCase       :		Tuple		=(
                 sequence_output,
                 pooled_output,
             ) + encoder_outputs[
                 1:
             ]  # add hidden_states and attentions if they are here
             return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits




class A  (   UpperCamelCase_     ):






      def __init__(					self						:     Dict			,   lowercase_						:     List[Any]			,   lowercase_						:     str )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Any		=message
             _lowerCamelCase       :		Union[str, Any]		=exit_layer  # start from 1!




class A  (   nn.Module     ):






      def __init__(					self						:     Optional[Any]			,   lowercase_						:     Optional[Any] )					->						Optional[int]:
             """simple docstring"""




             super().__init__()
             _lowerCamelCase       :		Any		=BertPooler(lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=nn.Dropout(config.hidden_dropout_prob )
             _lowerCamelCase       :		List[Any]		=nn.Linear(config.hidden_size			,   config.num_labels )






      def        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     List[Any] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Any		=encoder_outputs[0]
             _lowerCamelCase       :		List[str]		=self.pooler(lowercase_ )
             # "return" pooler_output

             # BertModel
             _lowerCamelCase       :		Optional[Any]		=(pooler_input, pooler_output) + encoder_outputs[1:]
             # "return" bmodel_output

             # Dropout and classification
             _lowerCamelCase       :		Dict		=bmodel_output[1]

             _lowerCamelCase       :		Tuple		=self.dropout(lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=self.classifier(lowercase_ )

             return logits, pooled_output




@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '    ,							UpperCamelCase_    ,							)
class A  (   UpperCamelCase_     ):






      def __init__(					self						:     str			,   lowercase_						:     List[Any] )					->						Any:
             """simple docstring"""




             super().__init__(lowercase_ )
             _lowerCamelCase       :		int		=config.num_labels
             _lowerCamelCase       :		Tuple		=config.num_hidden_layers

             _lowerCamelCase       :		List[str]		=DeeBertModel(lowercase_ )
             _lowerCamelCase       :		Union[str, Any]		=nn.Dropout(config.hidden_dropout_prob )
             _lowerCamelCase       :		List[Any]		=nn.Linear(config.hidden_size			,   self.config.num_labels )

             self.init_weights()






      @add_start_docstrings_to_model_forward(lowercase_ )
      def        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     List[str]=None			,   lowercase_						:     Tuple=None			,   lowercase_						:     Any=None			,   lowercase_						:     Optional[Any]=None			,   lowercase_						:     int=None			,   lowercase_						:     Optional[Any]=None			,   lowercase_						:     Any=None			,   lowercase_						:     Union[str, Any]=-1			,   lowercase_						:     Union[str, Any]=False			,   )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=self.num_layers
             try:
                    _lowerCamelCase       :		Union[str, Any]		=self.bert(
                        lowercase_			,   attention_mask=lowercase_			,   token_type_ids=lowercase_			,   position_ids=lowercase_			,   head_mask=lowercase_			,   inputs_embeds=lowercase_			,   )
                    # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

                    _lowerCamelCase       :		Optional[Any]		=outputs[1]

                    _lowerCamelCase       :		int		=self.dropout(lowercase_ )
                    _lowerCamelCase       :		int		=self.classifier(lowercase_ )
                    _lowerCamelCase       :		Dict		=(logits,) + outputs[2:]  # add hidden states and attention if they are here
             except HighwayException as e:
                    _lowerCamelCase       :		Optional[Any]		=e.message
                    _lowerCamelCase       :		str		=e.exit_layer
                    _lowerCamelCase       :		int		=outputs[0]

             if not self.training:
                    _lowerCamelCase       :		List[str]		=entropy(lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=[]
                    _lowerCamelCase       :		Any		=[]
             if labels is not None:
                    if self.num_labels == 1:
                           #  We are doing regression
                           _lowerCamelCase       :		Optional[Any]		=MSELoss()
                           _lowerCamelCase       :		Dict		=loss_fct(logits.view(-1 )			,   labels.view(-1 ) )
                    else:
                           _lowerCamelCase       :		int		=CrossEntropyLoss()
                           _lowerCamelCase       :		List[str]		=loss_fct(logits.view(-1			,   self.num_labels )			,   labels.view(-1 ) )

                    # work with highway exits
                    _lowerCamelCase       :		Optional[Any]		=[]
                    for highway_exit in outputs[-1]:
                           _lowerCamelCase       :		str		=highway_exit[0]
                           if not self.training:
                                  highway_logits_all.append(lowercase_ )
                                  highway_entropy.append(highway_exit[2] )
                           if self.num_labels == 1:
                                  #  We are doing regression
                                  _lowerCamelCase       :		Union[str, Any]		=MSELoss()
                                  _lowerCamelCase       :		Dict		=loss_fct(highway_logits.view(-1 )			,   labels.view(-1 ) )
                           else:
                                  _lowerCamelCase       :		Tuple		=CrossEntropyLoss()
                                  _lowerCamelCase       :		Dict		=loss_fct(highway_logits.view(-1			,   self.num_labels )			,   labels.view(-1 ) )
                           highway_losses.append(lowercase_ )

                    if train_highway:
                           _lowerCamelCase       :		List[str]		=(sum(highway_losses[:-1] ),) + outputs
                           # exclude the final highway, of course
                    else:
                           _lowerCamelCase       :		Union[str, Any]		=(loss,) + outputs
             if not self.training:
                    _lowerCamelCase       :		str		=outputs + ((original_entropy, highway_entropy), exit_layer)
                    if output_layer >= 0:
                           _lowerCamelCase       :		Union[str, Any]		=(
                               (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                           )  # use the highway of the last layer

             return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Any      =(DPMSolverSinglestepScheduler,)
      UpperCamelCase__							:  Tuple      =(('num_inference_steps', 25),)






      def        lowerCamelCase       (					self						:     Optional[Any]			,   **lowercase_						:     List[Any] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		={
                 'num_train_timesteps': 1000,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'solver_order': 2,
                 'prediction_type': 'epsilon',
                 'thresholding': False,
                 'sample_max_value': 1.0,
                 'algorithm_type': 'dpmsolver++',
                 'solver_type': 'midpoint',
                 'lambda_min_clipped': -float('inf' ),
                 'variance_type': None,
             }

             config.update(**lowercase_ )
             return config






      def        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     Optional[Any]=0			,   **lowercase_						:     Any )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=dict(self.forward_default_kwargs )
             _lowerCamelCase       :		Optional[Any]		=kwargs.pop('num_inference_steps'			,   lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.dummy_sample
             _lowerCamelCase       :		Dict		=0.1 * sample
             _lowerCamelCase       :		Optional[int]		=[residual + 0.2, residual + 0.15, residual + 0.10]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase       :		int		=self.get_scheduler_config(**lowercase_ )
                    _lowerCamelCase       :		str		=scheduler_class(**lowercase_ )
                    scheduler.set_timesteps(lowercase_ )
                    # copy over dummy past residuals
                    _lowerCamelCase       :		int		=dummy_past_residuals[: scheduler.config.solver_order]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_ )
                           _lowerCamelCase       :		Dict		=scheduler_class.from_pretrained(lowercase_ )
                           new_scheduler.set_timesteps(lowercase_ )
                           # copy over dummy past residuals
                           _lowerCamelCase       :		str		=dummy_past_residuals[: new_scheduler.config.solver_order]

                    _lowerCamelCase	,     _lowerCamelCase       :		Any		=sample, sample
                    for t in range(lowercase_			,   time_step + scheduler.config.solver_order + 1 ):
                           _lowerCamelCase       :		List[str]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample
                           _lowerCamelCase       :		Optional[Any]		=new_scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample

                           assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"






      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             pass






      def        lowerCamelCase       (					self						:     str			,   lowercase_						:     str=0			,   **lowercase_						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		int		=dict(self.forward_default_kwargs )
             _lowerCamelCase       :		Dict		=kwargs.pop('num_inference_steps'			,   lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.dummy_sample
             _lowerCamelCase       :		Any		=0.1 * sample
             _lowerCamelCase       :		int		=[residual + 0.2, residual + 0.15, residual + 0.10]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase       :		Optional[int]		=self.get_scheduler_config()
                    _lowerCamelCase       :		Any		=scheduler_class(**lowercase_ )
                    scheduler.set_timesteps(lowercase_ )

                    # copy over dummy past residuals (must be after setting timesteps)
                    _lowerCamelCase       :		Dict		=dummy_past_residuals[: scheduler.config.solver_order]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_ )
                           _lowerCamelCase       :		Optional[int]		=scheduler_class.from_pretrained(lowercase_ )
                           # copy over dummy past residuals
                           new_scheduler.set_timesteps(lowercase_ )

                           # copy over dummy past residual (must be after setting timesteps)
                           _lowerCamelCase       :		str		=dummy_past_residuals[: new_scheduler.config.solver_order]

                    _lowerCamelCase       :		Dict		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample
                    _lowerCamelCase       :		Dict		=new_scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample

                    assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Union[str, Any]=None			,   **lowercase_						:     List[str] )					->						str:
             """simple docstring"""




             if scheduler is None:
                    _lowerCamelCase       :		Tuple		=self.scheduler_classes[0]
                    _lowerCamelCase       :		Optional[int]		=self.get_scheduler_config(**lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		List[Any]		=self.scheduler_classes[0]
             _lowerCamelCase       :		Optional[Any]		=self.get_scheduler_config(**lowercase_ )
             _lowerCamelCase       :		List[Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		str		=10
             _lowerCamelCase       :		Union[str, Any]		=self.dummy_model()
             _lowerCamelCase       :		Dict		=self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_ )

             for i, t in enumerate(scheduler.timesteps ):
                    _lowerCamelCase       :		List[str]		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		Optional[int]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             return sample






      def        lowerCamelCase       (					self						:     Dict )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Any		=DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
             _lowerCamelCase       :		int		=50
             _lowerCamelCase       :		Optional[int]		=self.dummy_model()
             _lowerCamelCase       :		Tuple		=self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_ )

             # make sure that the first t is uneven
             for i, t in enumerate(scheduler.timesteps[3:] ):
                    _lowerCamelCase       :		Any		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		List[str]		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             _lowerCamelCase       :		int		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2574 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						List[str]:
             """simple docstring"""




             for timesteps in [25, 50, 100, 999, 1000]:
                    self.check_over_configs(num_train_timesteps=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
             _lowerCamelCase       :		Optional[Any]		=self.full_loop(scheduler=lowercase_ )
             _lowerCamelCase       :		Tuple		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3

             _lowerCamelCase       :		Dict		=DEISMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		Optional[int]		=DPMSolverMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		Union[str, Any]		=UniPCMultistepScheduler.from_config(scheduler.config )
             _lowerCamelCase       :		List[str]		=DPMSolverSinglestepScheduler.from_config(scheduler.config )

             _lowerCamelCase       :		List[Any]		=self.full_loop(scheduler=lowercase_ )
             _lowerCamelCase       :		List[str]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3






      def        lowerCamelCase       (					self						:     str )					->						Dict:
             """simple docstring"""




             self.check_over_configs(thresholding=lowercase_ )
             for order in [1, 2, 3]:
                    for solver_type in ["midpoint", "heun"]:
                           for threshold in [0.5, 1.0, 2.0]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             thresholding=lowercase_			,   prediction_type=lowercase_			,   sample_max_value=lowercase_			,   algorithm_type='dpmsolver++'			,   solver_order=lowercase_			,   solver_type=lowercase_			,   )






      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     List[str] )					->						Optional[int]:
             """simple docstring"""




             for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                    for solver_type in ["midpoint", "heun"]:
                           for order in [1, 2, 3]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         self.check_over_configs(
                                             solver_order=lowercase_			,   solver_type=lowercase_			,   prediction_type=lowercase_			,   algorithm_type=lowercase_			,   )
                                         _lowerCamelCase       :		Any		=self.full_loop(
                                             solver_order=lowercase_			,   solver_type=lowercase_			,   prediction_type=lowercase_			,   algorithm_type=lowercase_			,   )
                                         assert not torch.isnan(lowercase_ ).any(), "Samples have nan numbers"






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[Any]:
             """simple docstring"""




             self.check_over_configs(lower_order_final=lowercase_ )
             self.check_over_configs(lower_order_final=lowercase_ )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						int:
             """simple docstring"""




             self.check_over_configs(lambda_min_clipped=-float('inf' ) )
             self.check_over_configs(lambda_min_clipped=-5.1 )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




             self.check_over_configs(variance_type=lowercase_ )
             self.check_over_configs(variance_type='learned_range' )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Any:
             """simple docstring"""




             for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                    self.check_over_forward(num_inference_steps=lowercase_			,   time_step=0 )






      def        lowerCamelCase       (					self						:     str )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=self.full_loop()
             _lowerCamelCase       :		Dict		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2791 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.full_loop(use_karras_sigmas=lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.2248 ) < 1E-3






      def        lowerCamelCase       (					self						:     Dict )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Any		=self.full_loop(prediction_type='v_prediction' )
             _lowerCamelCase       :		Dict		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.1453 ) < 1E-3






      def        lowerCamelCase       (					self						:     Any )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.full_loop(prediction_type='v_prediction'			,   use_karras_sigmas=lowercase_ )
             _lowerCamelCase       :		List[str]		=torch.mean(torch.abs(lowercase_ ) )

             assert abs(result_mean.item() - 0.0649 ) < 1E-3






      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.scheduler_classes[0]
             _lowerCamelCase       :		List[str]		=self.get_scheduler_config(thresholding=lowercase_			,   dynamic_thresholding_ratio=0 )
             _lowerCamelCase       :		List[Any]		=scheduler_class(**lowercase_ )

             _lowerCamelCase       :		Optional[Any]		=10
             _lowerCamelCase       :		Optional[int]		=self.dummy_model()
             _lowerCamelCase       :		List[Any]		=self.dummy_sample_deter.half()
             scheduler.set_timesteps(lowercase_ )

             for i, t in enumerate(scheduler.timesteps ):
                    _lowerCamelCase       :		str		=model(lowercase_			,   lowercase_ )
                    _lowerCamelCase       :		Any		=scheduler.step(lowercase_			,   lowercase_			,   lowercase_ ).prev_sample

             assert sample.dtype == torch.floataa


