"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		(DDIMParallelScheduler,)
     _UpperCamelCase							:      Optional[Any]		 =		(("eta", 0.0), ("num_inference_steps", 50))






     def    __A  (		self    ,					**a__  ):
       _lowerCAmelCase						:	Any        =					{
           """num_train_timesteps""": 1000,
           """beta_start""": 0.0_0_0_1,
           """beta_end""": 0.0_2,
           """beta_schedule""": """linear""",
           """clip_sample""": True,
       }

       config.update(**a__  )
       return config






     def    __A  (		self    ,					**a__  ):
       _lowerCAmelCase						:	Dict        =					self.scheduler_classes[0]
       _lowerCAmelCase						:	Optional[Any]        =					self.get_scheduler_config(**a__  )
       _lowerCAmelCase						:	Dict        =					scheduler_class(**a__  )

       _lowerCAmelCase						:	List[Any]        =					10, 0.0

       _lowerCAmelCase						:	Any        =					self.dummy_model()
       _lowerCAmelCase						:	Optional[Any]        =					self.dummy_sample_deter

       scheduler.set_timesteps(a__  )

       for t in scheduler.timesteps:
         _lowerCAmelCase						:	str        =					model(a__    ,					a__  )
         _lowerCAmelCase						:	Tuple        =					scheduler.step(a__    ,					a__    ,					a__    ,					a__  ).prev_sample

       return sample






     def    __A  (		self  ):
       for timesteps in [100, 500, 1000]:
         self.check_over_configs(num_train_timesteps=a__  )






     def    __A  (		self  ):
       for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=a__  )

       _lowerCAmelCase						:	Union[str, Any]        =					self.scheduler_classes[0]
       _lowerCAmelCase						:	Optional[Any]        =					self.get_scheduler_config(steps_offset=1  )
       _lowerCAmelCase						:	List[Any]        =					scheduler_class(**a__  )
       scheduler.set_timesteps(5  )
       assert torch.equal(scheduler.timesteps    ,					torch.LongTensor([801, 601, 401, 201, 1]  )  )






     def    __A  (		self  ):
       for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,					[0.0_0_2, 0.0_2, 0.2, 2]  ):
         self.check_over_configs(beta_start=a__    ,					beta_end=a__  )






     def    __A  (		self  ):
       for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=a__  )






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






     def    __A  (		self  ):
       for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=a__  )






     def    __A  (		self  ):
       for timestep_spacing in ["trailing", "leading"]:
         self.check_over_configs(timestep_spacing=a__  )






     def    __A  (		self  ):
       for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=a__  )






     def    __A  (		self  ):
       self.check_over_configs(thresholding=a__  )
       for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=a__    ,					prediction_type=a__    ,					sample_max_value=a__    ,					)






     def    __A  (		self  ):
       for t in [1, 10, 49]:
         self.check_over_forward(time_step=a__  )






     def    __A  (		self  ):
       for t, num_inference_steps in zip([1, 10, 50]    ,					[10, 50, 500]  ):
         self.check_over_forward(time_step=a__    ,					num_inference_steps=a__  )






     def    __A  (		self  ):
       for t, eta in zip([1, 10, 49]    ,					[0.0, 0.5, 1.0]  ):
         self.check_over_forward(time_step=a__    ,					eta=a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					self.scheduler_classes[0]
       _lowerCAmelCase						:	List[str]        =					self.get_scheduler_config()
       _lowerCAmelCase						:	Optional[int]        =					scheduler_class(**a__  )

       assert torch.sum(torch.abs(scheduler._get_variance(0    ,					0  ) - 0.0  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(420    ,					400  ) - 0.1_4_7_7_1  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980    ,					960  ) - 0.3_2_4_6_0  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(0    ,					0  ) - 0.0  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487    ,					486  ) - 0.0_0_9_7_9  )  ) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999    ,					998  ) - 0.0_2  )  ) < 1e-5






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					self.scheduler_classes[0]
       _lowerCAmelCase						:	List[Any]        =					self.get_scheduler_config()
       _lowerCAmelCase						:	Tuple        =					scheduler_class(**a__  )

       _lowerCAmelCase						:	Any        =					10, 0.0
       scheduler.set_timesteps(a__  )

       _lowerCAmelCase						:	List[str]        =					self.dummy_model()
       _lowerCAmelCase						:	List[Any]        =					self.dummy_sample_deter
       _lowerCAmelCase						:	Optional[int]        =					self.dummy_sample_deter + 0.1
       _lowerCAmelCase						:	Tuple        =					self.dummy_sample_deter - 0.1

       _lowerCAmelCase						:	Tuple        =					samplea.shape[0]
       _lowerCAmelCase						:	int        =					torch.stack([samplea, samplea, samplea]    ,					dim=0  )
       _lowerCAmelCase						:	List[Any]        =					torch.arange(a__  )[0:3, None].repeat(1    ,					a__  )

       _lowerCAmelCase						:	Any        =					model(samples.flatten(0    ,					1  )    ,					timesteps.flatten(0    ,					1  )  )
       _lowerCAmelCase						:	Union[str, Any]        =					scheduler.batch_step_no_noise(a__    ,					timesteps.flatten(0    ,					1  )    ,					samples.flatten(0    ,					1  )    ,					a__  )

       _lowerCAmelCase						:	Tuple        =					torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:	str        =					torch.mean(torch.abs(a__  )  )

       assert abs(result_sum.item() - 1147.7904  ) < 1e-2
       assert abs(result_mean.item() - 0.4_9_8_2  ) < 1e-3






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					self.full_loop()

       _lowerCAmelCase						:	Any        =					torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:	Optional[int]        =					torch.mean(torch.abs(a__  )  )

       assert abs(result_sum.item() - 172.0067  ) < 1e-2
       assert abs(result_mean.item() - 0.2_2_3_9_6_7  ) < 1e-3






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.full_loop(prediction_type="""v_prediction"""  )

       _lowerCAmelCase						:	int        =					torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:	Optional[int]        =					torch.mean(torch.abs(a__  )  )

       assert abs(result_sum.item() - 52.5302  ) < 1e-2
       assert abs(result_mean.item() - 0.0_6_8_4  ) < 1e-3






     def    __A  (		self  ):
       # We specify different beta, so that the first alpha is 0.99
       _lowerCAmelCase						:	Union[str, Any]        =					self.full_loop(set_alpha_to_one=a__    ,					beta_start=0.0_1  )
       _lowerCAmelCase						:	List[Any]        =					torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:	Tuple        =					torch.mean(torch.abs(a__  )  )

       assert abs(result_sum.item() - 149.8295  ) < 1e-2
       assert abs(result_mean.item() - 0.1_9_5_1  ) < 1e-3






     def    __A  (		self  ):
       # We specify different beta, so that the first alpha is 0.99
       _lowerCAmelCase						:	List[str]        =					self.full_loop(set_alpha_to_one=a__    ,					beta_start=0.0_1  )
       _lowerCAmelCase						:	Optional[int]        =					torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:	int        =					torch.mean(torch.abs(a__  )  )

       assert abs(result_sum.item() - 149.0784  ) < 1e-2
       assert abs(result_mean.item() - 0.1_9_4_1  ) < 1e-3





"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[torch.FloatTensor]		 =		None
     _UpperCamelCase							:      torch.FloatTensor		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None







class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__=1    ,					a__=0    ,					a__=2    ,					a__=512    ,					a__="cls"    ,					a__=False    ,					a__=True    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )
       _lowerCAmelCase						:	Optional[Any]        =					project_dim
       _lowerCAmelCase						:	List[str]        =					pooler_fn
       _lowerCAmelCase						:	Any        =					learn_encoder
       _lowerCAmelCase						:	Optional[int]        =					use_attention_mask







class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		[R"pooler", R"logit_scale"]
     _UpperCamelCase							:      List[Any]		 =		[R"position_ids", R"predictions.decoder.bias"]
     _UpperCamelCase							:      List[Any]		 =		"roberta"
     _UpperCamelCase							:      Optional[int]		 =		RobertaSeriesConfig






     def __init__(		self    ,					a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:	str        =					XLMRobertaModel(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
       _lowerCAmelCase						:	List[Any]        =					getattr(a__    ,					"""has_pre_transformation"""    ,					a__  )
       if self.has_pre_transformation:
         _lowerCAmelCase						:	List[str]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
         _lowerCAmelCase						:	Optional[int]        =					nn.LayerNorm(config.hidden_size    ,					eps=config.layer_norm_eps  )
       self.post_init()






     def    __A  (		self    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					):
       _lowerCAmelCase						:	List[str]        =					return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:	Optional[int]        =					self.base_model(
           input_ids=a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					position_ids=a__    ,					head_mask=a__    ,					inputs_embeds=a__    ,					encoder_hidden_states=a__    ,					encoder_attention_mask=a__    ,					output_attentions=a__    ,					output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,					return_dict=a__    ,					)

       if self.has_pre_transformation:
         _lowerCAmelCase						:	Optional[Any]        =					outputs["""hidden_states"""][-2]
         _lowerCAmelCase						:	Optional[Any]        =					self.pre_LN(a__  )
         _lowerCAmelCase						:	int        =					self.transformation_pre(a__  )

         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)
       else:
         _lowerCAmelCase						:	Union[str, Any]        =					self.transformation(outputs.last_hidden_state  )
         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)




