


'''simple docstring'''



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    (DDIMParallelScheduler,)
	A_				=    (("eta", 0.0), ("num_inference_steps", 50))




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  {
						    'num_train_timesteps': 1000,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'clip_sample': True,
						}

						config.update(**__a							)
						return config




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						__a			:       int					    =  self.scheduler_classes[0]
						__a			:       List[Any]					    =  self.get_scheduler_config(**__a							)
						__a			:       Tuple					    =  scheduler_class(**__a							)

						__a    ,				__a			:       Optional[Any]					    =  10, 0.0

						__a			:       List[str]					    =  self.dummy_model()
						__a			:       Optional[int]					    =  self.dummy_sample_deter

						scheduler.set_timesteps(__a							)

						for t in scheduler.timesteps:
											__a			:       Dict					    =  model(__a						,				__a							)
											__a			:       Tuple					    =  scheduler.step(__a						,				__a						,				__a						,				__a							).prev_sample

						return sample




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=__a							)

						__a			:       Tuple					    =  self.scheduler_classes[0]
						__a			:       Optional[Any]					    =  self.get_scheduler_config(steps_offset=1							)
						__a			:       Optional[int]					    =  scheduler_class(**__a							)
						scheduler.set_timesteps(5							)
						assert torch.equal(scheduler.timesteps						,				torch.LongTensor([801, 601, 401, 201, 1]							)							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]						,				[0.002, 0.02, 0.2, 2]							):
											self.check_over_configs(beta_start=__a						,				beta_end=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  self.scheduler_classes[0]
						__a			:       List[str]					    =  self.get_scheduler_config()
						__a			:       List[Any]					    =  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							) - 0.14771							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980						,				960							) - 0.32460							)							) < 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							) - 0.00979							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999						,				998							) - 0.02							)							) < 1E-5




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

						__a    ,				__a			:       int					    =  10, 0.0
						scheduler.set_timesteps(__a							)

						__a			:       Optional[int]					    =  self.dummy_model()
						__a			:       List[Any]					    =  self.dummy_sample_deter
						__a			:       Union[str, Any]					    =  self.dummy_sample_deter + 0.1
						__a			:       List[Any]					    =  self.dummy_sample_deter - 0.1

						__a			:       Dict					    =  samplea.shape[0]
						__a			:       Optional[Any]					    =  torch.stack([samplea, samplea, samplea]						,				dim=0							)
						__a			:       Dict					    =  torch.arange(__a							)[0:3, None].repeat(1						,				__a							)

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

						__a			:       Any					    =  torch.sum(torch.abs(__a							)							)
						__a			:       Tuple					    =  torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 1147.7904							) < 1E-2
						assert abs(result_mean.item() - 0.4982							) < 1E-3




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  self.full_loop()

						__a			:       Optional[Any]					    =  torch.sum(torch.abs(__a							)							)
						__a			:       str					    =  torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 172.0067							) < 1E-2
						assert abs(result_mean.item() - 0.223967							) < 1E-3




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  self.full_loop(prediction_type='v_prediction'							)

						__a			:       List[str]					    =  torch.sum(torch.abs(__a							)							)
						__a			:       str					    =  torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 52.5302							) < 1E-2
						assert abs(result_mean.item() - 0.0684							) < 1E-3




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.full_loop(set_alpha_to_one=__a						,				beta_start=0.01							)
						__a			:       List[str]					    =  torch.sum(torch.abs(__a							)							)
						__a			:       Union[str, Any]					    =  torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 149.8295							) < 1E-2
						assert abs(result_mean.item() - 0.1951							) < 1E-3





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.full_loop(set_alpha_to_one=__a						,				beta_start=0.01							)
						__a			:       Optional[Any]					    =  torch.sum(torch.abs(__a							)							)
						__a			:       List[str]					    =  torch.mean(torch.abs(__a							)							)

						assert abs(result_sum.item() - 149.0784							) < 1E-2
						assert abs(result_mean.item() - 0.1941							) < 1E-3








'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      int				=      '''xlm-roberta-xl'''




  def __init__(    self  ,__UpperCAmelCase=25_0880  ,__UpperCAmelCase=2560  ,__UpperCAmelCase=36  ,__UpperCAmelCase=32  ,__UpperCAmelCase=1_0240  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=514  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-05  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0  ,__UpperCAmelCase=2  ,__UpperCAmelCase="absolute"  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		str:
      super().__init__(pad_token_id=__UpperCAmelCase  ,bos_token_id=__UpperCAmelCase  ,eos_token_id=__UpperCAmelCase  ,**__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					vocab_size
      lowerCAmelCase__ :  int							      =					hidden_size
      lowerCAmelCase__ :  int							      =					num_hidden_layers
      lowerCAmelCase__ :  str							      =					num_attention_heads
      lowerCAmelCase__ :  int							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					intermediate_size
      lowerCAmelCase__ :  List[Any]							      =					hidden_dropout_prob
      lowerCAmelCase__ :  str							      =					attention_probs_dropout_prob
      lowerCAmelCase__ :  Optional[int]							      =					max_position_embeddings
      lowerCAmelCase__ :  List[str]							      =					type_vocab_size
      lowerCAmelCase__ :  List[Any]							      =					initializer_range
      lowerCAmelCase__ :  Tuple							      =					layer_norm_eps
      lowerCAmelCase__ :  int							      =					position_embedding_type
      lowerCAmelCase__ :  Optional[Any]							      =					use_cache
      lowerCAmelCase__ :  Optional[Any]							      =					classifier_dropout







class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
          lowerCAmelCase__ :  Dict							      =					{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
          lowerCAmelCase__ :  Any							      =					{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]      )


