




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Any			=      (DDIMParallelScheduler,)
  _UpperCamelCase       :      Any			=      (('''eta''', 0.0), ('''num_inference_steps''', 5_0))






  def  __a      (  self		:     Optional[int]				,						**_A		:     int				)		->   Tuple:






       """simple docstring"""







       lowercase :       List[str]       =			{
           '''num_train_timesteps''': 1_000,
           '''beta_start''': 0.0_001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''clip_sample''': True,
       }

       config.update(**_A				)
       return config






  def  __a      (  self		:     int				,						**_A		:     Union[str, Any]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       Any       =			self.scheduler_classes[0]
       lowercase :       Dict       =			self.get_scheduler_config(**_A				)
       lowercase :       Dict       =			scheduler_class(**_A				)

       lowercase      ,	lowercase :       List[str]       =			10, 0.0

       lowercase :       int       =			self.dummy_model()
       lowercase :       Dict       =			self.dummy_sample_deter

       scheduler.set_timesteps(_A				)

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

       return sample






  def  __a      (  self		:     Optional[int]				)		->   Optional[Any]:






       """simple docstring"""







       for timesteps in [100, 500, 1_000]:
            self.check_over_configs(num_train_timesteps=_A				)






  def  __a      (  self		:     str				)		->   List[str]:






       """simple docstring"""







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

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






  def  __a      (  self		:     Tuple				)		->   Dict:






       """simple docstring"""







       for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]				,						[0.002, 0.02, 0.2, 2]				):
            self.check_over_configs(beta_start=_A				,						beta_end=_A				)






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






       """simple docstring"""







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






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






       """simple docstring"""







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






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






       """simple docstring"""







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






  def  __a      (  self		:     List[Any]				)		->   Optional[int]:






       """simple docstring"""







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






  def  __a      (  self		:     Dict				)		->   Any:






       """simple docstring"""







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






  def  __a      (  self		:     Any				)		->   int:






       """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  __a      (  self		:     int				)		->   Any:






       """simple docstring"""







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






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






       """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  __a      (  self		:     Tuple				)		->   str:






       """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  __a      (  self		:     Tuple				)		->   str:






       """simple docstring"""







       lowercase :       Any       =			self.scheduler_classes[0]
       lowercase :       Any       =			self.get_scheduler_config()
       lowercase :       Union[str, 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				) - 0.14_771				)				) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(980				,						960				) - 0.32_460				)				) < 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				) - 0.00_979				)				) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999				,						998				) - 0.02				)				) < 1E-5






  def  __a      (  self		:     Dict				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Dict       =			self.scheduler_classes[0]
       lowercase :       Union[str, Any]       =			self.get_scheduler_config()
       lowercase :       Optional[Any]       =			scheduler_class(**_A				)

       lowercase      ,	lowercase :       Any       =			10, 0.0
       scheduler.set_timesteps(_A				)

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

       lowercase :       Dict       =			samplea.shape[0]
       lowercase :       str       =			torch.stack([samplea, samplea, samplea]				,						dim=0				)
       lowercase :       Tuple       =			torch.arange(_A				)[0:3, None].repeat(1				,						_A				)

       lowercase :       Optional[Any]       =			model(samples.flatten(0				,						1				)				,						timesteps.flatten(0				,						1				)				)
       lowercase :       int       =			scheduler.batch_step_no_noise(_A				,						timesteps.flatten(0				,						1				)				,						samples.flatten(0				,						1				)				,						_A				)

       lowercase :       Union[str, Any]       =			torch.sum(torch.abs(_A				)				)
       lowercase :       Tuple       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 1_147.7_904				) < 1E-2
       assert abs(result_mean.item() - 0.4_982				) < 1E-3






  def  __a      (  self		:     Tuple				)		->   List[str]:






       """simple docstring"""







       lowercase :       Optional[int]       =			self.full_loop()

       lowercase :       List[Any]       =			torch.sum(torch.abs(_A				)				)
       lowercase :       Optional[int]       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 172.0_067				) < 1E-2
       assert abs(result_mean.item() - 0.223_967				) < 1E-3






  def  __a      (  self		:     int				)		->   Tuple:






       """simple docstring"""







       lowercase :       Tuple       =			self.full_loop(prediction_type='''v_prediction'''				)

       lowercase :       str       =			torch.sum(torch.abs(_A				)				)
       lowercase :       List[Any]       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 52.5_302				) < 1E-2
       assert abs(result_mean.item() - 0.0_684				) < 1E-3






  def  __a      (  self		:     List[str]				)		->   Any:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.full_loop(set_alpha_to_one=_A				,						beta_start=0.01				)
       lowercase :       Dict       =			torch.sum(torch.abs(_A				)				)
       lowercase :       Any       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 149.8_295				) < 1E-2
       assert abs(result_mean.item() - 0.1_951				) < 1E-3






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






       """simple docstring"""







       lowercase :       Optional[int]       =			self.full_loop(set_alpha_to_one=_A				,						beta_start=0.01				)
       lowercase :       Dict       =			torch.sum(torch.abs(_A				)				)
       lowercase :       str       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 149.0_784				) < 1E-2
       assert abs(result_mean.item() - 0.1_941				) < 1E-3





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/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Tuple			=      '''xmod'''






  def __init__(  self		:     Optional[Any]				,						_A		:     Union[str, Any]=30_522				,						_A		:     List[Any]=768				,						_A		:     Optional[Any]=12				,						_A		:     Any=12				,						_A		:     Tuple=3_072				,						_A		:     Optional[int]="gelu"				,						_A		:     List[Any]=0.1				,						_A		:     str=0.1				,						_A		:     List[Any]=512				,						_A		:     List[str]=2				,						_A		:     str=0.02				,						_A		:     Any=1E-12				,						_A		:     Union[str, Any]=1				,						_A		:     List[Any]=0				,						_A		:     Dict=2				,						_A		:     int="absolute"				,						_A		:     Dict=True				,						_A		:     int=None				,						_A		:     List[str]=False				,						_A		:     Dict=2				,						_A		:     int=False				,						_A		:     Optional[int]=True				,						_A		:     Any=True				,						_A		:     Optional[int]=("en_XX",)				,						_A		:     Any=None				,						**_A		:     Optional[int]				,						)		->   Union[str, Any]:






       """simple docstring"""







       super().__init__(pad_token_id=_A				,						bos_token_id=_A				,						eos_token_id=_A				,						**_A				)

       lowercase :       Optional[Any]       =			vocab_size
       lowercase :       Union[str, Any]       =			hidden_size
       lowercase :       Optional[Any]       =			num_hidden_layers
       lowercase :       Dict       =			num_attention_heads
       lowercase :       Union[str, Any]       =			hidden_act
       lowercase :       Tuple       =			intermediate_size
       lowercase :       List[str]       =			hidden_dropout_prob
       lowercase :       Union[str, Any]       =			attention_probs_dropout_prob
       lowercase :       Dict       =			max_position_embeddings
       lowercase :       Any       =			type_vocab_size
       lowercase :       Optional[Any]       =			initializer_range
       lowercase :       str       =			layer_norm_eps
       lowercase :       Tuple       =			position_embedding_type
       lowercase :       Optional[Any]       =			use_cache
       lowercase :       int       =			classifier_dropout
       lowercase :       Optional[int]       =			pre_norm
       lowercase :       Any       =			adapter_reduction_factor
       lowercase :       Union[str, Any]       =			adapter_layer_norm
       lowercase :       Optional[int]       =			adapter_reuse_layer_norm
       lowercase :       Optional[Any]       =			ln_before_adapter
       lowercase :       Union[str, Any]       =			list(_A				)
       lowercase :       List[Any]       =			default_language


class 						_A (  _lowerCamelCase				):






  @property
  def  __a      (  self		:     Union[str, Any]				)		->   Mapping[str, Mapping[int, str]]:






       """simple docstring"""







       if self.task == "multiple-choice":
            lowercase :       Any       =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
            lowercase :       Union[str, Any]       =			{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ]				)