




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Tuple			=      (EulerDiscreteScheduler,)
  _UpperCamelCase       :      Optional[Any]			=      1_0






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






       """simple docstring"""







       lowercase :       Optional[Any]       =			{
           '''num_train_timesteps''': 1_100,
           '''beta_start''': 0.0_001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_A				)
       return config






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






       """simple docstring"""







       for timesteps in [10, 50, 100, 1_000]:
            self.check_over_configs(num_train_timesteps=_A				)






  def  __a      (  self		:     Tuple				)		->   str:






       """simple docstring"""







       for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]				,						[0.0_002, 0.002, 0.02]				):
            self.check_over_configs(beta_start=_A				,						beta_end=_A				)






  def  __a      (  self		:     str				)		->   str:






       """simple docstring"""







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






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






       """simple docstring"""







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






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






       """simple docstring"""







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

       scheduler.set_timesteps(self.num_inference_steps				)

       lowercase :       Tuple       =			torch.manual_seed(0				)

       lowercase :       Union[str, Any]       =			self.dummy_model()
       lowercase :       Any       =			self.dummy_sample_deter * scheduler.init_noise_sigma
       lowercase :       Optional[int]       =			sample.to(_A				)

       for i, t in enumerate(scheduler.timesteps				):
            lowercase :       List[Any]       =			scheduler.scale_model_input(_A				,						_A				)

            lowercase :       str       =			model(_A				,						_A				)

            lowercase :       Tuple       =			scheduler.step(_A				,						_A				,						_A				,						generator=_A				)
            lowercase :       Tuple       =			output.prev_sample

       lowercase :       Tuple       =			torch.sum(torch.abs(_A				)				)
       lowercase :       int       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 10.0_807				) < 1E-2
       assert abs(result_mean.item() - 0.0_131				) < 1E-3






  def  __a      (  self		:     str				)		->   Tuple:






       """simple docstring"""







       lowercase :       int       =			self.scheduler_classes[0]
       lowercase :       str       =			self.get_scheduler_config(prediction_type='''v_prediction'''				)
       lowercase :       Optional[int]       =			scheduler_class(**_A				)

       scheduler.set_timesteps(self.num_inference_steps				)

       lowercase :       Optional[int]       =			torch.manual_seed(0				)

       lowercase :       List[Any]       =			self.dummy_model()
       lowercase :       Tuple       =			self.dummy_sample_deter * scheduler.init_noise_sigma
       lowercase :       Optional[Any]       =			sample.to(_A				)

       for i, t in enumerate(scheduler.timesteps				):
            lowercase :       Union[str, Any]       =			scheduler.scale_model_input(_A				,						_A				)

            lowercase :       Union[str, Any]       =			model(_A				,						_A				)

            lowercase :       int       =			scheduler.step(_A				,						_A				,						_A				,						generator=_A				)
            lowercase :       Optional[Any]       =			output.prev_sample

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

       assert abs(result_sum.item() - 0.0_002				) < 1E-2
       assert abs(result_mean.item() - 2.2_676E-06				) < 1E-3






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






       """simple docstring"""







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

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

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

       lowercase :       Dict       =			self.dummy_model()
       lowercase :       Any       =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       lowercase :       List[str]       =			sample.to(_A				)

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

            lowercase :       List[str]       =			model(_A				,						_A				)

            lowercase :       Optional[Any]       =			scheduler.step(_A				,						_A				,						_A				,						generator=_A				)
            lowercase :       int       =			output.prev_sample

       lowercase :       Dict       =			torch.sum(torch.abs(_A				)				)
       lowercase :       int       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 10.0_807				) < 1E-2
       assert abs(result_mean.item() - 0.0_131				) < 1E-3






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






       """simple docstring"""







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

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

       lowercase :       Dict       =			torch.manual_seed(0				)

       lowercase :       Union[str, Any]       =			self.dummy_model()
       lowercase :       Tuple       =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       lowercase :       Dict       =			sample.to(_A				)

       for t in scheduler.timesteps:
            lowercase :       int       =			scheduler.scale_model_input(_A				,						_A				)

            lowercase :       Optional[Any]       =			model(_A				,						_A				)

            lowercase :       int       =			scheduler.step(_A				,						_A				,						_A				,						generator=_A				)
            lowercase :       Any       =			output.prev_sample

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

       assert abs(result_sum.item() - 124.52_299_499_511_719				) < 1E-2
       assert abs(result_mean.item() - 0.16_213_932_633_399_963				) < 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),
           ]				)