




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       (KDPMaDiscreteScheduler,)
						UpperCamelCase      =       10

						def      A__    (		self :List[str]     ,			**__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										={
										    """num_train_timesteps""": 11_00,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										}

										config.update(**__snake_case			)
										return config

						def      A__    (		self :str			):
										'''simple docstring'''

										for timesteps in [10, 50, 1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]     ,			[0.0002, 0.002, 0.02]			):
														self.check_over_configs(beta_start=__snake_case     ,			beta_end=__snake_case			)

						def      A__    (		self :Dict			):
										'''simple docstring'''

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

						def      A__    (		self :Dict			):
										'''simple docstring'''

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

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self.scheduler_classes[0]
										__magic_name__						:						Optional[Any]										=self.get_scheduler_config(prediction_type="""v_prediction"""			)
										__magic_name__						:						List[Any]										=scheduler_class(**__snake_case			)

										scheduler.set_timesteps(self.num_inference_steps			)

										__magic_name__						:						List[str]										=self.dummy_model()
										__magic_name__						:						Union[str, Any]										=self.dummy_sample_deter * scheduler.init_noise_sigma
										__magic_name__						:						Union[str, Any]										=sample.to(__snake_case			)

										for i, t in enumerate(scheduler.timesteps			):
														__magic_name__						:						Tuple										=scheduler.scale_model_input(__snake_case     ,			__snake_case			)

														__magic_name__						:						Optional[Any]										=model(__snake_case     ,			__snake_case			)

														__magic_name__						:						Tuple										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case			)
														__magic_name__						:						Any										=output.prev_sample

										__magic_name__						:						int										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						Optional[Any]										=torch.mean(torch.abs(__snake_case			)			)

										if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 4.6934E-07			) < 1E-2
														assert abs(result_mean.item() - 6.1112E-10			) < 1E-3
										else:
														# CUDA
														assert abs(result_sum.item() - 4.693428650170972E-07			) < 1E-2
														assert abs(result_mean.item() - 0.0002			) < 1E-3

						def      A__    (		self :Dict			):
										'''simple docstring'''

										if torch_device == "mps":
														return
										__magic_name__						:						List[str]										=self.scheduler_classes[0]
										__magic_name__						:						Dict										=self.get_scheduler_config()
										__magic_name__						:						Any										=scheduler_class(**__snake_case			)

										scheduler.set_timesteps(self.num_inference_steps			)

										__magic_name__						:						Tuple										=self.dummy_model()
										__magic_name__						:						List[Any]										=self.dummy_sample_deter * scheduler.init_noise_sigma
										__magic_name__						:						Tuple										=sample.to(__snake_case			)

										for i, t in enumerate(scheduler.timesteps			):
														__magic_name__						:						Dict										=scheduler.scale_model_input(__snake_case     ,			__snake_case			)

														__magic_name__						:						Union[str, Any]										=model(__snake_case     ,			__snake_case			)

														__magic_name__						:						Optional[Any]										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case			)
														__magic_name__						:						str										=output.prev_sample

										__magic_name__						:						List[Any]										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						str										=torch.mean(torch.abs(__snake_case			)			)

										if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 20.4125			) < 1E-2
														assert abs(result_mean.item() - 0.0266			) < 1E-3
										else:
														# CUDA
														assert abs(result_sum.item() - 20.4125			) < 1E-2
														assert abs(result_mean.item() - 0.0266			) < 1E-3


						def      A__    (		self :Any			):
										'''simple docstring'''

										if torch_device == "mps":
														return
										__magic_name__						:						Tuple										=self.scheduler_classes[0]
										__magic_name__						:						List[str]										=self.get_scheduler_config()
										__magic_name__						:						int										=scheduler_class(**__snake_case			)

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

										__magic_name__						:						str										=self.dummy_model()
										__magic_name__						:						Optional[int]										=self.dummy_sample_deter.to(__snake_case			) * scheduler.init_noise_sigma

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

														__magic_name__						:						Union[str, Any]										=model(__snake_case     ,			__snake_case			)

														__magic_name__						:						Tuple										=scheduler.step(__snake_case     ,			__snake_case     ,			__snake_case			)
														__magic_name__						:						Optional[Any]										=output.prev_sample

										__magic_name__						:						Dict										=torch.sum(torch.abs(__snake_case			)			)
										__magic_name__						:						Union[str, Any]										=torch.mean(torch.abs(__snake_case			)			)

										if str(__snake_case			).startswith("""cpu"""			):
														# The following sum varies between 148 and 156 on mps. Why?
														assert abs(result_sum.item() - 20.4125			) < 1E-2
														assert abs(result_mean.item() - 0.0266			) < 1E-3
										else:
														# CUDA
														assert abs(result_sum.item() - 20.4125			) < 1E-2
														assert abs(result_mean.item() - 0.0266			) < 1E-3










from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "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 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """xlm-roberta-xl"""

						def __init__(		self :Dict     ,			__snake_case :Optional[Any]=25_08_80     ,			__snake_case :List[Any]=25_60     ,			__snake_case :Optional[Any]=36     ,			__snake_case :Any=32     ,			__snake_case :int=1_02_40     ,			__snake_case :List[Any]="gelu"     ,			__snake_case :Union[str, Any]=0.1     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :str=5_14     ,			__snake_case :Union[str, Any]=1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :str=1E-05     ,			__snake_case :str=1     ,			__snake_case :int=0     ,			__snake_case :Tuple=2     ,			__snake_case :Optional[int]="absolute"     ,			__snake_case :str=True     ,			__snake_case :Any=None     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			**__snake_case			)
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						List[str]										=hidden_size
										__magic_name__						:						Union[str, Any]										=num_hidden_layers
										__magic_name__						:						Any										=num_attention_heads
										__magic_name__						:						Any										=hidden_act
										__magic_name__						:						List[str]										=intermediate_size
										__magic_name__						:						Any										=hidden_dropout_prob
										__magic_name__						:						Union[str, Any]										=attention_probs_dropout_prob
										__magic_name__						:						Any										=max_position_embeddings
										__magic_name__						:						Any										=type_vocab_size
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						Optional[int]										=layer_norm_eps
										__magic_name__						:						Dict										=position_embedding_type
										__magic_name__						:						Any										=use_cache
										__magic_name__						:						Dict										=classifier_dropout




class 		__A   (      UpperCamelCase__      ):

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

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




