



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


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


						UpperCAmelCase			:		Optional[int]		=		(EulerDiscreteScheduler,)
						UpperCAmelCase			:		Optional[Any]		=		10







						def       lowerCamelCase_				(  self    :				Union[str, Any]       ,	**snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				{
											    'num_train_timesteps': 1100,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**snake_case	)
											return config







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=snake_case	)







						def       lowerCamelCase_				(  self    :				str	):



											'''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       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


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

											scheduler.set_timesteps(self.num_inference_steps	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.manual_seed(0	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				self.dummy_model()
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.dummy_sample_deter * scheduler.init_noise_sigma
											SCREAMING_SNAKE_CASE							:     Dict						=				sample.to(snake_case	)

											for i, t in enumerate(scheduler.timesteps	):
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				scheduler.scale_model_input(snake_case       ,	snake_case	)

																SCREAMING_SNAKE_CASE							:     Dict						=				model(snake_case       ,	snake_case	)

																SCREAMING_SNAKE_CASE							:     Optional[int]						=				scheduler.step(snake_case       ,	snake_case       ,	snake_case       ,	generator=snake_case	)
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				output.prev_sample

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.sum(torch.abs(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Any						=				torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 10.0807	) < 1E-2
											assert abs(result_mean.item() - 0.0131	) < 1E-3







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     List[str]						=				self.get_scheduler_config(prediction_type='v_prediction'	)
											SCREAMING_SNAKE_CASE							:     str						=				scheduler_class(**snake_case	)

											scheduler.set_timesteps(self.num_inference_steps	)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.manual_seed(0	)

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

											for i, t in enumerate(scheduler.timesteps	):
																SCREAMING_SNAKE_CASE							:     Any						=				scheduler.scale_model_input(snake_case       ,	snake_case	)

																SCREAMING_SNAKE_CASE							:     List[str]						=				model(snake_case       ,	snake_case	)

																SCREAMING_SNAKE_CASE							:     List[Any]						=				scheduler.step(snake_case       ,	snake_case       ,	snake_case       ,	generator=snake_case	)
																SCREAMING_SNAKE_CASE							:     int						=				output.prev_sample

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

											assert abs(result_sum.item() - 0.0002	) < 1E-2
											assert abs(result_mean.item() - 2.2676E-06	) < 1E-3







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     Dict						=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE							:     List[str]						=				scheduler_class(**snake_case	)

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

											SCREAMING_SNAKE_CASE							:     str						=				torch.manual_seed(0	)

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

											for t in scheduler.timesteps:
																SCREAMING_SNAKE_CASE							:     List[Any]						=				scheduler.scale_model_input(snake_case       ,	snake_case	)

																SCREAMING_SNAKE_CASE							:     Dict						=				model(snake_case       ,	snake_case	)

																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				scheduler.step(snake_case       ,	snake_case       ,	snake_case       ,	generator=snake_case	)
																SCREAMING_SNAKE_CASE							:     List[Any]						=				output.prev_sample

											SCREAMING_SNAKE_CASE							:     str						=				torch.sum(torch.abs(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Dict						=				torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 10.0807	) < 1E-2
											assert abs(result_mean.item() - 0.0131	) < 1E-3







						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


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

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

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

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.dummy_model()
											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				sample.to(snake_case	)

											for t in scheduler.timesteps:
																SCREAMING_SNAKE_CASE							:     Any						=				scheduler.scale_model_input(snake_case       ,	snake_case	)

																SCREAMING_SNAKE_CASE							:     str						=				model(snake_case       ,	snake_case	)

																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				scheduler.step(snake_case       ,	snake_case       ,	snake_case       ,	generator=snake_case	)
																SCREAMING_SNAKE_CASE							:     int						=				output.prev_sample

											SCREAMING_SNAKE_CASE							:     List[str]						=				torch.sum(torch.abs(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 124.52299499511719	) < 1E-2
											assert abs(result_mean.item() - 0.16213932633399963	) < 1E-3




from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase    :		int		      =    logging.get_logger(__name__)

_lowerCamelCase    :		Union[str, Any]		      =    {
    """microsoft/xprophetnet-large-wiki100-cased""": (
        """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
    ),
}


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


						UpperCAmelCase			:		Tuple		=		'xlm-prophetnet'
						UpperCAmelCase			:		int		=		['past_key_values']
						UpperCAmelCase			:		List[Any]		=		{
						    'num_attention_heads': 'num_encoder_attention_heads',
						}







						def __init__(  self    :				List[Any]       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[Union[str, Callable]] = "gelu"       ,	snake_case    :				Optional[int] = 30522       ,	snake_case    :				Optional[int] = 1024       ,	snake_case    :				Optional[int] = 4096       ,	snake_case    :				Optional[int] = 12       ,	snake_case    :				Optional[int] = 16       ,	snake_case    :				Optional[int] = 4096       ,	snake_case    :				Optional[int] = 12       ,	snake_case    :				Optional[int] = 16       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[int] = 512       ,	snake_case    :				Optional[float] = 0.02       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[int] = 0       ,	snake_case    :				Optional[int] = 2       ,	snake_case    :				Optional[int] = 32       ,	snake_case    :				Optional[int] = 128       ,	snake_case    :				Optional[bool] = False       ,	snake_case    :				Optional[float] = 0.0       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[int] = 0       ,	snake_case    :				Optional[int] = 1       ,	snake_case    :				Optional[int] = 2       ,	**snake_case    :				Optional[int]       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				vocab_size
											SCREAMING_SNAKE_CASE							:     int						=				hidden_size
											SCREAMING_SNAKE_CASE							:     Dict						=				encoder_ffn_dim
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				num_encoder_layers
											SCREAMING_SNAKE_CASE							:     str						=				num_encoder_attention_heads
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				decoder_ffn_dim
											SCREAMING_SNAKE_CASE							:     Any						=				num_decoder_layers
											SCREAMING_SNAKE_CASE							:     List[str]						=				num_decoder_attention_heads
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				max_position_embeddings
											SCREAMING_SNAKE_CASE							:     Any						=				init_std  # Normal(0, this parameter)
											SCREAMING_SNAKE_CASE							:     List[str]						=				activation_function

											# parameters for xlmprophetnet
											SCREAMING_SNAKE_CASE							:     Tuple						=				ngram
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				num_buckets
											SCREAMING_SNAKE_CASE							:     Any						=				relative_max_distance
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				disable_ngram_loss
											SCREAMING_SNAKE_CASE							:     List[str]						=				eps

											# 3 Types of Dropout
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				attention_dropout
											SCREAMING_SNAKE_CASE							:     Any						=				activation_dropout
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				dropout

											SCREAMING_SNAKE_CASE							:     Tuple						=				use_cache

											super().__init__(
											    pad_token_id=snake_case       ,	bos_token_id=snake_case       ,	eos_token_id=snake_case       ,	is_encoder_decoder=snake_case       ,	add_cross_attention=snake_case       ,	decoder_start_token_id=snake_case       ,	**snake_case       ,	)







						@property
						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											return self.num_encoder_layers + self.num_decoder_layers







						@num_hidden_layers.setter
						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				Optional[int]	):



											'''simple docstring'''


											raise NotImplementedError(
											    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
											    ' `num_decoder_layers`.'	)