






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							a_ (						__snake_case		):


							'''simple docstring'''






							__a:	List[str]     =    (EulerAncestralDiscreteScheduler,)
							__a:	int     =    1_0

							def      _lowercase       (		self			,			**lowercase_   )					->					str:
														'''simple docstring'''

														lowerCAmelCase_										=     {
														    "num_train_timesteps": 1_1_0_0,
														    "beta_start": 0.00_01,
														    "beta_end": 0.02,
														    "beta_schedule": "linear",
														}

														config.update(**lowercase_   )
														return config

							def      _lowercase       (		self   )					->					Union[str, Any]:
														'''simple docstring'''

														for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																					self.check_over_configs(num_train_timesteps=lowercase_   )

							def      _lowercase       (		self   )					->					Dict:
														'''simple docstring'''

														for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,			[0.00_02, 0.0_02, 0.02]   ):
																					self.check_over_configs(beta_start=lowercase_			,			beta_end=lowercase_   )

							def      _lowercase       (		self   )					->					Tuple:
														'''simple docstring'''

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

							def      _lowercase       (		self   )					->					int:
														'''simple docstring'''

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

							def      _lowercase       (		self   )					->					int:
														'''simple docstring'''

														lowerCAmelCase_										=     self.scheduler_classes[0]
														lowerCAmelCase_										=     self.get_scheduler_config()
														lowerCAmelCase_										=     scheduler_class(**lowercase_   )

														scheduler.set_timesteps(self.num_inference_steps   )

														lowerCAmelCase_										=     torch.manual_seed(0   )

														lowerCAmelCase_										=     self.dummy_model()
														lowerCAmelCase_										=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
														lowerCAmelCase_										=     sample.to(lowercase_   )

														for i, t in enumerate(scheduler.timesteps   ):
																					lowerCAmelCase_										=     scheduler.scale_model_input(lowercase_			,			lowercase_   )

																					lowerCAmelCase_										=     model(lowercase_			,			lowercase_   )

																					lowerCAmelCase_										=     scheduler.step(lowercase_			,			lowercase_			,			lowercase_			,			generator=lowercase_   )
																					lowerCAmelCase_										=     output.prev_sample

														lowerCAmelCase_										=     torch.sum(torch.abs(lowercase_   )   )
														lowerCAmelCase_										=     torch.mean(torch.abs(lowercase_   )   )

														assert abs(result_sum.item() - 1_52.31_92   ) < 1e-2
														assert abs(result_mean.item() - 0.19_83   ) < 1e-3

							def      _lowercase       (		self   )					->					int:
														'''simple docstring'''

														lowerCAmelCase_										=     self.scheduler_classes[0]
														lowerCAmelCase_										=     self.get_scheduler_config(prediction_type='v_prediction'   )
														lowerCAmelCase_										=     scheduler_class(**lowercase_   )

														scheduler.set_timesteps(self.num_inference_steps   )

														lowerCAmelCase_										=     torch.manual_seed(0   )

														lowerCAmelCase_										=     self.dummy_model()
														lowerCAmelCase_										=     self.dummy_sample_deter * scheduler.init_noise_sigma
														lowerCAmelCase_										=     sample.to(lowercase_   )

														for i, t in enumerate(scheduler.timesteps   ):
																					lowerCAmelCase_										=     scheduler.scale_model_input(lowercase_			,			lowercase_   )

																					lowerCAmelCase_										=     model(lowercase_			,			lowercase_   )

																					lowerCAmelCase_										=     scheduler.step(lowercase_			,			lowercase_			,			lowercase_			,			generator=lowercase_   )
																					lowerCAmelCase_										=     output.prev_sample

														lowerCAmelCase_										=     torch.sum(torch.abs(lowercase_   )   )
														lowerCAmelCase_										=     torch.mean(torch.abs(lowercase_   )   )

														assert abs(result_sum.item() - 1_08.44_39   ) < 1e-2
														assert abs(result_mean.item() - 0.14_12   ) < 1e-3






							def      _lowercase       (		self   )					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     self.scheduler_classes[0]
														lowerCAmelCase_										=     self.get_scheduler_config()
														lowerCAmelCase_										=     scheduler_class(**lowercase_   )

														scheduler.set_timesteps(self.num_inference_steps			,			device=lowercase_   )
														lowerCAmelCase_										=     torch.manual_seed(0   )

														lowerCAmelCase_										=     self.dummy_model()
														lowerCAmelCase_										=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
														lowerCAmelCase_										=     sample.to(lowercase_   )

														for t in scheduler.timesteps:
																					lowerCAmelCase_										=     scheduler.scale_model_input(lowercase_			,			lowercase_   )

																					lowerCAmelCase_										=     model(lowercase_			,			lowercase_   )

																					lowerCAmelCase_										=     scheduler.step(lowercase_			,			lowercase_			,			lowercase_			,			generator=lowercase_   )
																					lowerCAmelCase_										=     output.prev_sample

														lowerCAmelCase_										=     torch.sum(torch.abs(lowercase_   )   )
														lowerCAmelCase_										=     torch.mean(torch.abs(lowercase_   )   )

														assert abs(result_sum.item() - 1_52.31_92   ) < 1e-2
														assert abs(result_mean.item() - 0.19_83   ) < 1e-3




from collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE						:    str     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    Union[str, Any]     = {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}



class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'ernie'



							def __init__(						self      ,				lowercase_=30_522      ,				lowercase_=768      ,				lowercase_=12      ,				lowercase_=12      ,				lowercase_=3_072      ,				lowercase_="gelu"      ,				lowercase_=0.1      ,				lowercase_=0.1      ,				lowercase_=512      ,				lowercase_=2      ,				lowercase_=3      ,				lowercase_=False      ,				lowercase_=0.02      ,				lowercase_=1e-12      ,				lowercase_=0      ,				lowercase_="absolute"      ,				lowercase_=True      ,				lowercase_=None      ,				**lowercase_      ,				):
													super().__init__(pad_token_id=lowercase_      ,				**lowercase_				)

													_snake_case						:     List[Any]				=    vocab_size
													_snake_case						:     Dict				=    hidden_size
													_snake_case						:     Any				=    num_hidden_layers
													_snake_case						:     List[str]				=    num_attention_heads
													_snake_case						:     int				=    hidden_act
													_snake_case						:     Tuple				=    intermediate_size
													_snake_case						:     Tuple				=    hidden_dropout_prob
													_snake_case						:     Optional[int]				=    attention_probs_dropout_prob
													_snake_case						:     Any				=    max_position_embeddings
													_snake_case						:     List[str]				=    type_vocab_size
													_snake_case						:     List[Any]				=    task_type_vocab_size
													_snake_case						:     Optional[int]				=    use_task_id
													_snake_case						:     Tuple				=    initializer_range
													_snake_case						:     Union[str, Any]				=    layer_norm_eps
													_snake_case						:     Optional[Any]				=    position_embedding_type
													_snake_case						:     int				=    use_cache
													_snake_case						:     Optional[Any]				=    classifier_dropout




class    lowercase_    (     __snake_case		):



							@property
							def        UpperCamelCase       (						self				):
													if self.task == "multiple-choice":
																			_snake_case						:     str				=    {0: "batch", 1: "choice", 2: "sequence"}
													else:
																			_snake_case						:     Optional[int]				=    {0: "batch", 1: "sequence"}
													return OrderedDict(
													    [
													        ("input_ids", dynamic_axis),
													        ("attention_mask", dynamic_axis),
													        ("token_type_ids", dynamic_axis),
													        ("task_type_ids", dynamic_axis),
													    ]				)