


"""simple docstring"""







import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 							lowerCamelCase       (			snake_case__ ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      (KDPMaAncestralDiscreteScheduler,)
							SCREAMING_SNAKE_CASE										=      10






							def 					lowerCAmelCase     (						self :     int		, **lowerCamelCase_ :     Dict				):
													UpperCAmelCase__		=    {
													    '''num_train_timesteps''': 1100,
													    '''beta_start''': 0.0001,
													    '''beta_end''': 0.02,
													    '''beta_schedule''': '''linear''',
													}

													config.update(**__snake_case				)
													return config






							def 					lowerCAmelCase     (						self :     Dict				):
													for timesteps in [10, 50, 100, 1000]:
																			self.check_over_configs(num_train_timesteps=__snake_case				)






							def 					lowerCAmelCase     (						self :     Optional[int]				):
													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 :     Optional[Any]				):
													for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=__snake_case				)






							def 					lowerCAmelCase     (						self :     Optional[Any]				):
													if torch_device == "mps":
																			return
													UpperCAmelCase__		=    self.scheduler_classes[0]
													UpperCAmelCase__		=    self.get_scheduler_config()
													UpperCAmelCase__		=    scheduler_class(**__snake_case				)

													scheduler.set_timesteps(self.num_inference_steps				)

													UpperCAmelCase__		=    torch.manual_seed(0				)

													UpperCAmelCase__		=    self.dummy_model()
													UpperCAmelCase__		=    self.dummy_sample_deter * scheduler.init_noise_sigma
													UpperCAmelCase__		=    sample.to(__snake_case				)

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

																			UpperCAmelCase__		=    model(__snake_case		, __snake_case				)

																			UpperCAmelCase__		=    scheduler.step(__snake_case		, __snake_case		, __snake_case		, generator=__snake_case				)
																			UpperCAmelCase__		=    output.prev_sample

													UpperCAmelCase__		=    torch.sum(torch.abs(__snake_case				)				)
													UpperCAmelCase__		=    torch.mean(torch.abs(__snake_case				)				)

													assert abs(result_sum.item() - 13849.3877				) < 1E-2
													assert abs(result_mean.item() - 18.0331				) < 5E-3






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






							def 					lowerCAmelCase     (						self :     Any				):
													if torch_device == "mps":
																			return
													UpperCAmelCase__		=    self.scheduler_classes[0]
													UpperCAmelCase__		=    self.get_scheduler_config(prediction_type='''v_prediction'''				)
													UpperCAmelCase__		=    scheduler_class(**__snake_case				)

													scheduler.set_timesteps(self.num_inference_steps				)

													UpperCAmelCase__		=    self.dummy_model()
													UpperCAmelCase__		=    self.dummy_sample_deter * scheduler.init_noise_sigma
													UpperCAmelCase__		=    sample.to(__snake_case				)

													UpperCAmelCase__		=    torch.manual_seed(0				)

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

																			UpperCAmelCase__		=    model(__snake_case		, __snake_case				)

																			UpperCAmelCase__		=    scheduler.step(__snake_case		, __snake_case		, __snake_case		, generator=__snake_case				)
																			UpperCAmelCase__		=    output.prev_sample

													UpperCAmelCase__		=    torch.sum(torch.abs(__snake_case				)				)
													UpperCAmelCase__		=    torch.mean(torch.abs(__snake_case				)				)

													assert abs(result_sum.item() - 328.9970				) < 1E-2
													assert abs(result_mean.item() - 0.4284				) < 1E-3






							def 					lowerCAmelCase     (						self :     List[str]				):
													if torch_device == "mps":
																			return
													UpperCAmelCase__		=    self.scheduler_classes[0]
													UpperCAmelCase__		=    self.get_scheduler_config()
													UpperCAmelCase__		=    scheduler_class(**__snake_case				)

													scheduler.set_timesteps(self.num_inference_steps		, device=__snake_case				)
													UpperCAmelCase__		=    torch.manual_seed(0				)

													UpperCAmelCase__		=    self.dummy_model()
													UpperCAmelCase__		=    self.dummy_sample_deter.to(__snake_case				) * scheduler.init_noise_sigma

													for t in scheduler.timesteps:
																			UpperCAmelCase__		=    scheduler.scale_model_input(__snake_case		, __snake_case				)

																			UpperCAmelCase__		=    model(__snake_case		, __snake_case				)

																			UpperCAmelCase__		=    scheduler.step(__snake_case		, __snake_case		, __snake_case		, generator=__snake_case				)
																			UpperCAmelCase__		=    output.prev_sample

													UpperCAmelCase__		=    torch.sum(torch.abs(__snake_case				)				)
													UpperCAmelCase__		=    torch.mean(torch.abs(__snake_case				)				)

													assert abs(result_sum.item() - 13849.3818				) < 1E-1
													assert abs(result_mean.item() - 18.0331				) < 1E-3



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a			 =   {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "CamembertForCausalLM",
						    "CamembertForMaskedLM",
						    "CamembertForMultipleChoice",
						    "CamembertForQuestionAnswering",
						    "CamembertForSequenceClassification",
						    "CamembertForTokenClassification",
						    "CamembertModel",
						    "CamembertPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFCamembertForCausalLM",
						    "TFCamembertForMaskedLM",
						    "TFCamembertForMultipleChoice",
						    "TFCamembertForQuestionAnswering",
						    "TFCamembertForSequenceClassification",
						    "TFCamembertForTokenClassification",
						    "TFCamembertModel",
						    "TFCamembertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

						__a			 =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)