




"""simple docstring"""


import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class    A_     (					SCREAMING_SNAKE_CASE__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       (HeunDiscreteScheduler,)
							SCREAMING_SNAKE_CASE_     				=       10





							def 				UpperCAmelCase__							(  self  :str					,							**lowerCamelCase_  :List[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Tuple		={
										    'num_train_timesteps': 1_100,
										    'beta_start': 0.00_01,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										}

										config.update(**lowerCamelCase_	)
										return config





							def 				UpperCAmelCase__							(  self  :Tuple	):





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :Optional[Any]	):





										"""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=lowerCamelCase_					,							beta_end=lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

										for clip_sample_range in [1.0, 2.0, 3.0]:
													self.check_over_configs(clip_sample_range=lowerCamelCase_					,							clip_sample=lowerCamelCase_	)





							def 				UpperCAmelCase__							(  self  :Optional[Any]	):





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :Any	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=self.scheduler_classes[0]
										lowerCamelCase__     :					Optional[int]		=self.get_scheduler_config()
										lowerCamelCase__     :					Optional[int]		=scheduler_class(**lowerCamelCase_	)

										scheduler.set_timesteps(self.num_inference_steps	)

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

										for i, t in enumerate(scheduler.timesteps	):
													lowerCamelCase__     :					List[str]		=scheduler.scale_model_input(lowerCamelCase_					,							lowerCamelCase_	)

													lowerCamelCase__     :					str		=model(lowerCamelCase_					,							lowerCamelCase_	)

													lowerCamelCase__     :					Optional[Any]		=scheduler.step(lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	)
													lowerCamelCase__     :					Optional[int]		=output.prev_sample

										lowerCamelCase__     :					Tuple		=torch.sum(torch.abs(lowerCamelCase_	)	)
										lowerCamelCase__     :					Any		=torch.mean(torch.abs(lowerCamelCase_	)	)

										if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 0.12_33	) < 1e-2
													assert abs(result_mean.item() - 0.00_02	) < 1e-3
										else:
													# CUDA
													assert abs(result_sum.item() - 0.12_33	) < 1e-2
													assert abs(result_mean.item() - 0.00_02	) < 1e-3





							def 				UpperCAmelCase__							(  self  :List[str]	):





										"""simple docstring"""

										lowerCamelCase__     :					Any		=self.scheduler_classes[0]
										lowerCamelCase__     :					Tuple		=self.get_scheduler_config(prediction_type='v_prediction'	)
										lowerCamelCase__     :					Optional[Any]		=scheduler_class(**lowerCamelCase_	)

										scheduler.set_timesteps(self.num_inference_steps	)

										lowerCamelCase__     :					str		=self.dummy_model()
										lowerCamelCase__     :					Tuple		=self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCamelCase__     :					List[str]		=sample.to(lowerCamelCase_	)

										for i, t in enumerate(scheduler.timesteps	):
													lowerCamelCase__     :					Dict		=scheduler.scale_model_input(lowerCamelCase_					,							lowerCamelCase_	)

													lowerCamelCase__     :					Union[str, Any]		=model(lowerCamelCase_					,							lowerCamelCase_	)

													lowerCamelCase__     :					Dict		=scheduler.step(lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	)
													lowerCamelCase__     :					str		=output.prev_sample

										lowerCamelCase__     :					str		=torch.sum(torch.abs(lowerCamelCase_	)	)
										lowerCamelCase__     :					Optional[Any]		=torch.mean(torch.abs(lowerCamelCase_	)	)

										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() - 4.693_4286_5017_0972e-07	) < 1e-2
													assert abs(result_mean.item() - 0.00_02	) < 1e-3





							def 				UpperCAmelCase__							(  self  :Optional[int]	):





										"""simple docstring"""

										lowerCamelCase__     :					str		=self.scheduler_classes[0]
										lowerCamelCase__     :					Union[str, Any]		=self.get_scheduler_config()
										lowerCamelCase__     :					Any		=scheduler_class(**lowerCamelCase_	)

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

										lowerCamelCase__     :					Dict		=self.dummy_model()
										lowerCamelCase__     :					Union[str, Any]		=self.dummy_sample_deter.to(lowerCamelCase_	) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
													lowerCamelCase__     :					Tuple		=scheduler.scale_model_input(lowerCamelCase_					,							lowerCamelCase_	)

													lowerCamelCase__     :					Tuple		=model(lowerCamelCase_					,							lowerCamelCase_	)

													lowerCamelCase__     :					Optional[Any]		=scheduler.step(lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	)
													lowerCamelCase__     :					str		=output.prev_sample

										lowerCamelCase__     :					Any		=torch.sum(torch.abs(lowerCamelCase_	)	)
										lowerCamelCase__     :					Any		=torch.mean(torch.abs(lowerCamelCase_	)	)

										if str(lowerCamelCase_	).startswith('cpu'	):
													# The following sum varies between 148 and 156 on mps. Why?
													assert abs(result_sum.item() - 0.12_33	) < 1e-2
													assert abs(result_mean.item() - 0.00_02	) < 1e-3
										elif str(lowerCamelCase_	).startswith('mps'	):
													# Larger tolerance on mps
													assert abs(result_mean.item() - 0.00_02	) < 1e-2
										else:
													# CUDA
													assert abs(result_sum.item() - 0.12_33	) < 1e-2
													assert abs(result_mean.item() - 0.00_02	) < 1e-3





							def 				UpperCAmelCase__							(  self  :Optional[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Any		=self.scheduler_classes[0]
										lowerCamelCase__     :					Dict		=self.get_scheduler_config()
										lowerCamelCase__     :					int		=scheduler_class(**lowerCamelCase_					,							use_karras_sigmas=lowerCamelCase_	)

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

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

										for t in scheduler.timesteps:
													lowerCamelCase__     :					List[str]		=scheduler.scale_model_input(lowerCamelCase_					,							lowerCamelCase_	)

													lowerCamelCase__     :					List[str]		=model(lowerCamelCase_					,							lowerCamelCase_	)

													lowerCamelCase__     :					str		=scheduler.step(lowerCamelCase_					,							lowerCamelCase_					,							lowerCamelCase_	)
													lowerCamelCase__     :					Dict		=output.prev_sample

										lowerCamelCase__     :					Optional[Any]		=torch.sum(torch.abs(lowerCamelCase_	)	)
										lowerCamelCase__     :					Optional[Any]		=torch.mean(torch.abs(lowerCamelCase_	)	)

										assert abs(result_sum.item() - 0.0_00_15	) < 1e-2
										assert abs(result_mean.item() - 1.9869_5545_3503_4695e-07	) < 1e-2

"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase								=						{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""DebertaV2TokenizerFast"""]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDebertaV2ForMaskedLM""",
							    """TFDebertaV2ForQuestionAnswering""",
							    """TFDebertaV2ForSequenceClassification""",
							    """TFDebertaV2ForTokenClassification""",
							    """TFDebertaV2Model""",
							    """TFDebertaV2PreTrainedModel""",
							]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DebertaV2ForMaskedLM""",
							    """DebertaV2ForMultipleChoice""",
							    """DebertaV2ForQuestionAnswering""",
							    """DebertaV2ForSequenceClassification""",
							    """DebertaV2ForTokenClassification""",
							    """DebertaV2Model""",
							    """DebertaV2PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_deberta_va import (
							    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DebertaVaConfig,
							    DebertaVaOnnxConfig,
							)
							from .tokenization_deberta_va import DebertaVaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta_va import (
														    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaVaForMaskedLM,
														    TFDebertaVaForQuestionAnswering,
														    TFDebertaVaForSequenceClassification,
														    TFDebertaVaForTokenClassification,
														    TFDebertaVaModel,
														    TFDebertaVaPreTrainedModel,
														)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta_va import (
														    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaVaForMaskedLM,
														    DebertaVaForMultipleChoice,
														    DebertaVaForQuestionAnswering,
														    DebertaVaForSequenceClassification,
														    DebertaVaForTokenClassification,
														    DebertaVaModel,
														    DebertaVaPreTrainedModel,
														)

else:
							import sys

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