

import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: List[str]												=	(HeunDiscreteScheduler,)
	_lowercase	: Optional[Any]												=	1_0







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			**__UpperCamelCase:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     {
						    'num_train_timesteps': 11_00,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__UpperCamelCase   )
						return config







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			List[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=__UpperCamelCase	,			beta_end=__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





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







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

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





						__magic_name__				=     self.scheduler_classes[0]
						__magic_name__				=     self.get_scheduler_config()
						__magic_name__				=     scheduler_class(**__UpperCamelCase   )

						scheduler.set_timesteps(self.num_inference_steps   )

						__magic_name__				=     self.dummy_model()
						__magic_name__				=     self.dummy_sample_deter * scheduler.init_noise_sigma
						__magic_name__				=     sample.to(__UpperCamelCase   )

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

											__magic_name__				=     model(__UpperCamelCase	,			__UpperCamelCase   )

											__magic_name__				=     scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase   )
											__magic_name__				=     output.prev_sample

						__magic_name__				=     torch.sum(torch.abs(__UpperCamelCase   )   )
						__magic_name__				=     torch.mean(torch.abs(__UpperCamelCase   )   )

						if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 0.1233   ) < 1E-2
											assert abs(result_mean.item() - 0.0002   ) < 1E-3
						else:
											# CUDA
											assert abs(result_sum.item() - 0.1233   ) < 1E-2
											assert abs(result_mean.item() - 0.0002   ) < 1E-3







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     self.scheduler_classes[0]
						__magic_name__				=     self.get_scheduler_config(prediction_type='v_prediction'   )
						__magic_name__				=     scheduler_class(**__UpperCamelCase   )

						scheduler.set_timesteps(self.num_inference_steps   )

						__magic_name__				=     self.dummy_model()
						__magic_name__				=     self.dummy_sample_deter * scheduler.init_noise_sigma
						__magic_name__				=     sample.to(__UpperCamelCase   )

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

											__magic_name__				=     model(__UpperCamelCase	,			__UpperCamelCase   )

											__magic_name__				=     scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase   )
											__magic_name__				=     output.prev_sample

						__magic_name__				=     torch.sum(torch.abs(__UpperCamelCase   )   )
						__magic_name__				=     torch.mean(torch.abs(__UpperCamelCase   )   )

						if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.69_34E-07   ) < 1E-2
											assert abs(result_mean.item() - 6.11_12E-10   ) < 1E-3
						else:
											# CUDA
											assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07   ) < 1E-2
											assert abs(result_mean.item() - 0.0002   ) < 1E-3







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.scheduler_classes[0]
						__magic_name__				=     self.get_scheduler_config()
						__magic_name__				=     scheduler_class(**__UpperCamelCase   )

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

						__magic_name__				=     self.dummy_model()
						__magic_name__				=     self.dummy_sample_deter.to(__UpperCamelCase   ) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
											__magic_name__				=     scheduler.scale_model_input(__UpperCamelCase	,			__UpperCamelCase   )

											__magic_name__				=     model(__UpperCamelCase	,			__UpperCamelCase   )

											__magic_name__				=     scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase   )
											__magic_name__				=     output.prev_sample

						__magic_name__				=     torch.sum(torch.abs(__UpperCamelCase   )   )
						__magic_name__				=     torch.mean(torch.abs(__UpperCamelCase   )   )

						if str(__UpperCamelCase   ).startswith('cpu'   ):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 0.1233   ) < 1E-2
											assert abs(result_mean.item() - 0.0002   ) < 1E-3
						elif str(__UpperCamelCase   ).startswith('mps'   ):
											# Larger tolerance on mps
											assert abs(result_mean.item() - 0.0002   ) < 1E-2
						else:
											# CUDA
											assert abs(result_sum.item() - 0.1233   ) < 1E-2
											assert abs(result_mean.item() - 0.0002   ) < 1E-3







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.scheduler_classes[0]
						__magic_name__				=     self.get_scheduler_config()
						__magic_name__				=     scheduler_class(**__UpperCamelCase	,			use_karras_sigmas=__UpperCamelCase   )

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

						__magic_name__				=     self.dummy_model()
						__magic_name__				=     self.dummy_sample_deter.to(__UpperCamelCase   ) * scheduler.init_noise_sigma
						__magic_name__				=     sample.to(__UpperCamelCase   )

						for t in scheduler.timesteps:
											__magic_name__				=     scheduler.scale_model_input(__UpperCamelCase	,			__UpperCamelCase   )

											__magic_name__				=     model(__UpperCamelCase	,			__UpperCamelCase   )

											__magic_name__				=     scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase   )
											__magic_name__				=     output.prev_sample

						__magic_name__				=     torch.sum(torch.abs(__UpperCamelCase   )   )
						__magic_name__				=     torch.mean(torch.abs(__UpperCamelCase   )   )

						assert abs(result_sum.item() - 0.00015   ) < 1E-2
						assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07   ) < 1E-2







from typing import TYPE_CHECKING

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


A__							=				{"configuration_pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig"]}

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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PegasusForCausalLM",
						    "PegasusForConditionalGeneration",
						    "PegasusModel",
						    "PegasusPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "TFPegasusForConditionalGeneration",
						    "TFPegasusModel",
						    "TFPegasusPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "FlaxPegasusForConditionalGeneration",
						    "FlaxPegasusModel",
						    "FlaxPegasusPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus import PegasusTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus_fast import PegasusTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_pegasus import (
												    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PegasusForCausalLM,
												    PegasusForConditionalGeneration,
												    PegasusModel,
												    PegasusPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_pegasus import (
												    FlaxPegasusForConditionalGeneration,
												    FlaxPegasusModel,
												    FlaxPegasusPreTrainedModel,
												)

else:
						import sys

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




