

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =(DDIMParallelScheduler,)
							__UpperCAmelCase       =(("""eta""", 0.0), ("""num_inference_steps""", 50))




							def        A__  ( self					,						**_A							):
												__lowerCAmelCase					=							{
												    'num_train_timesteps': 1_0_0_0,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'clip_sample': True,
												}

												config.update(**_A							)
												return config




							def        A__  ( self					,						**_A							):
												__lowerCAmelCase					=							self.scheduler_classes[0]
												__lowerCAmelCase					=							self.get_scheduler_config(**_A							)
												__lowerCAmelCase					=							scheduler_class(**_A							)

												__lowerCAmelCase,	__lowerCAmelCase					=							1_0, 0.0

												__lowerCAmelCase					=							self.dummy_model()
												__lowerCAmelCase					=							self.dummy_sample_deter

												scheduler.set_timesteps(_A							)

												for t in scheduler.timesteps:
																	__lowerCAmelCase					=							model(_A					,						_A							)
																	__lowerCAmelCase					=							scheduler.step(_A					,						_A					,						_A					,						_A							).prev_sample

												return sample




							def        A__  ( self							):
												for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_A							)




							def        A__  ( self							):
												for steps_offset in [0, 1]:
																	self.check_over_configs(steps_offset=_A							)

												__lowerCAmelCase					=							self.scheduler_classes[0]
												__lowerCAmelCase					=							self.get_scheduler_config(steps_offset=1							)
												__lowerCAmelCase					=							scheduler_class(**_A							)
												scheduler.set_timesteps(5							)
												assert torch.equal(scheduler.timesteps					,						torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]							)							)




							def        A__  ( self							):
												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,						[0.002, 0.02, 0.2, 2]							):
																	self.check_over_configs(beta_start=_A					,						beta_end=_A							)




							def        A__  ( self							):
												for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=_A							)




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




							def        A__  ( self							):
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_A							)




							def        A__  ( self							):
												for timestep_spacing in ["trailing", "leading"]:
																	self.check_over_configs(timestep_spacing=_A							)




							def        A__  ( self							):
												for rescale_betas_zero_snr in [True, False]:
																	self.check_over_configs(rescale_betas_zero_snr=_A							)




							def        A__  ( self							):
												self.check_over_configs(thresholding=_A							)
												for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=_A					,						prediction_type=_A					,						sample_max_value=_A					,						)




							def        A__  ( self							):
												for t in [1, 1_0, 4_9]:
																	self.check_over_forward(time_step=_A							)




							def        A__  ( self							):
												for t, num_inference_steps in zip([1, 1_0, 5_0]					,						[1_0, 5_0, 5_0_0]							):
																	self.check_over_forward(time_step=_A					,						num_inference_steps=_A							)




							def        A__  ( self							):
												for t, eta in zip([1, 1_0, 4_9]					,						[0.0, 0.5, 1.0]							):
																	self.check_over_forward(time_step=_A					,						eta=_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.scheduler_classes[0]
												__lowerCAmelCase					=							self.get_scheduler_config()
												__lowerCAmelCase					=							scheduler_class(**_A							)

												assert torch.sum(torch.abs(scheduler._get_variance(0					,						0							) - 0.0							)							) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_2_0					,						4_0_0							) - 0.1_4771							)							) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_8_0					,						9_6_0							) - 0.3_2460							)							) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(0					,						0							) - 0.0							)							) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_8_7					,						4_8_6							) - 0.0_0979							)							) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_9_9					,						9_9_8							) - 0.02							)							) < 1e-5




							def        A__  ( self							):
												__lowerCAmelCase					=							self.scheduler_classes[0]
												__lowerCAmelCase					=							self.get_scheduler_config()
												__lowerCAmelCase					=							scheduler_class(**_A							)

												__lowerCAmelCase,	__lowerCAmelCase					=							1_0, 0.0
												scheduler.set_timesteps(_A							)

												__lowerCAmelCase					=							self.dummy_model()
												__lowerCAmelCase					=							self.dummy_sample_deter
												__lowerCAmelCase					=							self.dummy_sample_deter + 0.1
												__lowerCAmelCase					=							self.dummy_sample_deter - 0.1

												__lowerCAmelCase					=							samplea.shape[0]
												__lowerCAmelCase					=							torch.stack([samplea, samplea, samplea]					,						dim=0							)
												__lowerCAmelCase					=							torch.arange(_A							)[0:3, None].repeat(1					,						_A							)

												__lowerCAmelCase					=							model(samples.flatten(0					,						1							)					,						timesteps.flatten(0					,						1							)							)
												__lowerCAmelCase					=							scheduler.batch_step_no_noise(_A					,						timesteps.flatten(0					,						1							)					,						samples.flatten(0					,						1							)					,						_A							)

												__lowerCAmelCase					=							torch.sum(torch.abs(_A							)							)
												__lowerCAmelCase					=							torch.mean(torch.abs(_A							)							)

												assert abs(result_sum.item() - 1147.7904							) < 1e-2
												assert abs(result_mean.item() - 0.4982							) < 1e-3




							def        A__  ( self							):
												__lowerCAmelCase					=							self.full_loop()

												__lowerCAmelCase					=							torch.sum(torch.abs(_A							)							)
												__lowerCAmelCase					=							torch.mean(torch.abs(_A							)							)

												assert abs(result_sum.item() - 172.0067							) < 1e-2
												assert abs(result_mean.item() - 0.22_3967							) < 1e-3




							def        A__  ( self							):
												__lowerCAmelCase					=							self.full_loop(prediction_type='v_prediction'							)

												__lowerCAmelCase					=							torch.sum(torch.abs(_A							)							)
												__lowerCAmelCase					=							torch.mean(torch.abs(_A							)							)

												assert abs(result_sum.item() - 52.5302							) < 1e-2
												assert abs(result_mean.item() - 0.0684							) < 1e-3




							def        A__  ( self							):
												# We specify different beta, so that the first alpha is 0.99
												__lowerCAmelCase					=							self.full_loop(set_alpha_to_one=_A					,						beta_start=0.01							)
												__lowerCAmelCase					=							torch.sum(torch.abs(_A							)							)
												__lowerCAmelCase					=							torch.mean(torch.abs(_A							)							)

												assert abs(result_sum.item() - 149.8295							) < 1e-2
												assert abs(result_mean.item() - 0.1951							) < 1e-3




							def        A__  ( self							):
												# We specify different beta, so that the first alpha is 0.99
												__lowerCAmelCase					=							self.full_loop(set_alpha_to_one=_A					,						beta_start=0.01							)
												__lowerCAmelCase					=							torch.sum(torch.abs(_A							)							)
												__lowerCAmelCase					=							torch.mean(torch.abs(_A							)							)

												assert abs(result_sum.item() - 149.0784							) < 1e-2
												assert abs(result_mean.item() - 0.1941							) < 1e-3









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase      				=     {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecAudioForAudioFrameClassification''',
		    '''Data2VecAudioForCTC''',
		    '''Data2VecAudioForSequenceClassification''',
		    '''Data2VecAudioForXVector''',
		    '''Data2VecAudioModel''',
		    '''Data2VecAudioPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecTextForCausalLM''',
		    '''Data2VecTextForMaskedLM''',
		    '''Data2VecTextForMultipleChoice''',
		    '''Data2VecTextForQuestionAnswering''',
		    '''Data2VecTextForSequenceClassification''',
		    '''Data2VecTextForTokenClassification''',
		    '''Data2VecTextModel''',
		    '''Data2VecTextPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecVisionForImageClassification''',
		    '''Data2VecVisionForMaskedImageModeling''',
		    '''Data2VecVisionForSemanticSegmentation''',
		    '''Data2VecVisionModel''',
		    '''Data2VecVisionPreTrainedModel''',
		]

if is_tf_available():
		lowerCamelCase      				=     [
		    '''TFData2VecVisionForImageClassification''',
		    '''TFData2VecVisionForSemanticSegmentation''',
		    '''TFData2VecVisionModel''',
		    '''TFData2VecVisionPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
		from .configuration_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecTextConfig,
		    DataaVecTextOnnxConfig,
		)
		from .configuration_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecVisionConfig,
		    DataaVecVisionOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dataavec_audio import (
				    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecAudioForAudioFrameClassification,
				    DataaVecAudioForCTC,
				    DataaVecAudioForSequenceClassification,
				    DataaVecAudioForXVector,
				    DataaVecAudioModel,
				    DataaVecAudioPreTrainedModel,
				)
				from .modeling_dataavec_text import (
				    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecTextForCausalLM,
				    DataaVecTextForMaskedLM,
				    DataaVecTextForMultipleChoice,
				    DataaVecTextForQuestionAnswering,
				    DataaVecTextForSequenceClassification,
				    DataaVecTextForTokenClassification,
				    DataaVecTextModel,
				    DataaVecTextPreTrainedModel,
				)
				from .modeling_dataavec_vision import (
				    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecVisionForImageClassification,
				    DataaVecVisionForMaskedImageModeling,
				    DataaVecVisionForSemanticSegmentation,
				    DataaVecVisionModel,
				    DataaVecVisionPreTrainedModel,
				)
		if is_tf_available():
				from .modeling_tf_dataavec_vision import (
				    TFDataaVecVisionForImageClassification,
				    TFDataaVecVisionForSemanticSegmentation,
				    TFDataaVecVisionModel,
				    TFDataaVecVisionPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






