





"""simple docstring"""





from typing import TYPE_CHECKING

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


_UpperCamelCase							=      {
    '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:
							_UpperCamelCase							=      [
							    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'Data2VecAudioForAudioFrameClassification',
							    'Data2VecAudioForCTC',
							    'Data2VecAudioForSequenceClassification',
							    'Data2VecAudioForXVector',
							    'Data2VecAudioModel',
							    'Data2VecAudioPreTrainedModel',
							]
							_UpperCamelCase							=      [
							    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'Data2VecTextForCausalLM',
							    'Data2VecTextForMaskedLM',
							    'Data2VecTextForMultipleChoice',
							    'Data2VecTextForQuestionAnswering',
							    'Data2VecTextForSequenceClassification',
							    'Data2VecTextForTokenClassification',
							    'Data2VecTextModel',
							    'Data2VecTextPreTrainedModel',
							]
							_UpperCamelCase							=      [
							    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'Data2VecVisionForImageClassification',
							    'Data2VecVisionForMaskedImageModeling',
							    'Data2VecVisionForSemanticSegmentation',
							    'Data2VecVisionModel',
							    'Data2VecVisionPreTrainedModel',
							]

if is_tf_available():
							_UpperCamelCase							=      [
							    '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

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











"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[Any]	 =		(UnCLIPScheduler,)




				def 					__lowercase  (     self  :Any						,    **__lowercase  :str			):
											__lowerCamelCase      :      Optional[int]         ={
											    '''num_train_timesteps''': 1000,
											    '''variance_type''': '''fixed_small_log''',
											    '''clip_sample''': True,
											    '''clip_sample_range''': 1.0,
											    '''prediction_type''': '''epsilon''',
											}

											config.update(**__lowercase			)
											return config




				def 					__lowercase  (     self  :Tuple			):
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=__lowercase			)




				def 					__lowercase  (     self  :Tuple			):
											for variance in ["fixed_small_log", "learned_range"]:
																		self.check_over_configs(variance_type=__lowercase			)




				def 					__lowercase  (     self  :List[str]			):
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=__lowercase			)




				def 					__lowercase  (     self  :List[Any]			):
											for clip_sample_range in [1, 5, 10, 20]:
																		self.check_over_configs(clip_sample_range=__lowercase			)




				def 					__lowercase  (     self  :Any			):
											for prediction_type in ["epsilon", "sample"]:
																		self.check_over_configs(prediction_type=__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											for time_step in [0, 500, 999]:
																		for prev_timestep in [None, 5, 100, 250, 500, 750]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																																continue

																									self.check_over_forward(time_step=__lowercase						,    prev_timestep=__lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      List[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      str         =self.get_scheduler_config(variance_type='''fixed_small_log'''			)
											__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)

											assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_0_0_0e-1_0			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0549625			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9994987			)			) < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Tuple         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[Any]         =self.get_scheduler_config(variance_type='''learned_range'''			)
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Optional[int]         =0.5

											assert scheduler._get_variance(1						,    predicted_variance=__lowercase			) - -10.1712790 < 1e-5
											assert scheduler._get_variance(487						,    predicted_variance=__lowercase			) - -5.7998052 < 1e-5
											assert scheduler._get_variance(999						,    predicted_variance=__lowercase			) - -0.0010011 < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Any         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[int]         =self.get_scheduler_config()
											__lowerCamelCase      :      str         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Tuple         =scheduler.timesteps

											__lowerCamelCase      :      Any         =self.dummy_model()
											__lowerCamelCase      :      Any         =self.dummy_sample_deter
											__lowerCamelCase      :      List[Any]         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Tuple         =model(__lowercase						,    __lowercase			)

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      List[Any]         =pred_prev_sample

											__lowerCamelCase      :      Optional[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 252.2682495			) < 1e-2
											assert abs(result_mean.item() - 0.3284743			) < 1e-3




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Optional[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      Any         =self.get_scheduler_config()
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											scheduler.set_timesteps(25			)

											__lowerCamelCase      :      Dict         =scheduler.timesteps

											__lowerCamelCase      :      List[str]         =self.dummy_model()
											__lowerCamelCase      :      List[str]         =self.dummy_sample_deter
											__lowerCamelCase      :      Any         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    __lowercase			)

																		if i + 1 == timesteps.shape[0]:
																									__lowerCamelCase      :      Any         =None
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =timesteps[i + 1]

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[str]         =scheduler.step(
																		    __lowercase						,    __lowercase						,    __lowercase						,    prev_timestep=__lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      int         =pred_prev_sample

											__lowerCamelCase      :      List[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      List[str]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 258.2044983			) < 1e-2
											assert abs(result_mean.item() - 0.3362038			) < 1e-3




				def 					__lowercase  (     self  :List[str]			):
											pass




				def 					__lowercase  (     self  :Tuple			):
											pass




