



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class     UpperCamelCase_				(							_UpperCamelCase    ):
	lowercase    =				(UnCLIPScheduler,)





	def 				_lowercase(						self	,	**A					)    ->    int:
								UpperCAmelCase  :	Dict							  =	{
								    "num_train_timesteps": 1000,
								    "variance_type": "fixed_small_log",
								    "clip_sample": True,
								    "clip_sample_range": 1.0,
								    "prediction_type": "epsilon",
								}

								config.update(**A					)
								return config





	def 				_lowercase(						self					)    ->    Optional[Any]:
								for timesteps in [1, 5, 100, 1000]:
															self.check_over_configs(num_train_timesteps=A					)





	def 				_lowercase(						self					)    ->    List[str]:
								for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=A					)





	def 				_lowercase(						self					)    ->    Dict:
								for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=A					)





	def 				_lowercase(						self					)    ->    str:
								for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=A					)





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=A					)





	def 				_lowercase(						self					)    ->    Optional[int]:
								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=A	,	prev_timestep=A					)





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Optional[Any]							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	List[Any]							  =	self.get_scheduler_config(variance_type="""fixed_small_log"""					)
								UpperCAmelCase  :	Optional[Any]							  =	scheduler_class(**A					)

								assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000e-10					)					) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_5_4_9_6_2_5					)					) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9_9_9_4_9_8_7					)					) < 1e-5





	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	List[str]							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	str							  =	self.get_scheduler_config(variance_type="""learned_range"""					)
								UpperCAmelCase  :	Dict							  =	scheduler_class(**A					)

								UpperCAmelCase  :	Tuple							  =	0.5

								assert scheduler._get_variance(1	,	predicted_variance=A					) - -1_0.1_7_1_2_7_9_0 < 1e-5
								assert scheduler._get_variance(487	,	predicted_variance=A					) - -5.7_9_9_8_0_5_2 < 1e-5
								assert scheduler._get_variance(999	,	predicted_variance=A					) - -0.0_0_1_0_0_1_1 < 1e-5





	def 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	Optional[int]							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	Dict							  =	self.get_scheduler_config()
								UpperCAmelCase  :	Dict							  =	scheduler_class(**A					)

								UpperCAmelCase  :	List[str]							  =	scheduler.timesteps

								UpperCAmelCase  :	Optional[Any]							  =	self.dummy_model()
								UpperCAmelCase  :	int							  =	self.dummy_sample_deter
								UpperCAmelCase  :	Union[str, Any]							  =	torch.manual_seed(0					)

								for i, t in enumerate(A					):
															# 1. predict noise residual
															UpperCAmelCase  :	List[str]							  =	model(A	,	A					)

															# 2. predict previous mean of sample x_t-1
															UpperCAmelCase  :	Any							  =	scheduler.step(A	,	A	,	A	,	generator=A					).prev_sample

															UpperCAmelCase  :	Tuple							  =	pred_prev_sample

								UpperCAmelCase  :	Optional[int]							  =	torch.sum(torch.abs(A					)					)
								UpperCAmelCase  :	Union[str, Any]							  =	torch.mean(torch.abs(A					)					)

								assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5					) < 1e-2
								assert abs(result_mean.item() - 0.3_2_8_4_7_4_3					) < 1e-3





	def 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	Any							  =	self.scheduler_classes[0]
								UpperCAmelCase  :	Tuple							  =	self.get_scheduler_config()
								UpperCAmelCase  :	Dict							  =	scheduler_class(**A					)

								scheduler.set_timesteps(25					)

								UpperCAmelCase  :	int							  =	scheduler.timesteps

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

								for i, t in enumerate(A					):
															# 1. predict noise residual
															UpperCAmelCase  :	List[str]							  =	model(A	,	A					)

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

															# 2. predict previous mean of sample x_t-1
															UpperCAmelCase  :	int							  =	scheduler.step(
															    A	,	A	,	A	,	prev_timestep=A	,	generator=A					).prev_sample

															UpperCAmelCase  :	Dict							  =	pred_prev_sample

								UpperCAmelCase  :	List[Any]							  =	torch.sum(torch.abs(A					)					)
								UpperCAmelCase  :	str							  =	torch.mean(torch.abs(A					)					)

								assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3					) < 1e-2
								assert abs(result_mean.item() - 0.3_3_6_2_0_3_8					) < 1e-3





	def 				_lowercase(						self					)    ->    List[Any]:
								pass






	def 				_lowercase(						self					)    ->    Optional[Any]:
								pass






'''simple docstring'''




from typing import TYPE_CHECKING

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


a     :    Optional[Any]      						=					{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Dict      						=					["""BertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    List[Any]      						=					[
							    """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BertForMaskedLM""",
							    """BertForMultipleChoice""",
							    """BertForNextSentencePrediction""",
							    """BertForPreTraining""",
							    """BertForQuestionAnswering""",
							    """BertForSequenceClassification""",
							    """BertForTokenClassification""",
							    """BertLayer""",
							    """BertLMHeadModel""",
							    """BertModel""",
							    """BertPreTrainedModel""",
							    """load_tf_weights_in_bert""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    List[str]      						=					[
							    """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBertEmbeddings""",
							    """TFBertForMaskedLM""",
							    """TFBertForMultipleChoice""",
							    """TFBertForNextSentencePrediction""",
							    """TFBertForPreTraining""",
							    """TFBertForQuestionAnswering""",
							    """TFBertForSequenceClassification""",
							    """TFBertForTokenClassification""",
							    """TFBertLMHeadModel""",
							    """TFBertMainLayer""",
							    """TFBertModel""",
							    """TFBertPreTrainedModel""",
							]
try:
							if not is_tensorflow_text_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    int      						=					["""TFBertTokenizer"""]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Optional[Any]      						=					[
							    """FlaxBertForCausalLM""",
							    """FlaxBertForMaskedLM""",
							    """FlaxBertForMultipleChoice""",
							    """FlaxBertForNextSentencePrediction""",
							    """FlaxBertForPreTraining""",
							    """FlaxBertForQuestionAnswering""",
							    """FlaxBertForSequenceClassification""",
							    """FlaxBertForTokenClassification""",
							    """FlaxBertModel""",
							    """FlaxBertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
							from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_fast import BertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bert import (
														    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BertForMaskedLM,
														    BertForMultipleChoice,
														    BertForNextSentencePrediction,
														    BertForPreTraining,
														    BertForQuestionAnswering,
														    BertForSequenceClassification,
														    BertForTokenClassification,
														    BertLayer,
														    BertLMHeadModel,
														    BertModel,
														    BertPreTrainedModel,
														    load_tf_weights_in_bert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_bert import (
														    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBertEmbeddings,
														    TFBertForMaskedLM,
														    TFBertForMultipleChoice,
														    TFBertForNextSentencePrediction,
														    TFBertForPreTraining,
														    TFBertForQuestionAnswering,
														    TFBertForSequenceClassification,
														    TFBertForTokenClassification,
														    TFBertLMHeadModel,
														    TFBertMainLayer,
														    TFBertModel,
														    TFBertPreTrainedModel,
														)

							try:
														if not is_tensorflow_text_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_tf import TFBertTokenizer

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_bert import (
														    FlaxBertForCausalLM,
														    FlaxBertForMaskedLM,
														    FlaxBertForMultipleChoice,
														    FlaxBertForNextSentencePrediction,
														    FlaxBertForPreTraining,
														    FlaxBertForQuestionAnswering,
														    FlaxBertForSequenceClassification,
														    FlaxBertForTokenClassification,
														    FlaxBertModel,
														    FlaxBertPreTrainedModel,
														)

else:
							import sys

							a     :    Optional[int]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

