


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class     UpperCamelCase(  _a   ):
					snake_case_		:				Dict										=	(UnCLIPScheduler,)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    **SCREAMING_SNAKE_CASE	:		List[str]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		{
											    "num_train_timesteps": 1_0_0_0,
											    "variance_type": "fixed_small_log",
											    "clip_sample": True,
											    "clip_sample_range": 1.0,
											    "prediction_type": "epsilon",
											}

											config.update(**SCREAMING_SNAKE_CASE						)
											return config


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Union[str, Any]:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	str:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Optional[int]:




											'''simple docstring'''

											for clip_sample_range in [1, 5, 1_0, 2_0]:
																	self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Dict:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Optional[int]:




											'''simple docstring'''

											for time_step in [0, 5_0_0, 9_9_9]:
																	for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
																							if prev_timestep is not None and prev_timestep >= time_step:
																													continue

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config(variance_type="fixed_small_log"						)
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE						)

											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(4_8_7						) - 0.0549625						)						) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_9_9						) - 0.9994987						)						) < 1e-5


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config(variance_type="learned_range"						)
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE						)

											__snake_case				=		0.5

											assert scheduler._get_variance(1     ,    predicted_variance=SCREAMING_SNAKE_CASE						) - -10.1712790 < 1e-5
											assert scheduler._get_variance(4_8_7     ,    predicted_variance=SCREAMING_SNAKE_CASE						) - -5.7998052 < 1e-5
											assert scheduler._get_variance(9_9_9     ,    predicted_variance=SCREAMING_SNAKE_CASE						) - -0.0010011 < 1e-5


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE						)

											__snake_case				=		scheduler.timesteps

											__snake_case				=		self.dummy_model()
											__snake_case				=		self.dummy_sample_deter
											__snake_case				=		torch.manual_seed(0						)

											for i, t in enumerate(SCREAMING_SNAKE_CASE						):
																	# 1. predict noise residual
																	__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

																	# 2. predict previous mean of sample x_t-1
																	__snake_case				=		scheduler.step(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    generator=SCREAMING_SNAKE_CASE						).prev_sample

																	__snake_case				=		pred_prev_sample

											__snake_case				=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
											__snake_case				=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE						)

											scheduler.set_timesteps(2_5						)

											__snake_case				=		scheduler.timesteps

											__snake_case				=		self.dummy_model()
											__snake_case				=		self.dummy_sample_deter
											__snake_case				=		torch.manual_seed(0						)

											for i, t in enumerate(SCREAMING_SNAKE_CASE						):
																	# 1. predict noise residual
																	__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

																	if i + 1 == timesteps.shape[0]:
																							__snake_case				=		None
																	else:
																							__snake_case				=		timesteps[i + 1]

																	# 2. predict previous mean of sample x_t-1
																	__snake_case				=		scheduler.step(
																	    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    prev_timestep=SCREAMING_SNAKE_CASE     ,    generator=SCREAMING_SNAKE_CASE						).prev_sample

																	__snake_case				=		pred_prev_sample

											__snake_case				=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE						)						)
											__snake_case				=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Dict:




											'''simple docstring'''

											pass






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	List[Any]:




											'''simple docstring'''

											pass









from ...configuration_utils import PretrainedConfig
from ...utils import logging


A							:					str     						=     logging.get_logger(__name__)

A							:					str     						=     {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""fnet"""


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=3_2_0_0_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_2     ,    SCREAMING_SNAKE_CASE	:		Any=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu_new"     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	List[Any]:




											'''simple docstring'''

											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,    bos_token_id=SCREAMING_SNAKE_CASE     ,    eos_token_id=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											__snake_case				=		vocab_size
											__snake_case				=		max_position_embeddings
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		type_vocab_size
											__snake_case				=		layer_norm_eps
											__snake_case				=		use_tpu_fourier_optimizations
											__snake_case				=		tpu_short_seq_length





