import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     (DDIMParallelScheduler,)
							UpperCAmelCase				   =     (("eta", 0.0), ("num_inference_steps", 50))







							def  UpperCamelCase_						(   self      :     Dict ,			**_A      :     Any		):
												_UpperCamelCase			     =     {
												    '''num_train_timesteps''': 1000,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''clip_sample''': True,
												}

												config.update(**_A		)
												return config







							def  UpperCamelCase_						(   self      :     str ,			**_A      :     int		):
												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config(**_A		)
												_UpperCamelCase			     =     scheduler_class(**_A		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     10, 0.0

												_UpperCamelCase			     =     self.dummy_model()
												_UpperCamelCase			     =     self.dummy_sample_deter

												scheduler.set_timesteps(_A		)

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

												return sample







							def  UpperCamelCase_						(   self      :     List[str]		):
												for timesteps in [100, 500, 1000]:
																	self.check_over_configs(num_train_timesteps=_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												for steps_offset in [0, 1]:
																	self.check_over_configs(steps_offset=_A		)

												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config(steps_offset=1		)
												_UpperCamelCase			     =     scheduler_class(**_A		)
												scheduler.set_timesteps(5		)
												assert torch.equal(scheduler.timesteps ,			torch.LongTensor([801, 601, 401, 201, 1]		)		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												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  UpperCamelCase_						(   self      :     Optional[int]		):
												for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=_A		)







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







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_A		)







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







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







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												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  UpperCamelCase_						(   self      :     str		):
												for t in [1, 10, 49]:
																	self.check_over_forward(time_step=_A		)







							def  UpperCamelCase_						(   self      :     int		):
												for t, num_inference_steps in zip([1, 10, 50] ,			[10, 50, 500]		):
																	self.check_over_forward(time_step=_A ,			num_inference_steps=_A		)







							def  UpperCamelCase_						(   self      :     Dict		):
												for t, eta in zip([1, 10, 49] ,			[0.0, 0.5, 1.0]		):
																	self.check_over_forward(time_step=_A ,			eta=_A		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config()
												_UpperCamelCase			     =     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(420 ,			400		) - 0.1_4771		)		) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(980 ,			960		) - 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(487 ,			486		) - 0.0_0979		)		) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999 ,			998		) - 0.02		)		) < 1e-5







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config()
												_UpperCamelCase			     =     scheduler_class(**_A		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     10, 0.0
												scheduler.set_timesteps(_A		)

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

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

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

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

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







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     self.full_loop()

												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     self.full_loop(prediction_type='''v_prediction'''		)

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

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







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

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







							def  UpperCamelCase_						(   self      :     List[Any]		):
												# We specify different beta, so that the first alpha is 0.99
												_UpperCamelCase			     =     self.full_loop(set_alpha_to_one=_A ,			beta_start=0.01		)
												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     torch.mean(torch.abs(_A		)		)

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

import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowerCAmelCase       =		HfApi()

_lowerCAmelCase       =		{}
# fmt: off
_lowerCAmelCase       =		torch.tensor([
    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
    1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
    -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowerCAmelCase       =		torch.tensor([
    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
    1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
    -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowerCAmelCase       =		torch.tensor([
    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
    -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
    -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowerCAmelCase       =		torch.tensor([
    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
    -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
    0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowerCAmelCase       =		torch.tensor([
    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
    -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
    0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowerCAmelCase       =		torch.tensor([
    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
    -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
    0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowerCAmelCase       =		torch.tensor([
    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
    -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
    0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowerCAmelCase       =		torch.tensor([
    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
    -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
    0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowerCAmelCase       =		torch.tensor([
    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
    1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
    -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
    1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowerCAmelCase       =		torch.tensor([
    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
    0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
    -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowerCAmelCase       =		torch.tensor([
    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
    0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
    -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowerCAmelCase       =		torch.tensor([
    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
    1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
    -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowerCAmelCase       =		torch.tensor([
    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
    1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
    -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowerCAmelCase       =		torch.tensor([
    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
    1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
    -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowerCAmelCase       =		torch.tensor([
    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
    1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
    -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on

_lowerCAmelCase       =		api.list_models(filter="diffusers")
for mod in models:
					if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
										_lowerCAmelCase       =		"/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]

										print(f'Started running {mod.modelId}!!!')

										if mod.modelId.startswith("CompVis"):
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
										else:
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint)

										torch.manual_seed(0)
										random.seed(0)

										_lowerCAmelCase       =		torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
										_lowerCAmelCase       =		torch.tensor([10] * noise.shape[0])
										with torch.no_grad():
															_lowerCAmelCase       =		model(noise, time_step).sample

										assert torch.allclose(
										    logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
										)
										print(f'{mod.modelId} has passed successfully!!!')
