




import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


__a    :						Optional[Any]						      =       HfApi()

__a    :						List[str]						      =       {}
# fmt: off
__a    :						List[Any]						      =       torch.tensor([
    -0.75_15, -1.68_83, 0.24_20, 0.03_00, 0.63_47, 1.34_33, -1.17_43, -3.74_67,
    1.23_42, -2.24_85, 0.46_36, 0.80_76, -0.79_91, 0.39_69, 0.84_98, 0.91_89,
    -1.88_87, -3.35_22, 0.76_39, 0.20_40, 0.62_71, -2.71_48, -1.63_16, 3.08_39,
    0.31_86, 0.27_21, -0.97_59, -1.24_61, 2.62_57, 1.35_57
])
__a    :						str						      =       torch.tensor([
    -2.36_39, -2.53_44, 0.00_54, -0.66_74, 1.59_90, 1.01_58, 0.31_24, -2.14_36,
    1.87_95, -2.54_29, -0.15_66, -0.39_73, 1.24_90, 2.64_47, 1.22_83, -0.52_08,
    -2.81_54, -3.51_19, 2.38_38, 1.20_33, 1.72_01, -2.12_56, -1.45_76, 2.79_48,
    2.42_04, -0.97_52, -1.25_46, 0.80_27, 3.27_58, 3.13_65
])
__a    :						Optional[int]						      =       torch.tensor([
    -0.65_31, -0.68_91, -0.31_72, -0.53_75, -0.91_40, -0.53_67, -0.11_75, -0.78_69,
    -0.38_08, -0.45_13, -0.20_98, -0.00_83, 0.31_83, 0.51_40, 0.22_47, -0.13_04,
    -0.13_02, -0.28_02, -0.20_84, -0.20_25, -0.49_67, -0.48_73, -0.08_61, 0.69_25,
    0.02_50, 0.12_90, -0.15_43, 0.63_16, 1.04_60, 1.49_43
])
__a    :						Optional[Any]						      =       torch.tensor([
    0.09_11, 0.11_07, 0.01_82, 0.04_35, -0.08_05, -0.06_08, 0.03_81, 0.21_72,
    -0.02_80, 0.13_27, -0.02_99, -0.02_55, -0.00_50, -0.11_70, -0.10_46, 0.03_09,
    0.13_67, 0.17_28, -0.05_33, -0.07_48, -0.05_34, 0.16_24, 0.03_84, -0.18_05,
    -0.07_07, 0.06_42, 0.02_20, -0.01_34, -0.13_33, -0.15_05
])
__a    :						Dict						      =       torch.tensor([
    0.13_21, 0.13_37, 0.04_40, 0.06_22, -0.05_91, -0.03_70, 0.05_03, 0.21_33,
    -0.01_77, 0.14_15, -0.01_16, -0.01_12, 0.00_44, -0.09_80, -0.07_89, 0.03_95,
    0.15_02, 0.17_85, -0.04_88, -0.05_14, -0.04_04, 0.15_39, 0.04_54, -0.15_59,
    -0.06_65, 0.06_59, 0.03_83, -0.00_05, -0.12_66, -0.13_86
])
__a    :						Dict						      =       torch.tensor([
    0.11_54, 0.12_18, 0.03_07, 0.05_26, -0.07_11, -0.05_41, 0.03_66, 0.20_78,
    -0.02_67, 0.13_17, -0.02_26, -0.01_93, -0.00_14, -0.10_55, -0.09_02, 0.03_30,
    0.13_91, 0.17_09, -0.05_62, -0.06_93, -0.05_60, 0.14_82, 0.03_81, -0.16_83,
    -0.06_81, 0.06_61, 0.03_31, -0.00_46, -0.12_68, -0.14_31
])
__a    :						str						      =       torch.tensor([
    0.11_92, 0.12_40, 0.04_14, 0.06_06, -0.05_57, -0.04_12, 0.04_30, 0.20_42,
    -0.02_00, 0.13_85, -0.01_15, -0.01_32, 0.00_17, -0.09_65, -0.08_02, 0.03_98,
    0.14_33, 0.17_47, -0.04_58, -0.05_33, -0.04_07, 0.15_45, 0.04_19, -0.15_74,
    -0.06_45, 0.06_26, 0.03_41, -0.00_10, -0.11_99, -0.13_90
])
__a    :						Tuple						      =       torch.tensor([
    0.10_75, 0.10_74, 0.02_05, 0.04_31, -0.07_74, -0.06_07, 0.02_98, 0.20_42,
    -0.03_20, 0.12_67, -0.02_81, -0.02_50, -0.00_64, -0.10_91, -0.09_46, 0.02_90,
    0.13_28, 0.16_50, -0.05_80, -0.07_38, -0.05_86, 0.14_40, 0.03_37, -0.17_46,
    -0.07_12, 0.06_05, 0.02_50, -0.00_99, -0.13_16, -0.14_73
])
__a    :						List[Any]						      =       torch.tensor([
    -1.45_72, -2.04_81, -0.04_14, -0.60_05, 1.41_36, 0.58_48, 0.40_28, -2.73_30,
    1.22_12, -2.12_28, 0.21_55, 0.40_39, 0.76_62, 2.05_35, 0.74_77, -0.32_43,
    -2.17_58, -2.76_48, 1.69_47, 0.70_26, 1.23_38, -1.60_78, -0.86_82, 2.28_10,
    1.85_74, -0.57_18, -0.55_86, -0.01_86, 2.34_15, 2.12_51])
__a    :						Dict						      =       torch.tensor([
    -1.36_90, -1.97_20, -0.40_90, -0.69_66, 1.46_60, 0.99_38, -0.13_85, -2.73_24,
    0.77_36, -1.89_17, 0.29_23, 0.42_93, 0.16_93, 1.41_12, 1.18_87, -0.31_81,
    -2.21_60, -2.63_81, 1.31_70, 0.81_63, 0.92_40, -1.65_44, -0.60_99, 2.52_59,
    1.64_30, -0.90_90, -0.93_92, -0.01_26, 2.42_68, 2.32_66
])
__a    :						Tuple						      =       torch.tensor([
    -1.35_25, -1.96_28, -0.39_56, -0.68_60, 1.46_64, 1.00_14, -0.12_59, -2.72_12,
    0.77_72, -1.88_11, 0.29_96, 0.43_88, 0.17_04, 1.40_29, 1.17_01, -0.30_27,
    -2.20_53, -2.62_87, 1.33_50, 0.81_31, 0.92_74, -1.62_92, -0.60_98, 2.51_31,
    1.65_05, -0.89_58, -0.92_98, -0.01_51, 2.42_57, 2.33_55
])
__a    :						List[str]						      =       torch.tensor([
    -2.05_85, -2.78_97, -0.28_50, -0.89_40, 1.90_52, 0.57_02, 0.63_45, -3.89_59,
    1.59_32, -3.23_19, 0.19_74, 0.02_87, 1.75_66, 2.65_43, 0.83_87, -0.53_51,
    -3.27_36, -4.33_75, 2.90_29, 1.63_90, 1.46_40, -2.17_01, -1.90_13, 2.93_41,
    3.49_81, -0.62_55, -1.16_44, -0.15_91, 3.70_97, 3.20_66
])
__a    :						List[str]						      =       torch.tensor([
    -2.31_39, -2.55_94, -0.01_97, -0.67_85, 1.70_01, 1.16_06, 0.30_75, -2.17_40,
    1.80_71, -2.56_30, -0.09_26, -0.38_11, 1.21_16, 2.62_46, 1.27_31, -0.53_98,
    -2.81_53, -3.61_40, 2.38_93, 1.32_62, 1.62_58, -2.18_56, -1.32_67, 2.83_95,
    2.37_79, -1.06_23, -1.24_68, 0.89_59, 3.33_67, 3.22_43
])
__a    :						int						      =       torch.tensor([
    -2.06_28, -2.76_67, -0.20_89, -0.82_63, 2.05_39, 0.59_92, 0.64_95, -3.83_36,
    1.60_25, -3.28_17, 0.17_21, -0.06_33, 1.75_16, 2.70_39, 0.81_00, -0.59_08,
    -3.21_13, -4.43_43, 2.92_57, 1.36_32, 1.55_62, -2.14_89, -1.98_94, 3.05_60,
    3.33_96, -0.73_28, -1.04_17, 0.03_83, 3.70_93, 3.23_43
])
__a    :						str						      =       torch.tensor([
    -1.45_74, -2.05_69, -0.04_73, -0.61_17, 1.40_18, 0.57_69, 0.41_29, -2.73_44,
    1.22_41, -2.13_97, 0.20_00, 0.39_37, 0.76_16, 2.04_53, 0.73_24, -0.33_91,
    -2.17_46, -2.77_44, 1.69_63, 0.69_21, 1.21_87, -1.61_72, -0.88_77, 2.24_39,
    1.84_71, -0.58_39, -0.56_05, -0.04_64, 2.32_50, 2.12_19
])
# fmt: on

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

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

												if mod.modelId.startswith('''CompVis'''):
																		__a    :						Optional[Any]						      =       UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
												else:
																		__a    :						List[str]						      =       UNetaDModel.from_pretrained(local_checkpoint)

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

												__a    :						Optional[int]						      =       torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
												__a    :						int						      =       torch.tensor([1_0] * noise.shape[0])
												with torch.no_grad():
																		__a    :						Optional[int]						      =       model(noise, time_step).sample

												assert torch.allclose(
												    logits[0, 0, 0, :3_0], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1E-3
												)
												print(f'{mod.modelId} has passed successfully!!!')







from __future__ import annotations




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   None:






            '''simple docstring'''





            __snake_case      :str 							=    order

            # a_{0} ... a_{k}
            __snake_case      :int 							=    [1.0] + [0.0] * order
            # b_{0} ... b_{k}
            __snake_case      :Any 							=    [1.0] + [0.0] * order

            # x[n-1] ... x[n-k]
            __snake_case      :List[Any] 							=    [0.0] * self.order
            # y[n-1] ... y[n-k]
            __snake_case      :int 							=    [0.0] * self.order



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   None:






            '''simple docstring'''





            if len(a__     ) < self.order:
                 __snake_case      :Optional[int] 							=    [1.0, *a_coeffs]

            if len(a__     ) != self.order + 1:
                 __snake_case      :Dict 							=    (
                     F'''Expected a_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            if len(a__     ) != self.order + 1:
                 __snake_case      :Optional[Any] 							=    (
                     F'''Expected b_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            __snake_case      :str 							=    a_coeffs
            __snake_case      :str 							=    b_coeffs



       def        __lowercase    (				self      ,    a__     )			->   float:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    0.0

            # Start at index 1 and do index 0 at the end.
            for i in range(1      ,    self.order + 1     ):
                 result += (
                     self.b_coeffs[i] * self.input_history[i - 1]
                     - self.a_coeffs[i] * self.output_history[i - 1]
                 )

            __snake_case      :Dict 							=    (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

            __snake_case      :Optional[int] 							=    self.input_history[:-1]
            __snake_case      :Optional[Any] 							=    self.output_history[:-1]

            __snake_case      :Union[str, Any] 							=    sample
            __snake_case      :Optional[Any] 							=    result

            return result


