import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						Optional[Any]      =				DDIMPipeline
    a__     :						str      =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
    a__     :						Any      =				PipelineTesterMixin.required_optional_params - {
        """num_images_per_prompt""",
        """latents""",
        """callback""",
        """callback_steps""",
    }
    a__     :						str      =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
    a__     :						Optional[Any]      =				False







    def       UpperCamelCase__       (     self)	->     Tuple:
        torch.manual_seed(0)
        __UpperCamelCase					:Optional[int]        =		UNetaDModel(
            block_out_channels=(32, 64)			,			layers_per_block=2			,			sample_size=32			,			in_channels=3			,			out_channels=3			,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')			,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')			,			)
        __UpperCamelCase					:int        =		DDIMScheduler()
        __UpperCamelCase					:Any        =		{'''unet''': unet, '''scheduler''': scheduler}
        return components







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase=0)	->     Optional[Any]:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase					:List[Any]        =		torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase					:List[str]        =		torch.Generator(device=__lowercase).manual_seed(__lowercase)
        __UpperCamelCase					:List[Any]        =		{
            '''batch_size''': 1,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }
        return inputs







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:List[Any]        =		'''cpu'''

        __UpperCamelCase					:int        =		self.get_dummy_components()
        __UpperCamelCase					:List[Any]        =		self.pipeline_class(**__lowercase)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase					:Any        =		self.get_dummy_inputs(__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		pipe(**__lowercase).images
        __UpperCamelCase					:List[Any]        =		image[0, -3:, -3:, -1]

        self.assertEqual(image.shape			,			(1, 32, 32, 3))
        __UpperCamelCase					:int        =		np.array(
            [1.0_0_0E0_0, 5.7_1_7E-0_1, 4.7_1_7E-0_1, 1.0_0_0E0_0, 0.0_0_0E0_0, 1.0_0_0E0_0, 3.0_0_0E-0_4, 0.0_0_0E0_0, 9.0_0_0E-0_4])
        __UpperCamelCase					:str        =		np.abs(image_slice.flatten() - expected_slice).max()
        self.assertLessEqual(__lowercase			,			1E-3)







    def       UpperCamelCase__       (     self)	->     List[Any]:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        super().test_save_load_local(expected_max_difference=3E-3)







    def       UpperCamelCase__       (     self)	->     Tuple:
        super().test_save_load_optional_components(expected_max_difference=3E-3)







    def       UpperCamelCase__       (     self)	->     int:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3)


@slow
@require_torch_gpu
class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:List[str]        =		'''google/ddpm-cifar10-32'''

        __UpperCamelCase					:Optional[Any]        =		UNetaDModel.from_pretrained(__lowercase)
        __UpperCamelCase					:Dict        =		DDIMScheduler()

        __UpperCamelCase					:Optional[Any]        =		DDIMPipeline(unet=__lowercase			,			scheduler=__lowercase)
        ddim.to(__lowercase)
        ddim.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase					:List[str]        =		torch.manual_seed(0)
        __UpperCamelCase					:Optional[int]        =		ddim(generator=__lowercase			,			eta=0.0			,			output_type='''numpy''').images

        __UpperCamelCase					:Optional[Any]        =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase					:Union[str, Any]        =		np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Optional[int]        =		'''google/ddpm-ema-bedroom-256'''

        __UpperCamelCase					:str        =		UNetaDModel.from_pretrained(__lowercase)
        __UpperCamelCase					:int        =		DDIMScheduler.from_pretrained(__lowercase)

        __UpperCamelCase					:Any        =		DDIMPipeline(unet=__lowercase			,			scheduler=__lowercase)
        ddpm.to(__lowercase)
        ddpm.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase					:Dict        =		torch.manual_seed(0)
        __UpperCamelCase					:Any        =		ddpm(generator=__lowercase			,			output_type='''numpy''').images

        __UpperCamelCase					:List[str]        =		image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        __UpperCamelCase					:Dict        =		np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2






from __future__ import annotations

from math import pi



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if (inductance, frequency, reactance).count(0     ) != 1:
        raise ValueError('''One and only one argument must be 0'''     )
    if inductance < 0:
        raise ValueError('''Inductance cannot be negative'''     )
    if frequency < 0:
        raise ValueError('''Frequency cannot be negative'''     )
    if reactance < 0:
        raise ValueError('''Inductive reactance cannot be negative'''     )
    if inductance == 0:
        return {"inductance": reactance / (2 * pi * frequency)}
    elif frequency == 0:
        return {"frequency": reactance / (2 * pi * inductance)}
    elif reactance == 0:
        return {"reactance": 2 * pi * frequency * inductance}
    else:
        raise ValueError('''Exactly one argument must be 0'''     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





