






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class    _lowerCAmelCase			(		lowercase_     ,      lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   List[str]      =  StableDiffusionSAGPipeline
				_lowercase				:   Dict      =  TEXT_TO_IMAGE_PARAMS
				_lowercase				:   str      =  TEXT_TO_IMAGE_BATCH_PARAMS
				_lowercase				:   Optional[int]      =  TEXT_TO_IMAGE_IMAGE_PARAMS
				_lowercase				:   Tuple      =  TEXT_TO_IMAGE_IMAGE_PARAMS
				_lowercase				:   Tuple      =  False



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											torch.manual_seed(0)
											snake_case__	  =       UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)			,		layers_per_block=2			,		sample_size=3_2			,		in_channels=4			,		out_channels=4			,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,		cross_attention_dim=3_2			,		)
											snake_case__	  =       DDIMScheduler(
											    beta_start=0.0_00_85			,		beta_end=0.0_12			,		beta_schedule="""scaled_linear"""			,		clip_sample=UpperCamelCase__			,		set_alpha_to_one=UpperCamelCase__			,		)
											torch.manual_seed(0)
											snake_case__	  =       AutoencoderKL(
											    block_out_channels=[3_2, 6_4]			,		in_channels=3			,		out_channels=3			,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,		latent_channels=4			,		)
											torch.manual_seed(0)
											snake_case__	  =       CLIPTextConfig(
											    bos_token_id=0			,		eos_token_id=2			,		hidden_size=3_2			,		intermediate_size=3_7			,		layer_norm_eps=1E-05			,		num_attention_heads=4			,		num_hidden_layers=5			,		pad_token_id=1			,		vocab_size=1_0_0_0			,		)
											snake_case__	  =       CLIPTextModel(UpperCamelCase__)
											snake_case__	  =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

											snake_case__	  =       {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """vae""": vae,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """safety_checker""": None,
											    """feature_extractor""": None,
											}
											return components



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Union[str, Any]=0):






											'''simple docstring'''







											if str(UpperCamelCase__).startswith("""mps"""):
																		snake_case__	  =       torch.manual_seed(UpperCamelCase__)
											else:
																		snake_case__	  =       torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)
											snake_case__	  =       {
											    """prompt""": """.""",
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 1.0,
											    """sag_scale""": 1.0,
											    """output_type""": """numpy""",
											}
											return inputs



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											super().test_inference_batch_single_identical(expected_max_diff=3E-3)


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



				"""simple docstring"""



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""")
											snake_case__	  =       sag_pipe.to(UpperCamelCase__)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase__)

											snake_case__	  =       """."""
											snake_case__	  =       torch.manual_seed(0)
											snake_case__	  =       sag_pipe(
											    [prompt]			,		generator=UpperCamelCase__			,		guidance_scale=7.5			,		sag_scale=1.0			,		num_inference_steps=2_0			,		output_type="""np""")

											snake_case__	  =       output.images

											snake_case__	  =       image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											snake_case__	  =       np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-2



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
											snake_case__	  =       sag_pipe.to(UpperCamelCase__)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase__)

											snake_case__	  =       """."""
											snake_case__	  =       torch.manual_seed(0)
											snake_case__	  =       sag_pipe(
											    [prompt]			,		generator=UpperCamelCase__			,		guidance_scale=7.5			,		sag_scale=1.0			,		num_inference_steps=2_0			,		output_type="""np""")

											snake_case__	  =       output.images

											snake_case__	  =       image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											snake_case__	  =       np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-2



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
											snake_case__	  =       sag_pipe.to(UpperCamelCase__)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase__)

											snake_case__	  =       """."""
											snake_case__	  =       torch.manual_seed(0)
											snake_case__	  =       sag_pipe(
											    [prompt]			,		width=7_6_8			,		height=5_1_2			,		generator=UpperCamelCase__			,		guidance_scale=7.5			,		sag_scale=1.0			,		num_inference_steps=2_0			,		output_type="""np"""			,		)

											snake_case__	  =       output.images

											assert image.shape == (1, 5_1_2, 7_6_8, 3)








from collections.abc import Callable

def _UpperCAmelCase   (	a						:	Callable[[float], float]  ,					a						:	float  ,					a						:	float ):
							snake_case__	  =       a
							snake_case__	  =       b
							if function(a ) == 0:  # one of the a or b is a root for the function
														return a
							elif function(a ) == 0:
														return b
							elif (
							    function(a ) * function(a ) > 0
							):  # if none of these are root and they are both positive or negative,
														# then this algorithm can't find the root
														raise ValueError("""could not find root in given interval.""" )
							else:
														snake_case__	  =       start + (end - start) / 2.0
														while abs(start - mid ) > 10**-7:  # until precisely equals to 10^-7
																					if function(a ) == 0:
																												return mid
																					elif function(a ) * function(a ) < 0:
																												snake_case__	  =       mid
																					else:
																												snake_case__	  =       mid
																					snake_case__	  =       start + (end - start) / 2.0
														return mid

def _UpperCAmelCase   (	a						:	float ):
							return x**3 - 2 * x - 5


if __name__ == "__main__":
	print(bisection(f, 1, 1_0_0_0))

	import doctest

	doctest.testmod()
