



"""simple docstring"""
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()


class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Tuple										= StableDiffusionLDMaDPipeline
					lowercase__   :       str										= TEXT_TO_IMAGE_PARAMS
					lowercase__   :       Optional[int]										= TEXT_TO_IMAGE_BATCH_PARAMS
					lowercase__   :       Union[str, Any]										= TEXT_TO_IMAGE_IMAGE_PARAMS

					def 			snake_case__      (    self   ):
									torch.manual_seed(0   )
									_lowerCamelCase        =				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				,       )
									_lowerCamelCase        =				DDIMScheduler(
									    beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       beta_schedule='''scaled_linear'''				,       clip_sample=lowerCamelCase__				,       set_alpha_to_one=lowerCamelCase__				,       )
									torch.manual_seed(0   )
									_lowerCamelCase        =				AutoencoderKL(
									    block_out_channels=[3_2, 6_4]				,       in_channels=6				,       out_channels=6				,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,       latent_channels=4				,       )
									torch.manual_seed(0   )
									_lowerCamelCase        =				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				,       )
									_lowerCamelCase        =				CLIPTextModel(lowerCamelCase__   )
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

									_lowerCamelCase        =				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=0   ):
									if str(lowerCamelCase__   ).startswith('''mps'''   ):
													_lowerCamelCase        =				torch.manual_seed(lowerCamelCase__   )
									else:
													_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
									_lowerCamelCase        =				{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				StableDiffusionLDMaDPipeline(**lowerCamelCase__   )
									_lowerCamelCase        =				ldmad_pipe.to(lowerCamelCase__   )
									ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				ldmad_pipe(**lowerCamelCase__   )
									_lowerCamelCase  ,     _lowerCamelCase        =				output.rgb, output.depth

									_lowerCamelCase        =				rgb[0, -3:, -3:, -1]
									_lowerCamelCase        =				depth[0, -3:, -1]

									assert rgb.shape == (1, 6_4, 6_4, 3)
									assert depth.shape == (1, 6_4, 6_4)

									_lowerCamelCase        =				np.array(
									    [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2]   )
									_lowerCamelCase        =				np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6]   )

									assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb   ).max() < 1e-2
									assert np.abs(image_slice_depth.flatten() - expected_slice_depth   ).max() < 1e-2

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				StableDiffusionLDMaDPipeline(**lowerCamelCase__   )
									_lowerCamelCase        =				ldmad_pipe.to(lowerCamelCase__   )
									ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				3 * [inputs['''prompt''']]

									# forward
									_lowerCamelCase        =				ldmad_pipe(**lowerCamelCase__   )
									_lowerCamelCase  ,     _lowerCamelCase        =				output.rgb, output.depth
									_lowerCamelCase        =				rgb_slice_a[0, -3:, -3:, -1]
									_lowerCamelCase        =				depth_slice_a[0, -3:, -1]

									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				3 * [inputs.pop('''prompt'''   )]

									_lowerCamelCase        =				ldmad_pipe.tokenizer(
									    lowerCamelCase__				,       padding='''max_length'''				,       max_length=ldmad_pipe.tokenizer.model_max_length				,       truncation=lowerCamelCase__				,       return_tensors='''pt'''				,       )
									_lowerCamelCase        =				text_inputs['''input_ids'''].to(lowerCamelCase__   )

									_lowerCamelCase        =				ldmad_pipe.text_encoder(lowerCamelCase__   )[0]

									_lowerCamelCase        =				prompt_embeds

									# forward
									_lowerCamelCase        =				ldmad_pipe(**lowerCamelCase__   )
									_lowerCamelCase  ,     _lowerCamelCase        =				output.rgb, output.depth
									_lowerCamelCase        =				rgb_slice_a[0, -3:, -3:, -1]
									_lowerCamelCase        =				depth_slice_a[0, -3:, -1]

									assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()   ).max() < 1e-4
									assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()   ).max() < 1e-4





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				PNDMScheduler(skip_prk_steps=lowerCamelCase__   )
									_lowerCamelCase        =				StableDiffusionLDMaDPipeline(**lowerCamelCase__   )
									_lowerCamelCase        =				ldmad_pipe.to(lowerCamelCase__   )
									ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				'''french fries'''
									_lowerCamelCase        =				ldmad_pipe(**lowerCamelCase__				,       negative_prompt=lowerCamelCase__   )

									_lowerCamelCase  ,     _lowerCamelCase        =				output.rgb, output.depth
									_lowerCamelCase        =				rgb[0, -3:, -3:, -1]
									_lowerCamelCase        =				depth[0, -3:, -1]

									assert rgb.shape == (1, 6_4, 6_4, 3)
									assert depth.shape == (1, 6_4, 6_4)

									_lowerCamelCase        =				np.array(
									    [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7]   )
									_lowerCamelCase        =				np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5]   )
									assert np.abs(rgb_slice.flatten() - expected_slice_rgb   ).max() < 1e-2
									assert np.abs(depth_slice.flatten() - expected_slice_depth   ).max() < 1e-2


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





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__="cpu"				,       lowerCamelCase__=torch.floataa				,       lowerCamelCase__=0   ):
									_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
									_lowerCamelCase        =				np.random.RandomState(lowerCamelCase__   ).standard_normal((1, 4, 6_4, 6_4)   )
									_lowerCamelCase        =				torch.from_numpy(lowerCamelCase__   ).to(device=lowerCamelCase__				,       dtype=lowerCamelCase__   )
									_lowerCamelCase        =				{
									    '''prompt''': '''a photograph of an astronaut riding a horse''',
									    '''latents''': latents,
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''   )
									_lowerCamelCase        =				ldmad_pipe.to(lowerCamelCase__   )
									ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				ldmad_pipe(**lowerCamelCase__   )
									_lowerCamelCase  ,     _lowerCamelCase        =				output.rgb, output.depth
									_lowerCamelCase        =				rgb[0, -3:, -3:, -1].flatten()
									_lowerCamelCase        =				rgb[0, -3:, -1].flatten()

									assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
									assert depth.shape == (1, 5_1_2, 5_1_2)

									_lowerCamelCase        =				np.array(
									    [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6]   )
									_lowerCamelCase        =				np.array(
									    [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6]   )
									assert np.abs(rgb_slice - expected_slice_rgb   ).max() < 3e-3
									assert np.abs(depth_slice - expected_slice_depth   ).max() < 3e-3


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





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__="cpu"				,       lowerCamelCase__=torch.floataa				,       lowerCamelCase__=0   ):
									_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
									_lowerCamelCase        =				np.random.RandomState(lowerCamelCase__   ).standard_normal((1, 4, 6_4, 6_4)   )
									_lowerCamelCase        =				torch.from_numpy(lowerCamelCase__   ).to(device=lowerCamelCase__				,       dtype=lowerCamelCase__   )
									_lowerCamelCase        =				{
									    '''prompt''': '''a photograph of an astronaut riding a horse''',
									    '''latents''': latents,
									    '''generator''': generator,
									    '''num_inference_steps''': 5_0,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''   ).to(lowerCamelCase__   )
									ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				ldmad_pipe(**lowerCamelCase__   )
									_lowerCamelCase  ,     _lowerCamelCase        =				output.rgb, output.depth

									_lowerCamelCase        =				0.4_9_5_5_8_6
									_lowerCamelCase        =				0.3_3_7_9_5_5_1_5
									_lowerCamelCase        =				1_1_2.4_8_5_1_8
									_lowerCamelCase        =				9_8.4_8_9_7_4_6
									assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1e-3
									assert np.abs(expected_rgb_std - rgb.std()   ) < 1e-3
									assert np.abs(expected_depth_mean - depth.mean()   ) < 1e-3
									assert np.abs(expected_depth_std - depth.std()   ) < 1e-3





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''   ).to(lowerCamelCase__   )
									ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				ldmad_pipe(**lowerCamelCase__   )
									_lowerCamelCase  ,     _lowerCamelCase        =				output.rgb, output.depth

									_lowerCamelCase        =				0.4_1_9_4_1_2_7
									_lowerCamelCase        =				0.3_5_3_7_5_5_8_6
									_lowerCamelCase        =				0.5_6_3_8_5_0_2
									_lowerCamelCase        =				0.3_4_6_8_6_1_0_3

									assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
									assert depth.shape == (1, 5_1_2, 5_1_2, 1)
									assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1e-3
									assert np.abs(expected_rgb_std - rgb.std()   ) < 1e-3
									assert np.abs(expected_depth_mean - depth.mean()   ) < 1e-3
									assert np.abs(expected_depth_std - depth.std()   ) < 1e-3





"""simple docstring"""
from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE  :						List[str]    							=      numpy.array([0, 0])
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      numpy.array([0.5, 0.866_0254])
__SCREAMING_SNAKE_CASE  :						Tuple    							=      numpy.array([1, 0])
__SCREAMING_SNAKE_CASE  :						List[Any]    							=      [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]						,   lowercase_     :							int							)	->       list[numpy.ndarray]:
				_lowerCamelCase        =				initial_vectors
				for _ in range(lowercase_							):
								_lowerCamelCase        =				iteration_step(lowercase_							)
				return vectors



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]							)	->       list[numpy.ndarray]:
				_lowerCamelCase        =				[]
				for i, start_vector in enumerate(vectors[:-1]							):
								_lowerCamelCase        =				vectors[i + 1]
								new_vectors.append(lowercase_							)
								_lowerCamelCase        =				end_vector - start_vector
								new_vectors.append(start_vector + difference_vector / 3							)
								new_vectors.append(
								    start_vector + difference_vector / 3 + rotate(difference_vector / 3						,   60							)							)
								new_vectors.append(start_vector + difference_vector * 2 / 3							)
				new_vectors.append(vectors[-1]							)
				return new_vectors



def        lowerCAmelCase_(   lowercase_     :							numpy.ndarray						,   lowercase_     :							float							)	->       numpy.ndarray:
				_lowerCamelCase        =				numpy.radians(lowercase_							)
				_lowerCamelCase  ,     _lowerCamelCase        =				numpy.cos(lowercase_							), numpy.sin(lowercase_							)
				_lowerCamelCase        =				numpy.array(((c, -s), (s, c))							)
				return numpy.dot(lowercase_						,   lowercase_							)



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]							)	->       None:
				_lowerCamelCase        =				plt.gca()
				axes.set_aspect('''equal'''							)

				# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
				# y-coordinates as inputs, which are constructed from the vector-list using
				# zip()
				_lowerCamelCase  ,     _lowerCamelCase        =				zip(*lowercase_							)
				plt.plot(lowercase_						,   lowercase_							)
				plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__SCREAMING_SNAKE_CASE  :						str    							=      iterate(INITIAL_VECTORS, 5)
			plot(processed_vectors)
