

import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 	__magic_name__    (			A__  ,    A__  ,    A__  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						AltDiffusionPipeline
			SCREAMING_SNAKE_CASE							   =						TEXT_TO_IMAGE_PARAMS
			SCREAMING_SNAKE_CASE							   =						TEXT_TO_IMAGE_BATCH_PARAMS
			SCREAMING_SNAKE_CASE							   =						TEXT_TO_IMAGE_IMAGE_PARAMS
			SCREAMING_SNAKE_CASE							   =						TEXT_TO_IMAGE_IMAGE_PARAMS





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						torch.manual_seed(0					)
						__a        =UNetaDConditionModel(
						    block_out_channels=(32, 64)					,  layers_per_block=2					,  sample_size=32					,  in_channels=4					,  out_channels=4					,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,  cross_attention_dim=32					,  )
						__a        =DDIMScheduler(
						    beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='scaled_linear'					,  clip_sample=__snake_case					,  set_alpha_to_one=__snake_case					,  )
						torch.manual_seed(0					)
						__a        =AutoencoderKL(
						    block_out_channels=[32, 64]					,  in_channels=3					,  out_channels=3					,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,  latent_channels=4					,  )

						# TODO: address the non-deterministic text encoder (fails for save-load tests)
						# torch.manual_seed(0)
						# text_encoder_config = RobertaSeriesConfig(
						#     hidden_size=32,
						#     project_dim=32,
						#     intermediate_size=37,
						#     layer_norm_eps=1e-05,
						#     num_attention_heads=4,
						#     num_hidden_layers=5,
						#     vocab_size=5002,
						# )
						# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

						torch.manual_seed(0					)
						__a        =CLIPTextConfig(
						    bos_token_id=0					,  eos_token_id=2					,  hidden_size=32					,  projection_dim=32					,  intermediate_size=37					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=5002					,  )
						__a        =CLIPTextModel(__snake_case					)

						__a        =XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'					)
						__a        =77

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





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case=0					)   -> Union[str, Any]:




						'''simple docstring'''
						if str(__snake_case					).startswith('mps'					):
									__a        =torch.manual_seed(__snake_case					)
						else:
									__a        =torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
						__a        ={
						    '''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 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						super().test_attention_slicing_forward_pass(expected_max_diff=3e-3					)





			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

						__a        =self.get_dummy_components()
						torch.manual_seed(0					)
						__a        =RobertaSeriesConfig(
						    hidden_size=32					,  project_dim=32					,  intermediate_size=37					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  vocab_size=5002					,  )
						# TODO: remove after fixing the non-deterministic text encoder
						__a        =RobertaSeriesModelWithTransformation(__snake_case					)
						__a        =text_encoder

						__a        =AltDiffusionPipeline(**__snake_case					)
						__a        =alt_pipe.to(__snake_case					)
						alt_pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =self.get_dummy_inputs(__snake_case					)
						__a        ='''A photo of an astronaut'''
						__a        =alt_pipe(**__snake_case					)
						__a        =output.images
						__a        =image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__a        =np.array(
						    [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]					)

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





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

						__a        =self.get_dummy_components()
						__a        =PNDMScheduler(skip_prk_steps=__snake_case					)
						torch.manual_seed(0					)
						__a        =RobertaSeriesConfig(
						    hidden_size=32					,  project_dim=32					,  intermediate_size=37					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  vocab_size=5002					,  )
						# TODO: remove after fixing the non-deterministic text encoder
						__a        =RobertaSeriesModelWithTransformation(__snake_case					)
						__a        =text_encoder
						__a        =AltDiffusionPipeline(**__snake_case					)
						__a        =alt_pipe.to(__snake_case					)
						alt_pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =self.get_dummy_inputs(__snake_case					)
						__a        =alt_pipe(**__snake_case					)
						__a        =output.images
						__a        =image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__a        =np.array(
						    [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]					)

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






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





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						# make sure here that pndm scheduler skips prk
						__a        =AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'					,  safety_checker=__snake_case					)
						__a        =alt_pipe.to(__snake_case					)
						alt_pipe.set_progress_bar_config(disable=__snake_case					)

						__a        ='''A painting of a squirrel eating a burger'''
						__a        =torch.manual_seed(0					)
						__a        =alt_pipe([prompt]					,  generator=__snake_case					,  guidance_scale=6.0					,  num_inference_steps=20					,  output_type='np'					)

						__a        =output.images

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

						assert image.shape == (1, 512, 512, 3)
						__a        =np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]					)

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





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						__a        =DDIMScheduler.from_pretrained('BAAI/AltDiffusion'					,  subfolder='scheduler'					)

						__a        =AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'					,  scheduler=__snake_case					,  safety_checker=__snake_case					)
						__a        =alt_pipe.to(__snake_case					)
						alt_pipe.set_progress_bar_config(disable=__snake_case					)

						__a        ='''A painting of a squirrel eating a burger'''
						__a        =torch.manual_seed(0					)

						__a        =alt_pipe([prompt]					,  generator=__snake_case					,  num_inference_steps=2					,  output_type='numpy'					)
						__a        =output.images

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

						assert image.shape == (1, 512, 512, 3)
						__a        =np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]					)

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











'''simple docstring'''






from math import isclose, sqrt


def  snake_case_     (					_lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float   ,      _lowerCAmelCase  :						float  )      ->   tuple[float, float, float]:
							UpperCAmelCase :       Optional[int] 							=		point_y / 4 / point_x
							UpperCAmelCase :       str 							=		2 * normal_gradient / (1 + normal_gradient * normal_gradient)
							UpperCAmelCase :       Any 							=		(1 - normal_gradient * normal_gradient) / (
							    1 + normal_gradient * normal_gradient
							)
							UpperCAmelCase :       Union[str, Any] 							=		(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

							# to find the next point, solve the simultaeneous equations:
							# y^2 + 4x^2 = 100
							# y - b = m * (x - a)
							# ==> A x^2 + B x + C = 0
							UpperCAmelCase :       Union[str, Any] 							=		outgoing_gradient**2 + 4
							UpperCAmelCase :       Dict 							=		2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
							UpperCAmelCase :       List[Any] 							=		(point_y - outgoing_gradient * point_x) ** 2 - 100

							UpperCAmelCase :       List[str] 							=		(
							    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)
							UpperCAmelCase :       Optional[int] 							=		(
							    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term  )
							) / (2 * quadratic_term)

							# two solutions, one of which is our input point
							UpperCAmelCase :       Optional[Any] 							=		x_minus if isclose(_lowerCAmelCase   ,      _lowerCAmelCase  ) else x_plus
							UpperCAmelCase :       Union[str, Any] 							=		point_y + outgoing_gradient * (next_x - point_x)

							return next_x, next_y, outgoing_gradient


def  snake_case_     (					_lowerCAmelCase  :						float = 1.4   ,      _lowerCAmelCase  :						float = -9.6  )      ->   int:
							UpperCAmelCase :       int 							=		0
							UpperCAmelCase :       float 							=		first_x_coord
							UpperCAmelCase :       float 							=		first_y_coord
							UpperCAmelCase :       float 							=		(1_0.1 - point_y) / (0.0 - point_x)

							while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       str 							=		next_point(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														num_reflections += 1

							return num_reflections


if __name__ == "__main__":
						print(F"{solution() = }")





