
"""simple docstring"""




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     lowercase(_lowercase    ,						_lowercase    ,						_lowercase    ,						unittest.TestCase       ):
     __snake_case:	str						=     AltDiffusionPipeline
     __snake_case:	str						=     TEXT_TO_IMAGE_PARAMS
     __snake_case:	int						=     TEXT_TO_IMAGE_BATCH_PARAMS
     __snake_case:	Optional[int]						=     TEXT_TO_IMAGE_IMAGE_PARAMS
     __snake_case:	List[str]						=     TEXT_TO_IMAGE_IMAGE_PARAMS



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







         """simple docstring"""



         torch.manual_seed(0   )
         a__		=			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     ,				)
         a__		=			DDIMScheduler(
             beta_start=0.0_00_85     ,				beta_end=0.0_12     ,				beta_schedule='scaled_linear'     ,				clip_sample=__SCREAMING_SNAKE_CASE     ,				set_alpha_to_one=__SCREAMING_SNAKE_CASE     ,				)
         torch.manual_seed(0   )
         a__		=			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     ,				)

         # 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=3_2     ,				projection_dim=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=5_0_0_2     ,				)
         a__		=			CLIPTextModel(__SCREAMING_SNAKE_CASE   )

         a__		=			XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'   )
         a__		=			7_7

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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=0   )		->     Optional[Any]:







         """simple docstring"""



         if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
             a__		=			torch.manual_seed(__SCREAMING_SNAKE_CASE   )
         else:
             a__		=			torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_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 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )



     def 			lowercase__  (							self   )		->     int:







         """simple docstring"""



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



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







         """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=3_2     ,				project_dim=3_2     ,				intermediate_size=3_7     ,				layer_norm_eps=1e-05     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				vocab_size=5_0_0_2     ,				)
         # TODO: remove after fixing the non-deterministic text encoder
         a__		=			RobertaSeriesModelWithTransformation(__SCREAMING_SNAKE_CASE   )
         a__		=			text_encoder

         a__		=			AltDiffusionPipeline(**__SCREAMING_SNAKE_CASE   )
         a__		=			alt_pipe.to(__SCREAMING_SNAKE_CASE   )
         alt_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

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

         assert image.shape == (1, 6_4, 6_4, 3)
         a__		=			np.array(
             [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93]   )

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




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







         """simple docstring"""



         a__		=			'cpu'  # ensure determinism for the device-dependent torch.Generator

         a__		=			self.get_dummy_components()
         a__		=			PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE   )
         torch.manual_seed(0   )
         a__		=			RobertaSeriesConfig(
             hidden_size=3_2     ,				project_dim=3_2     ,				intermediate_size=3_7     ,				layer_norm_eps=1e-05     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				vocab_size=5_0_0_2     ,				)
         # TODO: remove after fixing the non-deterministic text encoder
         a__		=			RobertaSeriesModelWithTransformation(__SCREAMING_SNAKE_CASE   )
         a__		=			text_encoder
         a__		=			AltDiffusionPipeline(**__SCREAMING_SNAKE_CASE   )
         a__		=			alt_pipe.to(__SCREAMING_SNAKE_CASE   )
         alt_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

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

         assert image.shape == (1, 6_4, 6_4, 3)
         a__		=			np.array(
             [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37]   )

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







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



     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



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



     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         a__		=			AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'     ,				safety_checker=__SCREAMING_SNAKE_CASE   )
         a__		=			alt_pipe.to(__SCREAMING_SNAKE_CASE   )
         alt_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

         a__		=			'A painting of a squirrel eating a burger'
         a__		=			torch.manual_seed(0   )
         a__		=			alt_pipe([prompt]     ,				generator=__SCREAMING_SNAKE_CASE     ,				guidance_scale=6.0     ,				num_inference_steps=2_0     ,				output_type='np'   )

         a__		=			output.images

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         a__		=			np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86]   )

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




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







         """simple docstring"""



         a__		=			DDIMScheduler.from_pretrained('BAAI/AltDiffusion'     ,				subfolder='scheduler'   )

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

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

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

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         a__		=			np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23]   )

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




"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		)     ->						float:
    a__		=			[redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters     ):
        raise ValueError('All input parameters must be positive'     )

    if any(p > 1 for p in parameters[1:4]     ):
        raise ValueError('Relative densities cannot be greater than one'     )
    else:
        a__		=			1 - (matter_density + radiation_density + dark_energy)

        a__		=			(
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        a__		=			hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # demo LCDM approximation
    a		:       Any     						=       0.3

    print(
        hubble_parameter(
            hubble_constant=68.3,
            radiation_density=1e-4,
            matter_density=matter_density,
            dark_energy=1 - matter_density,
            redshift=0,
        )
    )


