





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):
      _lowercase    : List[Any]            =      StableDiffusionLDMaDPipeline
      _lowercase    : Dict            =      TEXT_TO_IMAGE_PARAMS
      _lowercase    : Optional[Any]            =      TEXT_TO_IMAGE_BATCH_PARAMS
      _lowercase    : List[Any]            =      TEXT_TO_IMAGE_IMAGE_PARAMS







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__     : Union[str, Any]  =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__     : Optional[int]  =DDIMScheduler(
            beta_start=0.0_00_85		,				beta_end=0.0_12		,				beta_schedule="scaled_linear"		,				clip_sample=__A		,				set_alpha_to_one=__A		,				)
        torch.manual_seed(0							)
        a__     : Union[str, Any]  =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							)
        a__     : Union[str, Any]  =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		,				)
        a__     : Tuple  =CLIPTextModel(__A							)
        a__     : Tuple  =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

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







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__=0							)       ->						int:



        '''simple docstring'''




        if str(__A							).startswith("mps"							):
          a__     : List[str]  =torch.manual_seed(__A							)
        else:
          a__     : str  =torch.Generator(device=__A							).manual_seed(__A							)
        a__     : Dict  ={
            """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							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[str]  ="""cpu"""  # ensure determinism for the device-dependent torch.Generator

        a__     : int  =self.get_dummy_components()
        a__     : Tuple  =StableDiffusionLDMaDPipeline(**__A							)
        a__     : Tuple  =ldmad_pipe.to(__A							)
        ldmad_pipe.set_progress_bar_config(disable=__A							)

        a__     : Union[str, Any]  =self.get_dummy_inputs(__A							)
        a__     : List[str]  =ldmad_pipe(**__A							)
        a__     : Dict  =output.rgb, output.depth

        a__     : List[str]  =rgb[0, -3:, -3:, -1]
        a__     : int  =depth[0, -3:, -1]

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

        a__     : Any  =np.array(
            [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62]							)
        a__     : Union[str, Any]  =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        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Any  =self.get_dummy_components()
        a__     : List[Any]  =StableDiffusionLDMaDPipeline(**__A							)
        a__     : int  =ldmad_pipe.to(__A							)
        ldmad_pipe.set_progress_bar_config(disable=__A							)

        a__     : Union[str, Any]  =self.get_dummy_inputs(__A							)
        a__     : str  =3 * [inputs["""prompt"""]]

        # forward
        a__     : Any  =ldmad_pipe(**__A							)
        a__     : Optional[int]  =output.rgb, output.depth
        a__     : int  =rgb_slice_a[0, -3:, -3:, -1]
        a__     : Optional[Any]  =depth_slice_a[0, -3:, -1]

        a__     : Dict  =self.get_dummy_inputs(__A							)
        a__     : Optional[Any]  =3 * [inputs.pop("prompt"							)]

        a__     : Dict  =ldmad_pipe.tokenizer(
            __A		,				padding="max_length"		,				max_length=ldmad_pipe.tokenizer.model_max_length		,				truncation=__A		,				return_tensors="pt"		,				)
        a__     : Optional[int]  =text_inputs["""input_ids"""].to(__A							)

        a__     : List[Any]  =ldmad_pipe.text_encoder(__A							)[0]

        a__     : Any  =prompt_embeds

        # forward
        a__     : Optional[int]  =ldmad_pipe(**__A							)
        a__     : Dict  =output.rgb, output.depth
        a__     : Tuple  =rgb_slice_a[0, -3:, -3:, -1]
        a__     : List[Any]  =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        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Union[str, Any]  ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
        a__     : Tuple  =self.get_dummy_components()
        a__     : List[Any]  =PNDMScheduler(skip_prk_steps=__A							)
        a__     : Optional[Any]  =StableDiffusionLDMaDPipeline(**__A							)
        a__     : str  =ldmad_pipe.to(__A							)
        ldmad_pipe.set_progress_bar_config(disable=__A							)

        a__     : str  =self.get_dummy_inputs(__A							)
        a__     : Optional[Any]  ="""french fries"""
        a__     : int  =ldmad_pipe(**__A		,				negative_prompt=__A							)

        a__     : Dict  =output.rgb, output.depth
        a__     : int  =rgb[0, -3:, -3:, -1]
        a__     : Dict  =depth[0, -3:, -1]

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

        a__     : Tuple  =np.array(
            [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17]							)
        a__     : Union[str, Any]  =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):







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



        '''simple docstring'''




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







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__="cpu"		,				lowerCAmelCase__=torch.floataa		,				lowerCAmelCase__=0							)       ->						Dict:



        '''simple docstring'''




        a__     : List[str]  =torch.Generator(device=__A							).manual_seed(__A							)
        a__     : List[str]  =np.random.RandomState(__A							).standard_normal((1, 4, 6_4, 6_4)							)
        a__     : Any  =torch.from_numpy(__A							).to(device=__A		,				dtype=__A							)
        a__     : Any  ={
            """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        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Union[str, Any]  =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"							)
        a__     : Union[str, Any]  =ldmad_pipe.to(__A							)
        ldmad_pipe.set_progress_bar_config(disable=__A							)

        a__     : Any  =self.get_inputs(__A							)
        a__     : Union[str, Any]  =ldmad_pipe(**__A							)
        a__     : Tuple  =output.rgb, output.depth
        a__     : List[str]  =rgb[0, -3:, -3:, -1].flatten()
        a__     : Any  =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)

        a__     : int  =np.array(
            [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06]							)
        a__     : str  =np.array(
            [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06]							)
        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):







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




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







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__="cpu"		,				lowerCAmelCase__=torch.floataa		,				lowerCAmelCase__=0							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Tuple  =torch.Generator(device=__A							).manual_seed(__A							)
        a__     : int  =np.random.RandomState(__A							).standard_normal((1, 4, 6_4, 6_4)							)
        a__     : List[Any]  =torch.from_numpy(__A							).to(device=__A		,				dtype=__A							)
        a__     : Optional[Any]  ={
            """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        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        a__     : str  =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"							).to(__A							)
        ldmad_pipe.set_progress_bar_config(disable=__A							)

        a__     : Dict  =self.get_inputs(__A							)
        a__     : Any  =ldmad_pipe(**__A							)
        a__     : List[str]  =output.rgb, output.depth

        a__     : int  =0.49_55_86
        a__     : Optional[Any]  =0.33_79_55_15
        a__     : int  =1_1_2.4_8_5_1_8
        a__     : Any  =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        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        a__     : int  =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"							).to(__A							)
        ldmad_pipe.set_progress_bar_config(disable=__A							)

        a__     : Dict  =self.get_inputs(__A							)
        a__     : List[str]  =ldmad_pipe(**__A							)
        a__     : Any  =output.rgb, output.depth

        a__     : str  =0.4_19_41_27
        a__     : List[Any]  =0.35_37_55_86
        a__     : List[Any]  =0.5_63_85_02
        a__     : List[Any]  =0.34_68_61_03

        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







import random
from typing import Any
def 					_A	(		SCREAMING_SNAKE_CASE					:       list						):





  """simple docstring"""



  for _ in range(len(SCREAMING_SNAKE_CASE						)						):
    a__     : Tuple  =random.randint(0	,	len(SCREAMING_SNAKE_CASE						) - 1						)
    a__     : Any  =random.randint(0	,	len(SCREAMING_SNAKE_CASE						) - 1						)
    a__ ,							a__     : Tuple  =data[b], data[a]
  return data


if __name__ == "__main__":
     UpperCAmelCase     :     Dict							=	[0, 1, 2, 3, 4, 5, 6, 7]
     UpperCAmelCase     :     Optional[Any]							=	["""python""", """says""", """hello""", """!"""]
     print("""Fisher-Yates Shuffle:""")
     print("""List""", integers, strings)
     print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))






