


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 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    StableDiffusionLDMaDPipeline
     __SCREAMING_SNAKE_CASE     					=    TEXT_TO_IMAGE_PARAMS
     __SCREAMING_SNAKE_CASE     					=    TEXT_TO_IMAGE_BATCH_PARAMS
     __SCREAMING_SNAKE_CASE     					=    TEXT_TO_IMAGE_IMAGE_PARAMS


     def        __snake_case(  self    ):
       torch.manual_seed(0    )
       _UpperCAmelCase		:							List[str]							=     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							,					)
       _UpperCAmelCase		:							List[Any]							=     DDIMScheduler(
           beta_start=0.0_0_0_8_5							,					beta_end=0.0_1_2							,					beta_schedule="""scaled_linear"""							,					clip_sample=_SCREAMING_SNAKE_CASE							,					set_alpha_to_one=_SCREAMING_SNAKE_CASE							,					)
       torch.manual_seed(0    )
       _UpperCAmelCase		:							str							=     AutoencoderKL(
           block_out_channels=[32, 64]							,					in_channels=6							,					out_channels=6							,					down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]							,					up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]							,					latent_channels=4							,					)
       torch.manual_seed(0    )
       _UpperCAmelCase		:							Any							=     CLIPTextConfig(
           bos_token_id=0							,					eos_token_id=2							,					hidden_size=32							,					intermediate_size=37							,					layer_norm_eps=1e-05							,					num_attention_heads=4							,					num_hidden_layers=5							,					pad_token_id=1							,					vocab_size=10_00							,					)
       _UpperCAmelCase		:							Optional[int]							=     CLIPTextModel(_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							Optional[int]							=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

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


     def        __snake_case(  self							,					A_							,					A_=0    ):




       if str(_SCREAMING_SNAKE_CASE    ).startswith("""mps"""    ):
         _UpperCAmelCase		:							Optional[int]							=     torch.manual_seed(_SCREAMING_SNAKE_CASE    )
       else:
         _UpperCAmelCase		:							Union[str, Any]							=     torch.Generator(device=_SCREAMING_SNAKE_CASE    ).manual_seed(_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							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        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     """cpu"""  # ensure determinism for the device-dependent torch.Generator

       _UpperCAmelCase		:							Any							=     self.get_dummy_components()
       _UpperCAmelCase		:							Optional[int]							=     StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							Any							=     ldmad_pipe.to(_SCREAMING_SNAKE_CASE    )
       ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

       _UpperCAmelCase		:							Optional[int]							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							List[Any]							=     ldmad_pipe(**_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase,_UpperCAmelCase		:							Tuple							=     output.rgb, output.depth

       _UpperCAmelCase		:							Dict							=     rgb[0, -3:, -3:, -1]
       _UpperCAmelCase		:							Union[str, Any]							=     depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       _UpperCAmelCase		:							Any							=     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]    )
       _UpperCAmelCase		:							List[Any]							=     np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36]    )

       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    ):
       _UpperCAmelCase		:							Optional[Any]							=     self.get_dummy_components()
       _UpperCAmelCase		:							Tuple							=     StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							Tuple							=     ldmad_pipe.to(_SCREAMING_SNAKE_CASE    )
       ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

       _UpperCAmelCase		:							Tuple							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							Optional[Any]							=     3 * [inputs["""prompt"""]]

       # forward
       _UpperCAmelCase		:							str							=     ldmad_pipe(**_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase,_UpperCAmelCase		:							List[Any]							=     output.rgb, output.depth
       _UpperCAmelCase		:							Any							=     rgb_slice_a[0, -3:, -3:, -1]
       _UpperCAmelCase		:							Tuple							=     depth_slice_a[0, -3:, -1]

       _UpperCAmelCase		:							Optional[Any]							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							Any							=     3 * [inputs.pop("""prompt"""    )]

       _UpperCAmelCase		:							Optional[int]							=     ldmad_pipe.tokenizer(
           _SCREAMING_SNAKE_CASE							,					padding="""max_length"""							,					max_length=ldmad_pipe.tokenizer.model_max_length							,					truncation=_SCREAMING_SNAKE_CASE							,					return_tensors="""pt"""							,					)
       _UpperCAmelCase		:							List[Any]							=     text_inputs["""input_ids"""].to(_SCREAMING_SNAKE_CASE    )

       _UpperCAmelCase		:							Optional[int]							=     ldmad_pipe.text_encoder(_SCREAMING_SNAKE_CASE    )[0]

       _UpperCAmelCase		:							List[Any]							=     prompt_embeds

       # forward
       _UpperCAmelCase		:							Optional[int]							=     ldmad_pipe(**_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     output.rgb, output.depth
       _UpperCAmelCase		:							List[Any]							=     rgb_slice_a[0, -3:, -3:, -1]
       _UpperCAmelCase		:							str							=     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    ):
       _UpperCAmelCase		:							List[Any]							=     """cpu"""  # ensure determinism for the device-dependent torch.Generator
       _UpperCAmelCase		:							Any							=     self.get_dummy_components()
       _UpperCAmelCase		:							List[Any]							=     PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							Optional[int]							=     StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							Optional[int]							=     ldmad_pipe.to(_SCREAMING_SNAKE_CASE    )
       ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

       _UpperCAmelCase		:							str							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							Union[str, Any]							=     """french fries"""
       _UpperCAmelCase		:							Dict							=     ldmad_pipe(**_SCREAMING_SNAKE_CASE							,					negative_prompt=_SCREAMING_SNAKE_CASE    )

       _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     output.rgb, output.depth
       _UpperCAmelCase		:							Any							=     rgb[0, -3:, -3:, -1]
       _UpperCAmelCase		:							Dict							=     depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       _UpperCAmelCase		:							Any							=     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]    )
       _UpperCAmelCase		:							Optional[Any]							=     np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35]    )
       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 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):


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


     def        __snake_case(  self							,					A_							,					A_="cpu"							,					A_=torch.floataa							,					A_=0    ):
       _UpperCAmelCase		:							List[Any]							=     torch.Generator(device=_SCREAMING_SNAKE_CASE    ).manual_seed(_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							Optional[Any]							=     np.random.RandomState(_SCREAMING_SNAKE_CASE    ).standard_normal((1, 4, 64, 64)    )
       _UpperCAmelCase		:							Tuple							=     torch.from_numpy(_SCREAMING_SNAKE_CASE    ).to(device=_SCREAMING_SNAKE_CASE							,					dtype=_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							int							=     {
           """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    ):
       _UpperCAmelCase		:							Optional[Any]							=     StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""    )
       _UpperCAmelCase		:							Optional[Any]							=     ldmad_pipe.to(_SCREAMING_SNAKE_CASE    )
       ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

       _UpperCAmelCase		:							Optional[int]							=     self.get_inputs(_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							Dict							=     ldmad_pipe(**_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase,_UpperCAmelCase		:							List[Any]							=     output.rgb, output.depth
       _UpperCAmelCase		:							Optional[int]							=     rgb[0, -3:, -3:, -1].flatten()
       _UpperCAmelCase		:							str							=     rgb[0, -3:, -1].flatten()

       assert rgb.shape == (1, 5_12, 5_12, 3)
       assert depth.shape == (1, 5_12, 5_12)

       _UpperCAmelCase		:							Union[str, Any]							=     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]    )
       _UpperCAmelCase		:							str							=     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 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):


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


     def        __snake_case(  self							,					A_							,					A_="cpu"							,					A_=torch.floataa							,					A_=0    ):
       _UpperCAmelCase		:							Union[str, Any]							=     torch.Generator(device=_SCREAMING_SNAKE_CASE    ).manual_seed(_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							List[Any]							=     np.random.RandomState(_SCREAMING_SNAKE_CASE    ).standard_normal((1, 4, 64, 64)    )
       _UpperCAmelCase		:							str							=     torch.from_numpy(_SCREAMING_SNAKE_CASE    ).to(device=_SCREAMING_SNAKE_CASE							,					dtype=_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							List[str]							=     {
           """prompt""": """a photograph of an astronaut riding a horse""",
           """latents""": latents,
           """generator""": generator,
           """num_inference_steps""": 50,
           """guidance_scale""": 7.5,
           """output_type""": """numpy""",
       }
       return inputs


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							int							=     StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""    ).to(_SCREAMING_SNAKE_CASE    )
       ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

       _UpperCAmelCase		:							Tuple							=     self.get_inputs(_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							str							=     ldmad_pipe(**_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase,_UpperCAmelCase		:							Optional[int]							=     output.rgb, output.depth

       _UpperCAmelCase		:							List[Any]							=     0.4_9_5_5_8_6
       _UpperCAmelCase		:							List[str]							=     0.3_3_7_9_5_5_1_5
       _UpperCAmelCase		:							List[Any]							=     1_12.4_85_18
       _UpperCAmelCase		:							List[Any]							=     98.48_97_46
       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    ):
       _UpperCAmelCase		:							Optional[int]							=     StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d-4c"""    ).to(_SCREAMING_SNAKE_CASE    )
       ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

       _UpperCAmelCase		:							Optional[int]							=     self.get_inputs(_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase		:							Optional[Any]							=     ldmad_pipe(**_SCREAMING_SNAKE_CASE    )
       _UpperCAmelCase,_UpperCAmelCase		:							Dict							=     output.rgb, output.depth

       _UpperCAmelCase		:							int							=     0.4_1_9_4_1_2_7
       _UpperCAmelCase		:							Any							=     0.3_5_3_7_5_5_8_6
       _UpperCAmelCase		:							List[str]							=     0.5_6_3_8_5_0_2
       _UpperCAmelCase		:							Optional[Any]							=     0.3_4_6_8_6_1_0_3

       assert rgb.shape == (1, 5_12, 5_12, 3)
       assert depth.shape == (1, 5_12, 5_12, 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













def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			bool:
      """simple docstring"""

      return numa ^ numa < 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()



