





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       __A     (						unittest.TestCase    ):
				__A      =				StableDiffusionLDMaDPipeline
				__A      =				TEXT_TO_IMAGE_PARAMS
				__A      =				TEXT_TO_IMAGE_BATCH_PARAMS
				__A      =				TEXT_TO_IMAGE_IMAGE_PARAMS


				def _snake_case       (		self							):
								torch.manual_seed(0							)
								lowerCamelCase	 =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   , )
								lowerCamelCase	 =DDIMScheduler(
								    beta_start=0.0_0_0_8_5   , beta_end=0.0_1_2   , beta_schedule="""scaled_linear"""   , clip_sample=snake_case_   , set_alpha_to_one=snake_case_   , )
								torch.manual_seed(0							)
								lowerCamelCase	 =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							)
								lowerCamelCase	 =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=1000   , )
								lowerCamelCase	 =CLIPTextModel(snake_case_							)
								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   , UpperCAmelCase_   , UpperCAmelCase_=0							):
								if str(snake_case_							).startswith("""mps"""							):
												lowerCamelCase	 =torch.manual_seed(snake_case_							)
								else:
												lowerCamelCase	 =torch.Generator(device=snake_case_							).manual_seed(snake_case_							)
								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(**snake_case_							)
								lowerCamelCase	 =ldmad_pipe.to(snake_case_							)
								ldmad_pipe.set_progress_bar_config(disable=snake_case_							)

								lowerCamelCase	 =self.get_dummy_inputs(snake_case_							)
								lowerCamelCase	 =ldmad_pipe(**snake_case_							)
								lowerCamelCase ,  lowerCamelCase	 =output.rgb, output.depth

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

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

								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(**snake_case_							)
								lowerCamelCase	 =ldmad_pipe.to(snake_case_							)
								ldmad_pipe.set_progress_bar_config(disable=snake_case_							)

								lowerCamelCase	 =self.get_dummy_inputs(snake_case_							)
								lowerCamelCase	 =3 * [inputs["""prompt"""]]

								# forward
								lowerCamelCase	 =ldmad_pipe(**snake_case_							)
								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(snake_case_							)
								lowerCamelCase	 =3 * [inputs.pop("""prompt"""							)]

								lowerCamelCase	 =ldmad_pipe.tokenizer(
								    snake_case_   , padding="""max_length"""   , max_length=ldmad_pipe.tokenizer.model_max_length   , truncation=snake_case_   , return_tensors="""pt"""   , )
								lowerCamelCase	 =text_inputs["""input_ids"""].to(snake_case_							)

								lowerCamelCase	 =ldmad_pipe.text_encoder(snake_case_							)[0]

								lowerCamelCase	 =prompt_embeds

								# forward
								lowerCamelCase	 =ldmad_pipe(**snake_case_							)
								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=snake_case_							)
								lowerCamelCase	 =StableDiffusionLDMaDPipeline(**snake_case_							)
								lowerCamelCase	 =ldmad_pipe.to(snake_case_							)
								ldmad_pipe.set_progress_bar_config(disable=snake_case_							)

								lowerCamelCase	 =self.get_dummy_inputs(snake_case_							)
								lowerCamelCase	 ="""french fries"""
								lowerCamelCase	 =ldmad_pipe(**snake_case_   , negative_prompt=snake_case_							)

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

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

								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       __A     (						unittest.TestCase    ):


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


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_="cpu"   , UpperCAmelCase_=torch.floataa   , UpperCAmelCase_=0							):
								lowerCamelCase	 =torch.Generator(device=snake_case_							).manual_seed(snake_case_							)
								lowerCamelCase	 =np.random.RandomState(snake_case_							).standard_normal((1, 4, 64, 64)							)
								lowerCamelCase	 =torch.from_numpy(snake_case_							).to(device=snake_case_   , dtype=snake_case_							)
								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(snake_case_							)
								ldmad_pipe.set_progress_bar_config(disable=snake_case_							)

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

								assert rgb.shape == (1, 512, 512, 3)
								assert depth.shape == (1, 512, 512)

								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       __A     (						unittest.TestCase    ):


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


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_="cpu"   , UpperCAmelCase_=torch.floataa   , UpperCAmelCase_=0							):
								lowerCamelCase	 =torch.Generator(device=snake_case_							).manual_seed(snake_case_							)
								lowerCamelCase	 =np.random.RandomState(snake_case_							).standard_normal((1, 4, 64, 64)							)
								lowerCamelCase	 =torch.from_numpy(snake_case_							).to(device=snake_case_   , dtype=snake_case_							)
								lowerCamelCase	 ={
								    """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							):
								lowerCamelCase	 =StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""							).to(snake_case_							)
								ldmad_pipe.set_progress_bar_config(disable=snake_case_							)

								lowerCamelCase	 =self.get_inputs(snake_case_							)
								lowerCamelCase	 =ldmad_pipe(**snake_case_							)
								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(snake_case_							)
								ldmad_pipe.set_progress_bar_config(disable=snake_case_							)

								lowerCamelCase	 =self.get_inputs(snake_case_							)
								lowerCamelCase	 =ldmad_pipe(**snake_case_							)
								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, 512, 512, 3)
								assert depth.shape == (1, 512, 512, 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 argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCAmelCase__						:			Tuple									=version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False			,      )						->  int:
				output_path.parent.mkdir(parents=_UpperCAmelCase			,      exist_ok=_UpperCAmelCase				)
				# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
				# so we check the torch version for backwards compatibility
				if is_torch_less_than_1_11:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      use_external_data_format=_UpperCAmelCase			,      enable_onnx_checker=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )
				else:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = False				)						->  Tuple:
				lowerCamelCase	 =torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								lowerCamelCase	 ="""cuda"""
				elif fpaa and not torch.cuda.is_available():
								raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""				)
				else:
								lowerCamelCase	 ="""cpu"""
				lowerCamelCase	 =Path(_UpperCAmelCase				)

				# VAE DECODER
				lowerCamelCase	 =AutoencoderKL.from_pretrained(model_path + """/vae"""				)
				lowerCamelCase	 =vae_decoder.config.latent_channels
				# forward only through the decoder part
				lowerCamelCase	 =vae_decoder.decode
				onnx_export(
				    _UpperCAmelCase			,      model_args=(
				        torch.randn(1			,      _UpperCAmelCase			,      25			,      25				).to(device=_UpperCAmelCase			,      dtype=_UpperCAmelCase				),
				        False,
				    )			,      output_path=output_path / """vae_decoder""" / """model.onnx"""			,      ordered_input_names=["""latent_sample""", """return_dict"""]			,      output_names=["""sample"""]			,      dynamic_axes={
				        """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
				    }			,      opset=_UpperCAmelCase			,      )
				del vae_decoder


if __name__ == "__main__":
							UpperCAmelCase__						:			Union[str, Any]									=argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_path''',
							    type=str,
							    required=True,
							    help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
							)

							parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
							parser.add_argument(
							    '''--opset''',
							    default=14,
							    type=int,
							    help='''The version of the ONNX operator set to use.''',
							)
							parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

							UpperCAmelCase__						:			Union[str, Any]									=parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print('''SD: Done: ONNX''')





