





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				KandinskyVaaPipeline
				__A      =				[
				    """image_embeds""",
				    """negative_image_embeds""",
				]
				__A      =				["""image_embeds""", """negative_image_embeds"""]
				__A      =				[
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				__A      =				False


				@property
				def _snake_case       (		self							):
								return 32


				@property
				def _snake_case       (		self							):
								return 32


				@property
				def _snake_case       (		self							):
								return self.time_input_dim


				@property
				def _snake_case       (		self							):
								return self.time_input_dim * 4


				@property
				def _snake_case       (		self							):
								return 100


				@property
				def _snake_case       (		self							):
								torch.manual_seed(0							)

								lowerCamelCase	 ={
								    """in_channels""": 4,
								    # Out channels is double in channels because predicts mean and variance
								    """out_channels""": 8,
								    """addition_embed_type""": """image""",
								    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
								    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
								    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
								    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
								    """layers_per_block""": 1,
								    """encoder_hid_dim""": self.text_embedder_hidden_size,
								    """encoder_hid_dim_type""": """image_proj""",
								    """cross_attention_dim""": self.cross_attention_dim,
								    """attention_head_dim""": 4,
								    """resnet_time_scale_shift""": """scale_shift""",
								    """class_embed_type""": None,
								}

								lowerCamelCase	 =UNetaDConditionModel(**UpperCAmelCase_							)
								return model


				@property
				def _snake_case       (		self							):
								return {
								    "block_out_channels": [32, 64],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 12,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}


				@property
				def _snake_case       (		self							):
								torch.manual_seed(0							)
								lowerCamelCase	 =VQModel(**self.dummy_movq_kwargs							)
								return model


				def _snake_case       (		self							):
								lowerCamelCase	 =self.dummy_unet
								lowerCamelCase	 =self.dummy_movq

								lowerCamelCase	 =DDIMScheduler(
								    num_train_timesteps=1000   , beta_schedule="""linear"""   , beta_start=0.0_0_0_8_5   , beta_end=0.0_1_2   , clip_sample=UpperCAmelCase_   , set_alpha_to_one=UpperCAmelCase_   , steps_offset=1   , prediction_type="""epsilon"""   , thresholding=UpperCAmelCase_   , )

								lowerCamelCase	 ={
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}
								return components


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=0							):
								lowerCamelCase	 =floats_tensor((1, self.text_embedder_hidden_size)   , rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase	 =floats_tensor((1, self.text_embedder_hidden_size)   , rng=random.Random(seed + 1							)							).to(
								    UpperCAmelCase_							)
								if str(UpperCAmelCase_							).startswith("""mps"""							):
												lowerCamelCase	 =torch.manual_seed(UpperCAmelCase_							)
								else:
												lowerCamelCase	 =torch.Generator(device=UpperCAmelCase_							).manual_seed(UpperCAmelCase_							)
								lowerCamelCase	 ={
								    """image_embeds""": image_embeds,
								    """negative_image_embeds""": negative_image_embeds,
								    """generator""": generator,
								    """height""": 64,
								    """width""": 64,
								    """guidance_scale""": 4.0,
								    """num_inference_steps""": 2,
								    """output_type""": """np""",
								}
								return inputs


				def _snake_case       (		self							):
								lowerCamelCase	 ="""cpu"""

								lowerCamelCase	 =self.get_dummy_components()

								lowerCamelCase	 =self.pipeline_class(**UpperCAmelCase_							)
								lowerCamelCase	 =pipe.to(UpperCAmelCase_							)

								pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

								lowerCamelCase	 =pipe(**self.get_dummy_inputs(UpperCAmelCase_							)							)
								lowerCamelCase	 =output.images

								lowerCamelCase	 =pipe(
								    **self.get_dummy_inputs(UpperCAmelCase_							)   , return_dict=UpperCAmelCase_   , )[0]

								lowerCamelCase	 =image[0, -3:, -3:, -1]
								lowerCamelCase	 =image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								lowerCamelCase	 =np.array(
								    [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]							)

								assert (
								    np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
								), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
								), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




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


				def _snake_case       (		self							):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


				def _snake_case       (		self							):
								lowerCamelCase	 =load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""							)

								lowerCamelCase	 =KandinskyVaaPriorPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-prior"""   , torch_dtype=torch.floataa							)
								pipe_prior.to(UpperCAmelCase_							)

								lowerCamelCase	 =KandinskyVaaPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-decoder"""   , torch_dtype=torch.floataa							)
								lowerCamelCase	 =pipeline.to(UpperCAmelCase_							)
								pipeline.set_progress_bar_config(disable=UpperCAmelCase_							)

								lowerCamelCase	 ="""red cat, 4k photo"""

								lowerCamelCase	 =torch.Generator(device="""cuda"""							).manual_seed(0							)
								lowerCamelCase ,  lowerCamelCase	 =pipe_prior(
								    UpperCAmelCase_   , generator=UpperCAmelCase_   , num_inference_steps=5   , negative_prompt=""""""   , ).to_tuple()

								lowerCamelCase	 =torch.Generator(device="""cuda"""							).manual_seed(0							)
								lowerCamelCase	 =pipeline(
								    image_embeds=UpperCAmelCase_   , negative_image_embeds=UpperCAmelCase_   , generator=UpperCAmelCase_   , num_inference_steps=100   , output_type="""np"""   , )

								lowerCamelCase	 =output.images[0]

								assert image.shape == (512, 512, 3)

								assert_mean_pixel_difference(UpperCAmelCase_   , UpperCAmelCase_							)












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''')





