'''simple docstring'''


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCAmelCase	:       str						       =	version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   tuple    ,					UpperCamelCase__				:   Path    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   Union[str, Any]=False    ,					):


	"""simple docstring"""
	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       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   bool = False       ):


	"""simple docstring"""
	__UpperCAmelCase							=			torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		__UpperCAmelCase							=			'''cuda'''
	elif fpaa and not torch.cuda.is_available():
		raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''       )
	else:
		__UpperCAmelCase							=			'''cpu'''
	__UpperCAmelCase							=			Path(UpperCamelCase__       )

	# VAE DECODER
	__UpperCAmelCase							=			AutoencoderKL.from_pretrained(model_path + '''/vae'''       )
	__UpperCAmelCase							=			vae_decoder.config.latent_channels
	# forward only through the decoder part
	__UpperCAmelCase							=			vae_decoder.decode
	onnx_export(
	    UpperCamelCase__    ,					model_args=(
	        torch.randn(1    ,					UpperCamelCase__    ,					2_5    ,					2_5       ).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__":
				__lowerCAmelCase	:       List[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")

				__lowerCAmelCase	:       Optional[int]						       =	parser.parse_args()
				print(args.output_path)
				convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
				print("SD: Done: ONNX")

'''simple docstring'''


import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp






@slow
@require_flax
class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							List[Any]     ,		__a				:							List[str]     ,		__a				:							Optional[Any]	)							->	List[Any]:
								return f"""gaussian_noise_s={seed}_shape={'_'.join([str(__a	) for s in shape]	)}.npy"""





							def       snake_case__						(      self				:							Dict	)							->	Union[str, Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Tuple=0     ,		__a				:							List[Any]=(4, 4, 6_4, 6_4)     ,		__a				:							Optional[Any]=False	)							->	Tuple:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			jnp.array(load_hf_numpy(self.get_file_format(__a     ,		__a	)	)     ,		dtype=__a	)
								return image





							def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]=False     ,		__a				:							Optional[Any]="CompVis/stable-diffusion-v1-4"	)							->	Any:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			'''bf16''' if fpaa else None

								__UpperCAmelCase				,			__UpperCAmelCase							=			FlaxUNetaDConditionModel.from_pretrained(
								    __a     ,		subfolder='''unet'''     ,		dtype=__a     ,		revision=__a	)
								return model, params





							def       snake_case__						(      self				:							str     ,		__a				:							int=0     ,		__a				:							Tuple=(4, 7_7, 7_6_8)     ,		__a				:							Optional[int]=False	)							->	Union[str, Any]:
								__UpperCAmelCase							=			jnp.bfloataa if fpaa else jnp.floataa
								__UpperCAmelCase							=			jnp.array(load_hf_numpy(self.get_file_format(__a     ,		__a	)	)     ,		dtype=__a	)
								return hidden_states





							@parameterized.expand(
							    [
							        # fmt: off
							        [8_3, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
							        [1_7, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
							        [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
							        [3, 1_0_0_0, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
							        # fmt: on
							    ]	)
							def       snake_case__						(      self				:							Tuple     ,		__a				:							Tuple     ,		__a				:							str     ,		__a				:							Optional[Any]	)							->	Any:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_latents(__a     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_encoder_hidden_states(__a     ,		fpaa=__a	)

								__UpperCAmelCase							=			model.apply(
								    {'''params''': params}     ,		__a     ,		jnp.array(__a     ,		dtype=jnp.intaa	)     ,		encoder_hidden_states=__a     ,		).sample

								assert sample.shape == latents.shape

								__UpperCAmelCase							=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)     ,		dtype=jnp.floataa	)
								__UpperCAmelCase							=			jnp.array(__a     ,		dtype=jnp.floataa	)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(__a     ,		__a     ,		atol=1e-2	)






							@parameterized.expand(
							    [
							        # fmt: off
							        [8_3, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
							        [1_7, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
							        [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
							        [3, 1_0_0_0, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
							        # fmt: on
							    ]	)
							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							Optional[int]     ,		__a				:							Optional[Any]     ,		__a				:							Optional[Any]	)							->	Union[str, Any]:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_latents(__a     ,		shape=(4, 4, 9_6, 9_6)     ,		fpaa=__a	)
								__UpperCAmelCase							=			self.get_encoder_hidden_states(__a     ,		shape=(4, 7_7, 1_0_2_4)     ,		fpaa=__a	)

								__UpperCAmelCase							=			model.apply(
								    {'''params''': params}     ,		__a     ,		jnp.array(__a     ,		dtype=jnp.intaa	)     ,		encoder_hidden_states=__a     ,		).sample

								assert sample.shape == latents.shape

								__UpperCAmelCase							=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)     ,		dtype=jnp.floataa	)
								__UpperCAmelCase							=			jnp.array(__a     ,		dtype=jnp.floataa	)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(__a     ,		__a     ,		atol=1e-2	)
