



import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
							import jax
							import jax.numpy as jnp
							from flax.jax_utils import replicate
							from flax.training.common_utils import shard


@slow
@require_flax
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								super().tearDown()
								gc.collect()



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/sd2-inpaint/init_image.png'       )
								A__	  =     load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'       )

								A__	  =     'xvjiarui/stable-diffusion-2-inpainting'
								A__  ,			A__	  =     FlaxStableDiffusionInpaintPipeline.from_pretrained(_snake_case					,       safety_checker=_snake_case       )

								A__	  =     'Face of a yellow cat, high resolution, sitting on a park bench'

								A__	  =     jax.random.PRNGKey(0       )
								A__	  =     50

								A__	  =     jax.device_count()
								A__	  =     num_samples * [prompt]
								A__	  =     num_samples * [init_image]
								A__	  =     num_samples * [mask_image]
								A__  ,			A__  ,			A__	  =     pipeline.prepare_inputs(_snake_case					,       _snake_case					,       _snake_case       )

								# shard inputs and rng
								A__	  =     replicate(_snake_case       )
								A__	  =     jax.random.split(_snake_case					,       jax.device_count()       )
								A__	  =     shard(_snake_case       )
								A__	  =     shard(_snake_case       )
								A__	  =     shard(_snake_case       )

								A__	  =     pipeline(
								    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       jit=_snake_case       )

								A__	  =     output.images.reshape(_snake_case					,       5_12					,       5_12					,       3       )

								A__	  =     images[0, 2_53:2_56, 2_53:2_56, -1]

								A__	  =     jnp.asarray(jax.device_get(image_slice.flatten()       )       )
								A__	  =     jnp.array(
								    [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]       )
								print(F'''output_slice: {output_slice}'''       )
								assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2









import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)

os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)


def A    (							__UpperCamelCase       )    ->   Union[str, Any]:
			if hor == 128:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D')

			elif hor == 32:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 64, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
			A__	  =     torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''       )
			A__	  =     model.state_dict()
			A__	  =     {
			    'down_block_types': down_block_types,
			    'block_out_channels': block_out_channels,
			    'up_block_types': up_block_types,
			    'layers_per_block': 1,
			    'use_timestep_embedding': True,
			    'out_block_type': 'OutConv1DBlock',
			    'norm_num_groups': 8,
			    'downsample_each_block': False,
			    'in_channels': 14,
			    'out_channels': 14,
			    'extra_in_channels': 0,
			    'time_embedding_type': 'positional',
			    'flip_sin_to_cos': False,
			    'freq_shift': 1,
			    'sample_size': 65_536,
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'act_fn': 'mish',
			}
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )
			A__	  =     dict(zip(model.state_dict().keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )
			hf_value_function.load_state_dict(__UpperCamelCase       )

			torch.save(hf_value_function.state_dict()			,						f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''       )
			with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''			,						'w'       ) as f:
						json.dump(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							)    ->   List[str]:
			A__	  =     {
			    'in_channels': 14,
			    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
			    'up_block_types': (),
			    'out_block_type': 'ValueFunction',
			    'mid_block_type': 'ValueFunctionMidBlock1D',
			    'block_out_channels': (32, 64, 128, 256),
			    'layers_per_block': 1,
			    'downsample_each_block': True,
			    'sample_size': 65_536,
			    'out_channels': 14,
			    'extra_in_channels': 0,
			    'time_embedding_type': 'positional',
			    'use_timestep_embedding': True,
			    'flip_sin_to_cos': False,
			    'freq_shift': 1,
			    'norm_num_groups': 8,
			    'act_fn': 'mish',
			}

			A__	  =     torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'       )
			A__	  =     model
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )

			A__	  =     dict(zip(state_dict.keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )

			hf_value_function.load_state_dict(__UpperCamelCase       )

			torch.save(hf_value_function.state_dict()			,						'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'       )
			with open('hub/hopper-medium-v2/value_function/config.json'			,						'w'       ) as f:
						json.dump(__UpperCamelCase			,						__UpperCamelCase       )


if __name__ == "__main__":
							unet(3_2)
							# unet(128)
							value_function()




