


import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							def 			_lowerCamelCase						(  self					):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__			       =     FlaxStableDiffusionPipeline.from_pretrained(
														    """stabilityai/stable-diffusion-2"""					,	revision="""bf16"""					,	dtype=jnp.bfloataa					,	)

														UpperCamelCase__			       =     """A painting of a squirrel eating a burger"""
														UpperCamelCase__			       =     jax.device_count()
														UpperCamelCase__			       =     num_samples * [prompt]
														UpperCamelCase__			       =     sd_pipe.prepare_inputs(__lowerCAmelCase					)

														UpperCamelCase__			       =     replicate(__lowerCAmelCase					)
														UpperCamelCase__			       =     shard(__lowerCAmelCase					)

														UpperCamelCase__			       =     jax.random.PRNGKey(0					)
														UpperCamelCase__			       =     jax.random.split(__lowerCAmelCase					,	jax.device_count()					)

														UpperCamelCase__			       =     sd_pipe(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	num_inference_steps=25					,	jit=__lowerCAmelCase					)[0]
														assert images.shape == (jax.device_count(), 1, 768, 768, 3)

														UpperCamelCase__			       =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
														UpperCamelCase__			       =     images[0, 253:256, 253:256, -1]

														UpperCamelCase__			       =     jnp.asarray(jax.device_get(image_slice.flatten()					)					)
														UpperCamelCase__			       =     jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512]					)
														print(f"""output_slice: {output_slice}"""					)
														assert jnp.abs(output_slice - expected_slice					).max() < 1E-2







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """stabilityai/stable-diffusion-2"""
														UpperCamelCase__       ,       UpperCamelCase__			       =     FlaxDPMSolverMultistepScheduler.from_pretrained(__lowerCAmelCase					,	subfolder="""scheduler"""					)
														UpperCamelCase__       ,       UpperCamelCase__			       =     FlaxStableDiffusionPipeline.from_pretrained(
														    __lowerCAmelCase					,	scheduler=__lowerCAmelCase					,	revision="""bf16"""					,	dtype=jnp.bfloataa					,	)
														UpperCamelCase__			       =     scheduler_params

														UpperCamelCase__			       =     """A painting of a squirrel eating a burger"""
														UpperCamelCase__			       =     jax.device_count()
														UpperCamelCase__			       =     num_samples * [prompt]
														UpperCamelCase__			       =     sd_pipe.prepare_inputs(__lowerCAmelCase					)

														UpperCamelCase__			       =     replicate(__lowerCAmelCase					)
														UpperCamelCase__			       =     shard(__lowerCAmelCase					)

														UpperCamelCase__			       =     jax.random.PRNGKey(0					)
														UpperCamelCase__			       =     jax.random.split(__lowerCAmelCase					,	jax.device_count()					)

														UpperCamelCase__			       =     sd_pipe(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	num_inference_steps=25					,	jit=__lowerCAmelCase					)[0]
														assert images.shape == (jax.device_count(), 1, 768, 768, 3)

														UpperCamelCase__			       =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
														UpperCamelCase__			       =     images[0, 253:256, 253:256, -1]

														UpperCamelCase__			       =     jnp.asarray(jax.device_get(image_slice.flatten()					)					)
														UpperCamelCase__			       =     jnp.array([0.4336, 0.4_2969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]					)
														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       _UpperCamelCase       (a__				:int    ):






							"""simple docstring"""





							if hor == 128:
														UpperCamelCase__			       =     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
														UpperCamelCase__			       =     (32, 128, 256)
														UpperCamelCase__			       =     ("""UpResnetBlock1D""", """UpResnetBlock1D""")

							elif hor == 32:
														UpperCamelCase__			       =     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
														UpperCamelCase__			       =     (32, 64, 128, 256)
														UpperCamelCase__			       =     ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
							UpperCamelCase__			       =     torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""    )
							UpperCamelCase__			       =     model.state_dict()
							UpperCamelCase__			       =     {
							    """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""": 6_5536,
							    """mid_block_type""": """MidResTemporalBlock1D""",
							    """act_fn""": """mish""",
							}
							UpperCamelCase__			       =     UNetaDModel(**a__    )
							print(f"""length of state dict: {len(state_dict.keys()    )}"""    )
							print(f"""length of value function dict: {len(hf_value_function.state_dict().keys()    )}"""    )
							UpperCamelCase__			       =     dict(zip(model.state_dict().keys()				,     hf_value_function.state_dict().keys()    )    )
							for k, v in mapping.items():
														UpperCamelCase__			       =     state_dict.pop(a__    )
							hf_value_function.load_state_dict(a__    )

							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(a__				,     a__    )





def       _UpperCamelCase       ():






							"""simple docstring"""





							UpperCamelCase__			       =     {
							    """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""": 6_5536,
							    """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""",
							}

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

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

							hf_value_function.load_state_dict(a__    )

							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(a__				,     a__    )


if __name__ == "__main__":
					unet(32)
					# unet(128)
					value_function()




