

"""simple docstring"""




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  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							if hor == 128:
														snake_case_          =   ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
														snake_case_          =   (32, 128, 256)
														snake_case_          =   ('UpResnetBlock1D', 'UpResnetBlock1D')

							elif hor == 32:
														snake_case_          =   ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
														snake_case_          =   (32, 64, 128, 256)
														snake_case_          =   ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
							snake_case_          =   torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''				)
							snake_case_          =   model.state_dict()
							snake_case_          =   {
							    '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': 65536,
							    'mid_block_type': 'MidResTemporalBlock1D',
							    'act_fn': 'mish',
							}
							snake_case_          =   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()				)}'''				)
							snake_case_          =   dict(zip(model.state_dict().keys()       ,							hf_value_function.state_dict().keys()				)				)
							for k, v in mapping.items():
														snake_case_          =   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  __lowerCamelCase     (			):







							'''simple docstring'''






							snake_case_          =   {
							    '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': 65536,
							    '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',
							}

							snake_case_          =   torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'				)
							snake_case_          =   model
							snake_case_          =   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()				)}'''				)

							snake_case_          =   dict(zip(state_dict.keys()       ,							hf_value_function.state_dict().keys()				)				)
							for k, v in mapping.items():
														snake_case_          =   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(32)
					# unet(128)
					value_function()







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel




