
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	( lowercase			)       ->  List[Any]:
		'''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': 65_536,
		    'mid_block_type': 'MidResTemporalBlock1D',
		    'act_fn': 'mish',
		}
		UpperCamelCase          =					UNetaDModel(**SCREAMING_SNAKE_CASE_			)
		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(SCREAMING_SNAKE_CASE_			)
		hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_			)

		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(SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_			)


def 				A	( )       ->  int:
		'''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': 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',
		}

		UpperCamelCase          =					torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'			)
		UpperCamelCase          =					model
		UpperCamelCase          =					UNetaDModel(**SCREAMING_SNAKE_CASE_			)
		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(SCREAMING_SNAKE_CASE_			)

		hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_			)

		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(SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_			)


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
