





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__(__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







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

	elif hor == 32:
		lowerCamelCase__              =	('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
		lowerCamelCase__              =	(32, 64, 128, 256)
		lowerCamelCase__              =	('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
	lowerCamelCase__              =	torch.load(F'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'							)
	lowerCamelCase__              =	model.state_dict()
	lowerCamelCase__              =	{
	    '''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''',
	}
	lowerCamelCase__              =	UNetaDModel(**__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()							)}'							)
	lowerCamelCase__              =	dict(zip(model.state_dict().keys() ,hf_value_function.state_dict().keys()							)							)
	for k, v in mapping.items():
		lowerCamelCase__              =	state_dict.pop(__snake_case							)
	hf_value_function.load_state_dict(__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(__snake_case ,__snake_case							)





def lowerCAmelCase__()       ->   Optional[int]:







	'''simple docstring'''







	lowerCamelCase__              =	{
	    '''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''',
	}

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

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

	hf_value_function.load_state_dict(__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(__snake_case ,__snake_case							)


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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_a						=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTNeoForCausalLM",
			    "GPTNeoForQuestionAnswering",
			    "GPTNeoForSequenceClassification",
			    "GPTNeoForTokenClassification",
			    "GPTNeoModel",
			    "GPTNeoPreTrainedModel",
			    "load_tf_weights_in_gpt_neo",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FlaxGPTNeoForCausalLM",
			    "FlaxGPTNeoModel",
			    "FlaxGPTNeoPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			_a						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



