
'''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 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[int]:
	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': 65_536,
	    'mid_block_type': 'MidResTemporalBlock1D',
	    'act_fn': 'mish',
	}
	_snake_case   						=		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()	)}'	)
	_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(__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 				SCREAMING_SNAKE_CASE__	(   )       ->	List[Any]:
	_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': 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',
	}

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

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



'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase    :							List[str]      =					{
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Optional[int]      =					["Pix2StructImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Tuple      =					[
					    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Pix2StructPreTrainedModel",
					    "Pix2StructForConditionalGeneration",
					    "Pix2StructVisionModel",
					    "Pix2StructTextModel",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					lowercase    :							List[str]      =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

