
"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Union[str, 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': 6_5536,
							    'mid_block_type': 'MidResTemporalBlock1D',
							    'act_fn': 'mish',
							}
							_lowerCAmelCase  	=			UNetaDModel(**__lowerCAmelCase						)
							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(__lowerCAmelCase						)
							hf_value_function.load_state_dict(__lowerCAmelCase						)

							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(__lowerCAmelCase     ,  __lowerCAmelCase						)






def 	__snake_case  (    ):






							"""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': 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',
							}

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

							hf_value_function.load_state_dict(__lowerCAmelCase						)

							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(__lowerCAmelCase     ,  __lowerCAmelCase						)


if __name__ == "__main__":
	unet(3_2)
	# unet(128)
	value_function()



"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					"mobilenet_v1"


				def __init__(					self	:			List[Any]				,     UpperCAmelCase_	:			Optional[int]=3				,     UpperCAmelCase_	:			str=224				,     UpperCAmelCase_	:			Union[str, Any]=1.0				,     UpperCAmelCase_	:			List[str]=8				,     UpperCAmelCase_	:			Union[str, Any]="relu6"				,     UpperCAmelCase_	:			Optional[Any]=True				,     UpperCAmelCase_	:			Optional[int]=0.999				,     UpperCAmelCase_	:			Optional[Any]=0.02				,     UpperCAmelCase_	:			Union[str, Any]=0.001				,     **UpperCAmelCase_	:			List[Any]				,     )		->  Dict:







											"""simple docstring"""




											super().__init__(**UpperCAmelCase_  )

											if depth_multiplier <= 0:
																		raise ValueError('depth_multiplier must be greater than zero.'  )

											_lowerCAmelCase  	=			num_channels
											_lowerCAmelCase  	=			image_size
											_lowerCAmelCase  	=			depth_multiplier
											_lowerCAmelCase  	=			min_depth
											_lowerCAmelCase  	=			hidden_act
											_lowerCAmelCase  	=			tf_padding
											_lowerCAmelCase  	=			classifier_dropout_prob
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			layer_norm_eps






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					version.parse("1.11"							)


				@property
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											return OrderedDict([('pixel_values', {0: 'batch'})]  )


				@property
				def 				__lowerCamelCase	(					self	:			Dict  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											if self.task == "image-classification":
																		return OrderedDict([('logits', {0: 'batch'})]  )
											else:
																		return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]  )







				@property
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  float:







											"""simple docstring"""




											return 1E-4






