



'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase__ :			Optional[Any]												=				logging.get_logger(__name__)

UpperCamelCase__ :			List[Any]												=				{
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}






class 		_UpperCamelCase						(							lowerCamelCase__					,      lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	'''dinat'''

			_A   :	Optional[Any]    					=	{
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}

			def __init__(							self  :				Optional[Any]							,  lowerCAmelCase__  :				Dict=4							,  lowerCAmelCase__  :				List[Any]=3							,  lowerCAmelCase__  :				List[str]=6_4							,  lowerCAmelCase__  :				Tuple=[3, 4, 6, 5]							,  lowerCAmelCase__  :				Optional[Any]=[2, 4, 8, 1_6]							,  lowerCAmelCase__  :				str=7							,  lowerCAmelCase__  :				Optional[Any]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]							,  lowerCAmelCase__  :				Tuple=3.0							,  lowerCAmelCase__  :				str=True							,  lowerCAmelCase__  :				Union[str, Any]=0.0							,  lowerCAmelCase__  :				Dict=0.0							,  lowerCAmelCase__  :				List[Any]=0.1							,  lowerCAmelCase__  :				Union[str, Any]="gelu"							,  lowerCAmelCase__  :				int=0.02							,  lowerCAmelCase__  :				Any=1E-5							,  lowerCAmelCase__  :				int=0.0							,  lowerCAmelCase__  :				Union[str, Any]=None							,  lowerCAmelCase__  :				Optional[int]=None							,  **lowerCAmelCase__  :				Union[str, Any]							,  ):





					"""simple docstring"""






					super().__init__(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	patch_size
					__SCREAMING_SNAKE_CASE :	Any					 =	num_channels
					__SCREAMING_SNAKE_CASE :	Any					 =	embed_dim
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	depths
					__SCREAMING_SNAKE_CASE :	Dict					 =	len(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	num_heads
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	kernel_size
					__SCREAMING_SNAKE_CASE :	str					 =	dilations
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	mlp_ratio
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	qkv_bias
					__SCREAMING_SNAKE_CASE :	Any					 =	hidden_dropout_prob
					__SCREAMING_SNAKE_CASE :	int					 =	attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE :	Dict					 =	drop_path_rate
					__SCREAMING_SNAKE_CASE :	str					 =	hidden_act
					__SCREAMING_SNAKE_CASE :	List[str]					 =	layer_norm_eps
					__SCREAMING_SNAKE_CASE :	Tuple					 =	initializer_range
					# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
					# this indicates the channel dimension after the last stage of the model
					__SCREAMING_SNAKE_CASE :	int					 =	int(embed_dim * 2 ** (len(lowerCAmelCase__		) - 1)		)
					__SCREAMING_SNAKE_CASE :	str					 =	layer_scale_init_value
					__SCREAMING_SNAKE_CASE :	int					 =	["""stem"""] + [F"stage{idx}" for idx in range(1							,  len(lowerCAmelCase__		) + 1		)]
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	get_aligned_output_features_output_indices(
					    out_features=lowerCAmelCase__							,  out_indices=lowerCAmelCase__							,  stage_names=self.stage_names		)




'''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_     (			_lowerCamelCase:     int      ):
		if hor == 1_28:
				__SCREAMING_SNAKE_CASE :	Any					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	(32, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	str					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""")

		elif hor == 32:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	str					 =	(32, 64, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	Tuple					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"      )
		__SCREAMING_SNAKE_CASE :	Any					 =	model.state_dict()
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{
		    """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_55_36,
		    """mid_block_type""": """MidResTemporalBlock1D""",
		    """act_fn""": """mish""",
		}
		__SCREAMING_SNAKE_CASE :	int					 =	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()      )}"      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dict(zip(model.state_dict().keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	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        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Dict					 =	{
		    """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, 1_28, 2_56),
		    """layers_per_block""": 1,
		    """downsample_each_block""": True,
		    """sample_size""": 6_55_36,
		    """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""",
		}

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	model
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	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()      )}"      )

		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	dict(zip(state_dict.keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	str					 =	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(32)
						# unet(128)
						value_function()