





from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser






@dataclass
class        _a:
		lowerCamelCase__     :str   				=		field(
		    metadata={'help': 'The output directory where the model will be written.'}	,   )
		lowerCamelCase__     :str   				=		field(
		    metadata={
		        'help': (
		            'The encoder model checkpoint for weights initialization.'
		            'Don\'t set if you want to train an encoder model from scratch.'
		        )
		    }	,   )
		lowerCamelCase__     :str   				=		field(
		    metadata={
		        'help': (
		            'The decoder model checkpoint for weights initialization.'
		            'Don\'t set if you want to train a decoder model from scratch.'
		        )
		    }	,   )
		lowerCamelCase__     :Optional[str]   				=		field(
		    default=__A	,   metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}    )
		lowerCamelCase__     :Optional[str]   				=		field(
		    default=__A	,   metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}    )







def    A				(							):
					_snake_case       :					Optional[int]				       =   HfArgumentParser((ModelArguments,)				)
					((_snake_case)       , )       :					Optional[int]				       =   parser.parse_args_into_dataclasses()

					# Load pretrained model and tokenizer

					# Use explicit specified encoder config
					if model_args.encoder_config_name:
										_snake_case       :					int				       =   AutoConfig.from_pretrained(model_args.encoder_config_name				)
					# Use pretrained encoder model's config
					else:
										_snake_case       :					Tuple				       =   AutoConfig.from_pretrained(model_args.encoder_model_name_or_path				)

					# Use explicit specified decoder config
					if model_args.decoder_config_name:
										_snake_case       :					Dict				       =   AutoConfig.from_pretrained(model_args.decoder_config_name				)
					# Use pretrained decoder model's config
					else:
										_snake_case       :					List[str]				       =   AutoConfig.from_pretrained(model_args.decoder_model_name_or_path				)

					# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
					_snake_case       :					str				       =   True
					_snake_case       :					Optional[int]				       =   True

					_snake_case       :					List[str]				       =   FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
					    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path  ,  decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path  ,  encoder_config=UpperCAmelCase  ,  decoder_config=UpperCAmelCase  ,  )

					# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
					_snake_case       :					Tuple				       =   decoder_config.decoder_start_token_id
					_snake_case       :					Optional[Any]				       =   decoder_config.pad_token_id
					if decoder_start_token_id is None:
										_snake_case       :					Any				       =   decoder_config.bos_token_id
					if pad_token_id is None:
										_snake_case       :					int				       =   decoder_config.eos_token_id

					# This is necessary to make Flax's generate() work
					_snake_case       :					Dict				       =   decoder_config.eos_token_id
					_snake_case       :					Tuple				       =   decoder_start_token_id
					_snake_case       :					Tuple				       =   pad_token_id

					_snake_case       :					Dict				       =   AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path				)

					_snake_case       :					List[str]				       =   AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path				)
					_snake_case       :					Optional[Any]				       =   tokenizer.convert_ids_to_tokens(model.config.pad_token_id				)

					model.save_pretrained(model_args.output_dir				)
					image_processor.save_pretrained(model_args.output_dir				)
					tokenizer.save_pretrained(model_args.output_dir				)


if __name__ == "__main__":
							main()






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				(							UpperCAmelCase				):
					if hor == 128:
										_snake_case       :					int				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Tuple				       =   (32, 128, 256)
										_snake_case       :					str				       =   ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										_snake_case       :					List[Any]				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Optional[int]				       =   (32, 64, 128, 256)
										_snake_case       :					int				       =   ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					_snake_case       :					Dict				       =   torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""				)
					_snake_case       :					Dict				       =   model.state_dict()
					_snake_case       :					Tuple				       =   {
					    "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       :					Any				       =   UNetaDModel(**UpperCAmelCase				)
					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       :					List[Any]				       =   dict(zip(model.state_dict().keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Optional[int]				       =   state_dict.pop(UpperCAmelCase				)
					hf_value_function.load_state_dict(UpperCAmelCase				)

					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(UpperCAmelCase  ,  UpperCAmelCase				)







def    A				(							):
					_snake_case       :					Any				       =   {
					    "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       :					Dict				       =   torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"				)
					_snake_case       :					Optional[int]				       =   model
					_snake_case       :					List[str]				       =   UNetaDModel(**UpperCAmelCase				)
					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       :					Optional[int]				       =   dict(zip(state_dict.keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Any				       =   state_dict.pop(UpperCAmelCase				)

					hf_value_function.load_state_dict(UpperCAmelCase				)

					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(UpperCAmelCase  ,  UpperCAmelCase				)


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