



'''simple docstring'''




from dataclasses import dataclass, field
from typing import Optional

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






@dataclass
class 		_UpperCamelCase						:


			'''simple docstring'''

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


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	HfArgumentParser((ModelArguments,)      )
		(__SCREAMING_SNAKE_CASE		) :	Any					 =	parser.parse_args_into_dataclasses()

		# Load pretrained model and tokenizer

		# Use explicit specified encoder config
		if model_args.encoder_config_name:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	AutoConfig.from_pretrained(model_args.encoder_config_name      )
		# Use pretrained encoder model's config
		else:
				__SCREAMING_SNAKE_CASE :	Tuple					 =	AutoConfig.from_pretrained(model_args.encoder_model_name_or_path      )

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

		# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	True
		__SCREAMING_SNAKE_CASE :	str					 =	True

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	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=_lowerCamelCase ,   decoder_config=_lowerCamelCase ,   )

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

		# This is necessary to make Flax's generate() work
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	decoder_config.eos_token_id
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	decoder_start_token_id
		__SCREAMING_SNAKE_CASE :	Dict					 =	pad_token_id

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path      )

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path      )
		__SCREAMING_SNAKE_CASE :	List[str]					 =	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()




'''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()