





'''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_2_8:
						__lowercase										=							('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						__lowercase										=							(3_2, 1_2_8, 2_5_6)
						__lowercase										=							('''UpResnetBlock1D''', '''UpResnetBlock1D''')

			elif hor == 3_2:
						__lowercase										=							('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						__lowercase										=							(3_2, 6_4, 1_2_8, 2_5_6)
						__lowercase										=							('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
			__lowercase										=							torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"				)
			__lowercase										=							model.state_dict()
			__lowercase										=							{
			    '''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''': 1_4,
			    '''out_channels''': 1_4,
			    '''extra_in_channels''': 0,
			    '''time_embedding_type''': '''positional''',
			    '''flip_sin_to_cos''': False,
			    '''freq_shift''': 1,
			    '''sample_size''': 6_5_5_3_6,
			    '''mid_block_type''': '''MidResTemporalBlock1D''',
			    '''act_fn''': '''mish''',
			}
			__lowercase										=							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()				)}"				)
			__lowercase										=							dict(zip(model.state_dict().keys()		,       hf_value_function.state_dict().keys()				)				)
			for k, v in mapping.items():
						__lowercase										=							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						(				):
			__lowercase										=							{
			    '''in_channels''': 1_4,
			    '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
			    '''up_block_types''': (),
			    '''out_block_type''': '''ValueFunction''',
			    '''mid_block_type''': '''ValueFunctionMidBlock1D''',
			    '''block_out_channels''': (3_2, 6_4, 1_2_8, 2_5_6),
			    '''layers_per_block''': 1,
			    '''downsample_each_block''': True,
			    '''sample_size''': 6_5_5_3_6,
			    '''out_channels''': 1_4,
			    '''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''',
			}

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

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



import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase = "▁"     ,_lowerCamelCase = True     ,_lowerCamelCase = "<unk>"     ,_lowerCamelCase = "</s>"     ,_lowerCamelCase = "<pad>"     ,)							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							{
				    '''pad''': {'''id''': 0, '''token''': pad_token},
				    '''eos''': {'''id''': 1, '''token''': eos_token},
				    '''unk''': {'''id''': 2, '''token''': unk_token},
				}

				__lowercase										=							[None] * len(self.special_tokens )
				for token_dict in self.special_tokens.values():
							__lowercase										=							token_dict['''token''']

				__lowercase										=							Tokenizer(Unigram() )

				__lowercase										=							normalizers.Sequence(
				    [
				        normalizers.Nmt(),
				        normalizers.NFKC(),
				        normalizers.Replace(Regex(''' {2,}''' )     ,''' ''' ),
				        normalizers.Lowercase(),
				    ] )
				__lowercase										=							pre_tokenizers.Sequence(
				    [
				        pre_tokenizers.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase ),
				        pre_tokenizers.Digits(individual_digits=_lowerCamelCase ),
				        pre_tokenizers.Punctuation(),
				    ] )
				__lowercase										=							decoders.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase )

				__lowercase										=							TemplateProcessing(
				    single=f"$A {self.special_tokens['eos']['token']}"     ,special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])]     ,)

				__lowercase										=							{
				    '''model''': '''SentencePieceUnigram''',
				    '''replacement''': replacement,
				    '''add_prefix_space''': add_prefix_space,
				}

				super().__init__(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				if isinstance(_lowerCamelCase     ,_lowerCamelCase ):
							__lowercase										=							[files]
				self._tokenizer.train(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							List[str]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				self._tokenizer.train_from_iterator(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							json.loads(self._tokenizer.to_str() )

				__lowercase										=							self.special_tokens['''unk''']['''id''']

				__lowercase										=							Tokenizer.from_str(json.dumps(_lowerCamelCase ) )





