


'''simple docstring'''



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase    :Any				       =     logging.get_logger(__name__)

__UpperCAmelCase    :Optional[Any]				       =     {"vocab_file": "vocab.json"}

__UpperCAmelCase    :Optional[int]				       =     {
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

__UpperCAmelCase    :List[str]				       =     {"mgp-str": 2_7}



class 			a       (	_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

     def __init__(       self  :	List[Any]							,				snake_case  :	List[str]							,				snake_case  :	Tuple="[GO]"							,				snake_case  :	Tuple="[GO]"							,				snake_case  :	Dict="[s]"							,				snake_case  :	Any="[GO]"							,				**snake_case  :	Any    )		->							List[str]:
          super().__init__(
              unk_token=snake_case							,				bos_token=snake_case							,				eos_token=snake_case							,				pad_token=snake_case							,				**snake_case							,				)

          with open(snake_case							,				encoding='''utf-8'''    ) as vocab_handle:
               __UpperCAmelCase			:				Union[str, Any]													=    json.load(snake_case    )
          __UpperCAmelCase			:				Union[str, Any]													=    {v: k for k, v in self.vocab.items()}

     @property
     def 				lowerCamelCase__				(       self  :	Tuple    )		->							List[str]:
          return len(self.vocab    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							List[str]:
          return dict(self.vocab							,				**self.added_tokens_encoder    )

     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	Optional[Any]    )		->							Optional[int]:
          __UpperCAmelCase			:				List[Any]													=    []
          for s in text:
               char_tokens.extend(snake_case    )
          return char_tokens

     def 				lowerCamelCase__				(       self  :	Tuple							,				snake_case  :	Tuple    )		->							Tuple:
          return self.vocab.get(snake_case							,				self.vocab.get(self.unk_token    )    )

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Optional[Any]    )		->							str:
          return self.decoder.get(snake_case    )






     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	str							,				snake_case  :	Optional[str] = None    )		->							Tuple[str]:
          if not os.path.isdir(snake_case    ):
               logger.error('''Vocabulary path ({}) should be a directory'''.format(snake_case    )    )
               return
          __UpperCAmelCase			:				Optional[Any]													=    os.path.join(
              snake_case							,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

          with open(snake_case							,				'''w'''							,				encoding='''utf-8'''    ) as f:
               f.write(json.dumps(self.vocab							,				indent=2							,				sort_keys=snake_case							,				ensure_ascii=snake_case    ) + '''\n'''    )

          return (vocab_file,)



'''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 			_a			(		_lowercase      :			Any						):





     '''simple docstring'''






     if hor == 128:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Union[str, Any]													=    (32, 128, 256)
          __UpperCAmelCase			:				str													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

     elif hor == 32:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Any													=    (32, 64, 128, 256)
          __UpperCAmelCase			:				Optional[int]													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
     __UpperCAmelCase			:				Any													=    torch.load(F'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'						)
     __UpperCAmelCase			:				str													=    model.state_dict()
     __UpperCAmelCase			:				int													=    {
         '''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''': 65536,
         '''mid_block_type''': '''MidResTemporalBlock1D''',
         '''act_fn''': '''mish''',
     }
     __UpperCAmelCase			:				Optional[Any]													=    UNetaDModel(**_lowercase						)
     print(F'length of state dict: {len(state_dict.keys()						)}'						)
     print(F'length of value function dict: {len(hf_value_function.state_dict().keys()						)}'						)
     __UpperCAmelCase			:				Union[str, Any]													=    dict(zip(model.state_dict().keys()    ,      hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          __UpperCAmelCase			:				List[Any]													=    state_dict.pop(_lowercase						)
     hf_value_function.load_state_dict(_lowercase						)

     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(_lowercase    ,      _lowercase						)




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    {
         '''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''': 65536,
         '''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''',
     }

     __UpperCAmelCase			:				str													=    torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''						)
     __UpperCAmelCase			:				Optional[Any]													=    model
     __UpperCAmelCase			:				Optional[int]													=    UNetaDModel(**_lowercase						)
     print(F'length of state dict: {len(state_dict.keys()						)}'						)
     print(F'length of value function dict: {len(hf_value_function.state_dict().keys()						)}'						)

     __UpperCAmelCase			:				Union[str, Any]													=    dict(zip(state_dict.keys()    ,      hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          __UpperCAmelCase			:				Dict													=    state_dict.pop(_lowercase						)

     hf_value_function.load_state_dict(_lowercase						)

     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(_lowercase    ,      _lowercase						)


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