





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						):


     if hor == 128:
          _SCREAMING_SNAKE_CASE							:						List[Any]         =				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          _SCREAMING_SNAKE_CASE							:						int         =				(32, 128, 256)
          _SCREAMING_SNAKE_CASE							:						str         =				("UpResnetBlock1D", "UpResnetBlock1D")

     elif hor == 32:
          _SCREAMING_SNAKE_CASE							:						str         =				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          _SCREAMING_SNAKE_CASE							:						Any         =				(32, 64, 128, 256)
          _SCREAMING_SNAKE_CASE							:						List[Any]         =				("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
     _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""						)
     _SCREAMING_SNAKE_CASE							:						Tuple         =				model.state_dict()
     _SCREAMING_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": 65536,
         "mid_block_type": "MidResTemporalBlock1D",
         "act_fn": "mish",
     }
     _SCREAMING_SNAKE_CASE							:						Optional[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							:						Union[str, Any]         =				dict(zip(model.state_dict().keys(),							hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          _SCREAMING_SNAKE_CASE							:						Optional[int]         =				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							:						Union[str, 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": 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",
     }

     _SCREAMING_SNAKE_CASE							:						int         =				torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"						)
     _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				model
     _SCREAMING_SNAKE_CASE							:						Optional[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							:						str         =				dict(zip(state_dict.keys(),							hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          _SCREAMING_SNAKE_CASE							:						List[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()






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A__							:  Any    ={
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =['''SpeechT5Tokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[Any]    =[
          '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''SpeechT5ForSpeechToText''',
          '''SpeechT5ForSpeechToSpeech''',
          '''SpeechT5ForTextToSpeech''',
          '''SpeechT5Model''',
          '''SpeechT5PreTrainedModel''',
          '''SpeechT5HifiGan''',
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A__							:  Tuple    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






