



'''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__   (					UpperCAmelCase_							):
      if hor == 1_2_8:
            _UpperCamelCase :		Any			    =       ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
            _UpperCamelCase :		List[str]			    =       (3_2, 1_2_8, 2_5_6)
            _UpperCamelCase :		Optional[int]			    =       ('UpResnetBlock1D', 'UpResnetBlock1D')

      elif hor == 3_2:
            _UpperCamelCase :		Dict			    =       ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
            _UpperCamelCase :		Any			    =       (3_2, 6_4, 1_2_8, 2_5_6)
            _UpperCamelCase :		Optional[Any]			    =       ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
      _UpperCamelCase :		Dict			    =       torch.load(f'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'							)
      _UpperCamelCase :		Any			    =       model.state_dict()
      _UpperCamelCase :		Union[str, 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': 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',
      }
      _UpperCamelCase :		int			    =       UNetaDModel(**SCREAMING_SNAKE_CASE_							)
      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 :		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(SCREAMING_SNAKE_CASE_							)
      hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_							)

      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(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_							)






def A__   (					):
      _UpperCamelCase :		Union[str, Any]			    =       {
          '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',
      }

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

      hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_							)

      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(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_							)


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







'''simple docstring'''
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


snake_case_			:     Any  	=  {
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  [
					    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXJapaneseForCausalLM',
					    'GPTNeoXJapaneseLayer',
					    'GPTNeoXJapaneseModel',
					    'GPTNeoXJapanesePreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
					from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox_japanese import (
										    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXJapaneseForCausalLM,
										    GPTNeoXJapaneseLayer,
										    GPTNeoXJapaneseModel,
										    GPTNeoXJapanesePreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Any  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


