






'''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__				:						str							):




      '''simple docstring'''






      if hor == 1_2_8:
            SCREAMING_SNAKE_CASE__     :				List[Any]									=('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
            SCREAMING_SNAKE_CASE__     :				Dict									=(3_2, 1_2_8, 2_5_6)
            SCREAMING_SNAKE_CASE__     :				List[str]									=('''UpResnetBlock1D''', '''UpResnetBlock1D''')

      elif hor == 3_2:
            SCREAMING_SNAKE_CASE__     :				Tuple									=('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
            SCREAMING_SNAKE_CASE__     :				int									=(3_2, 6_4, 1_2_8, 2_5_6)
            SCREAMING_SNAKE_CASE__     :				Optional[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__     :				int									=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''': 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''',
      }
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=UNetaDModel(**UpperCamelCase__							)
      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__     :				Tuple									=dict(zip(model.state_dict().keys(),	hf_value_function.state_dict().keys()							)							)
      for k, v in mapping.items():
            SCREAMING_SNAKE_CASE__     :				Any									=state_dict.pop(UpperCamelCase__							)
      hf_value_function.load_state_dict(UpperCamelCase__							)

      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(UpperCamelCase__,	UpperCamelCase__							)





def  _a(  ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				str									={
          '''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''',
      }

      SCREAMING_SNAKE_CASE__     :				Dict									=torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''							)
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model
      SCREAMING_SNAKE_CASE__     :				int									=UNetaDModel(**UpperCamelCase__							)
      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(state_dict.keys(),	hf_value_function.state_dict().keys()							)							)
      for k, v in mapping.items():
            SCREAMING_SNAKE_CASE__     :				Dict									=state_dict.pop(UpperCamelCase__							)

      hf_value_function.load_state_dict(UpperCamelCase__							)

      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(UpperCamelCase__,	UpperCamelCase__							)


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







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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					= {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['DebertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DebertaForMaskedLM',
      'DebertaForQuestionAnswering',
      'DebertaForSequenceClassification',
      'DebertaForTokenClassification',
      'DebertaModel',
      'DebertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDebertaForMaskedLM',
      'TFDebertaForQuestionAnswering',
      'TFDebertaForSequenceClassification',
      'TFDebertaForTokenClassification',
      'TFDebertaModel',
      'TFDebertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

  a_					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)