
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 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     if hor == 128:
          UpperCamelCase__				=    ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
          UpperCamelCase__				=    (32, 128, 256)
          UpperCamelCase__				=    ("""UpResnetBlock1D""", """UpResnetBlock1D""")

     elif hor == 32:
          UpperCamelCase__				=    ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
          UpperCamelCase__				=    (32, 64, 128, 256)
          UpperCamelCase__				=    ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
     UpperCamelCase__				=    torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"						)
     UpperCamelCase__				=    model.state_dict()
     UpperCamelCase__				=    {
         """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""": 65_536,
         """mid_block_type""": """MidResTemporalBlock1D""",
         """act_fn""": """mish""",
     }
     UpperCamelCase__				=    UNetaDModel(**__a						)
     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__				=    dict(zip(model.state_dict().keys()	,		hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          UpperCamelCase__				=    state_dict.pop(__a						)
     hf_value_function.load_state_dict(__a						)

     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(__a	,		__a						)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    {
         """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""": 65_536,
         """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__				=    torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""						)
     UpperCamelCase__				=    model
     UpperCamelCase__				=    UNetaDModel(**__a						)
     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__				=    dict(zip(state_dict.keys()	,		hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          UpperCamelCase__				=    state_dict.pop(__a						)

     hf_value_function.load_state_dict(__a						)

     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(__a	,		__a						)


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







import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows






def 						__magic_name__			(   __a      :	Any						):  # picklable for multiprocessing



     '''simple docstring'''




     return i + 1






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 						__magic_name__			(   ):



     '''simple docstring'''




     with parallel_backend("""spark"""						):
          assert ParallelBackendConfig.backend_name == "spark"

     UpperCamelCase__				=    [1, 2, 3]
     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=2						)

     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=-1						)






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc"""	,		[2, -1]						)
def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    [1, 2]
     UpperCamelCase__				=    {"""a""": 1, """b""": 2}
     UpperCamelCase__				=    {"""a""": [1, 2], """b""": [3, 4]}
     UpperCamelCase__				=    {"""a""": {"""1""": 1}, """b""": 2}
     UpperCamelCase__				=    {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
     UpperCamelCase__				=    [2, 3]
     UpperCamelCase__				=    {"""a""": 2, """b""": 3}
     UpperCamelCase__				=    {"""a""": [2, 3], """b""": [4, 5]}
     UpperCamelCase__				=    {"""a""": {"""1""": 2}, """b""": 3}
     UpperCamelCase__				=    {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}

     with parallel_backend("""spark"""						):
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa





