


import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__     =	namedtuple(
    "_TestCommandArgs",
    [
        "dataset",
        "name",
        "cache_dir",
        "data_dir",
        "all_configs",
        "save_infos",
        "ignore_verifications",
        "force_redownload",
        "clear_cache",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def       _UpperCamelCase       (a__				:str				,     a__				:int    ):






							"""simple docstring"""





							return (abs(source - target    ) / target) < 0.01





@pytest.mark.integration
def       _UpperCamelCase       (a__				:List[str]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     _TestCommandArgs(dataset=a__				,     all_configs=a__				,     save_infos=a__    )
							UpperCamelCase__			       =     TestCommand(*a__    )
							test_command.run()
							UpperCamelCase__			       =     os.path.join(a__				,     """README.md"""    )
							assert os.path.exists(a__    )
							UpperCamelCase__			       =     DatasetInfosDict.from_directory(a__    )
							UpperCamelCase__			       =     DatasetInfosDict(
							    {
							        """default""": DatasetInfo(
							            features=Features(
							                {
							                    """tokens""": Sequence(Value("""string"""    )    ),
							                    """ner_tags""": Sequence(
							                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]    )    ),
							                    """langs""": Sequence(Value("""string"""    )    ),
							                    """spans""": Sequence(Value("""string"""    )    ),
							                }    )				,     splits=[
							                {
							                    """name""": """train""",
							                    """num_bytes""": 235_1563,
							                    """num_examples""": 1_0000,
							                },
							                {
							                    """name""": """validation""",
							                    """num_bytes""": 23_8418,
							                    """num_examples""": 1000,
							                },
							            ]				,     download_size=394_0680				,     dataset_size=258_9981				,     )
							    }    )
							assert dataset_infos.keys() == expected_dataset_infos.keys()
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														UpperCamelCase__       ,       UpperCamelCase__			       =     getattr(dataset_infos["""default"""]				,     a__    ), getattr(expected_dataset_infos["""default"""]				,     a__    )
														if key == "num_bytes":
																					assert is_apercent_close(a__				,     a__    )
														elif key == "splits":
																					assert list(a__    ) == list(a__    )
																					for split in result:
																												assert result[split].name == expected[split].name
																												assert result[split].num_examples == expected[split].num_examples
																												assert is_apercent_close(result[split].num_bytes				,     expected[split].num_bytes    )
														else:
																					result == expected











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       _UpperCamelCase       (a__				: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""": 6_5536,
							    """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       _UpperCamelCase       ():






							"""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""": 6_5536,
							    """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()




