"""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      UpperCAmelCase__			(							lowerCAmelCase__	:Union[str, Any]						)					->       Tuple:
      '''simple docstring'''


      if hor == 1_2_8:
            lowercase								=      ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
            lowercase								=      (3_2, 1_2_8, 2_5_6)
            lowercase								=      ("""UpResnetBlock1D""", """UpResnetBlock1D""")

      elif hor == 3_2:
            lowercase								=      ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
            lowercase								=      (3_2, 6_4, 1_2_8, 2_5_6)
            lowercase								=      ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
      lowercase								=      torch.load(f'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'						)
      lowercase								=      model.state_dict()
      lowercase								=      {
          """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""",
      }
      lowercase								=      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()						)}'						)
      lowercase								=      dict(zip(model.state_dict().keys()   ,						hf_value_function.state_dict().keys()						)						)
      for k, v in mapping.items():
            lowercase								=      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      UpperCAmelCase__			(							)					->       List[str]:
      '''simple docstring'''


      lowercase								=      {
          """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""",
      }

      lowercase								=      torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""						)
      lowercase								=      model
      lowercase								=      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()						)}'						)

      lowercase								=      dict(zip(state_dict.keys()   ,						hf_value_function.state_dict().keys()						)						)
      for k, v in mapping.items():
            lowercase								=      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(3_2)
       # unet(128)
       value_function()




"""simple docstring"""






import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
       import sqlitea

       import sqlalchemy





class 					_A		(			lowerCAmelCase      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = False						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            super().__init__(features=__lowerCAmelCase						,			cache_dir=__lowerCAmelCase						,			keep_in_memory=__lowerCAmelCase						,			**__lowerCAmelCase		)
            lowercase								=      Sql(
                cache_dir=__lowerCAmelCase						,			features=__lowerCAmelCase						,			sql=__lowerCAmelCase						,			con=__lowerCAmelCase						,			**__lowerCAmelCase						,			)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      None
            lowercase								=      None
            lowercase								=      None
            lowercase								=      None

            self.builder.download_and_prepare(
                download_config=__lowerCAmelCase						,			download_mode=__lowerCAmelCase						,			verification_mode=__lowerCAmelCase						,			base_path=__lowerCAmelCase						,			)

            # Build dataset for splits
            lowercase								=      self.builder.as_dataset(
                split="""train"""						,			verification_mode=__lowerCAmelCase						,			in_memory=self.keep_in_memory		)
            return dataset






class 					_A		:

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if num_proc is not None and num_proc <= 0:
                  raise ValueError(f'num_proc {num_proc} must be an integer > 0.'		)

            lowercase								=      dataset
            lowercase								=      name
            lowercase								=      con
            lowercase								=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
            lowercase								=      num_proc
            lowercase								=      to_sql_kwargs

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.to_sql_kwargs.pop("""sql"""						,			__lowerCAmelCase		)
            lowercase								=      self.to_sql_kwargs.pop("""con"""						,			__lowerCAmelCase		)
            lowercase								=      self.to_sql_kwargs.pop("""index"""						,			__lowerCAmelCase		)

            lowercase								=      self._write(index=__lowerCAmelCase						,			**self.to_sql_kwargs		)
            return written

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase				,				lowercase				,				lowercase								=      args
            lowercase								=      {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
            lowercase								=      query_table(
                table=self.dataset.data						,			key=slice(__lowerCAmelCase						,			offset + self.batch_size		)						,			indices=self.dataset._indices						,			)
            lowercase								=      batch.to_pandas()
            lowercase								=      df.to_sql(self.name						,			self.con						,			index=__lowerCAmelCase						,			**__lowerCAmelCase		)
            return num_rows or len(__lowerCAmelCase		)







      def 							A__			(		self						,			__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      0

            if self.num_proc is None or self.num_proc == 1:
                  for offset in logging.tqdm(
                      range(0						,			len(self.dataset		)						,			self.batch_size		)						,			unit="""ba"""						,			disable=not logging.is_progress_bar_enabled()						,			desc="""Creating SQL from Arrow format"""						,			):
                        written += self._batch_sql((offset, index, to_sql_kwargs)		)
            else:
                  lowercase				,				lowercase								=      len(self.dataset		), self.batch_size
                  with multiprocessing.Pool(self.num_proc		) as pool:
                        for num_rows in logging.tqdm(
                            pool.imap(
                                self._batch_sql						,			[(offset, index, to_sql_kwargs) for offset in range(0						,			__lowerCAmelCase						,			__lowerCAmelCase		)]						,			)						,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,			unit="""ba"""						,			disable=not logging.is_progress_bar_enabled()						,			desc="""Creating SQL from Arrow format"""						,			):
                              written += num_rows

            return written



