


import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = None,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   Tuple:




           """simple docstring"""

           super().__init__(
               lowerCamelCase,      split=lowerCamelCase,      features=lowerCamelCase,      cache_dir=lowerCamelCase,      keep_in_memory=lowerCamelCase,      streaming=lowerCamelCase,      num_proc=lowerCamelCase,      **lowerCamelCase,      )
           _lowercase       :							Optional[Any]      		=		field
           _lowercase       :							Dict      		=		path_or_paths if isinstance(lowerCamelCase,      lowerCamelCase) else {self.split: path_or_paths}
           _lowercase       :							Tuple      		=		Json(
               cache_dir=lowerCamelCase,      data_files=lowerCamelCase,      features=lowerCamelCase,      field=lowerCamelCase,      **lowerCamelCase,      )




       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           if self.streaming:
               _lowercase       :							str      		=		self.builder.as_streaming_dataset(split=self.split)
           # Build regular (map-style) dataset
           else:
               _lowercase       :							Any      		=		None
               _lowercase       :							List[str]      		=		None
               _lowercase       :							Dict      		=		None
               _lowercase       :							Tuple      		=		None

               self.builder.download_and_prepare(
                   download_config=lowerCamelCase,      download_mode=lowerCamelCase,      verification_mode=lowerCamelCase,      base_path=lowerCamelCase,      num_proc=self.num_proc,      )
               _lowercase       :							Tuple      		=		self.builder.as_dataset(
                   split=self.split,      verification_mode=lowerCamelCase,      in_memory=self.keep_in_memory)
           return dataset






class _lowerCamelCase:
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   Optional[int]:




           """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       :							List[Any]      		=		dataset
           _lowercase       :							int      		=		path_or_buf
           _lowercase       :							Optional[Any]      		=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           _lowercase       :							Dict      		=		num_proc
           _lowercase       :							Optional[Any]      		=		'utf-8'
           _lowercase       :							Optional[Any]      		=		to_json_kwargs
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							int      		=		self.to_json_kwargs.pop('path_or_buf',      lowerCamelCase)
           _lowercase       :							List[Any]      		=		self.to_json_kwargs.pop('orient',      'records')
           _lowercase       :							Dict      		=		self.to_json_kwargs.pop('lines',      True if orient == 'records' else False)
           _lowercase       :							Dict      		=		self.to_json_kwargs.pop('index',      False if orient in ['split', 'table'] else True)
           _lowercase       :							List[Any]      		=		self.to_json_kwargs.pop('compression',      lowerCamelCase)

           if compression not in [None, "infer", "gzip", "bz2", "xz"]:
               raise NotImplementedError(F'''`datasets` currently does not support {compression} compression''')

           if isinstance(self.path_or_buf,      (str, bytes, os.PathLike)):
               with fsspec.open(self.path_or_buf,      'wb',      compression=lowerCamelCase) as buffer:
                   _lowercase       :							Dict      		=		self._write(file_obj=lowerCamelCase,      orient=lowerCamelCase,      lines=lowerCamelCase,      index=lowerCamelCase,      **self.to_json_kwargs)
           else:
               if compression:
                   raise NotImplementedError(
                       F'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
                       ' was passed. Please provide a local path instead.')
               _lowercase       :							Any      		=		self._write(
                   file_obj=self.path_or_buf,      orient=lowerCamelCase,      lines=lowerCamelCase,      index=lowerCamelCase,      **self.to_json_kwargs)
           return written
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Any:




           """simple docstring"""

           _lowercase		,				_lowercase		,				_lowercase		,				_lowercase		,				_lowercase       :							str      		=		args

           _lowercase       :							Optional[Any]      		=		query_table(
               table=self.dataset.data,      key=slice(lowerCamelCase,      offset + self.batch_size),      indices=self.dataset._indices,      )
           _lowercase       :							List[Any]      		=		batch.to_pandas().to_json(
               path_or_buf=lowerCamelCase,      orient=lowerCamelCase,      lines=lowerCamelCase,      index=lowerCamelCase,      **lowerCamelCase)
           if not json_str.endswith('\n'):
               json_str += "\n"
           return json_str.encode(self.encoding)




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      **lowerCamelCase,      )       ->   int:




           """simple docstring"""

           _lowercase       :							Any      		=		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 json from Arrow format',      ):
                   _lowercase       :							Dict      		=		self._batch_json((offset, orient, lines, index, to_json_kwargs))
                   written += file_obj.write(lowerCamelCase)
           else:
               _lowercase		,				_lowercase       :							Dict      		=		len(self.dataset), self.batch_size
               with multiprocessing.Pool(self.num_proc) as pool:
                   for json_str in logging.tqdm(
                       pool.imap(
                           self._batch_json,      [(offset, orient, lines, index, to_json_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 json from Arrow format',      ):
                       written += file_obj.write(lowerCamelCase)

           return written








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      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					if hor == 1_28:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 64, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					__lowerCAmelCase				   =       torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""     )
					__lowerCAmelCase				   =       model.state_dict()
					__lowerCAmelCase				   =       {
					    "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_55_36,
					    "mid_block_type": "MidResTemporalBlock1D",
					    "act_fn": "mish",
					}
					__lowerCAmelCase				   =       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()     )}"""     )
					__lowerCAmelCase				   =       dict(zip(model.state_dict().keys()      ,      hf_value_function.state_dict().keys()     )     )
					for k, v in mapping.items():
										__lowerCAmelCase				   =       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      (						):
					__lowerCAmelCase				   =       {
					    "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, 1_28, 2_56),
					    "layers_per_block": 1,
					    "downsample_each_block": True,
					    "sample_size": 6_55_36,
					    "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",
					}

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

					__lowerCAmelCase				   =       dict(zip(state_dict.keys()      ,      hf_value_function.state_dict().keys()     )     )
					for k, v in mapping.items():
										__lowerCAmelCase				   =       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()




