



"""simple docstring"""






import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE



class     A__     (						A_							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			''''''
	SCREAMING_SNAKE_CASE					   =			(
	    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
	)
	SCREAMING_SNAKE_CASE					   =			None  # compression type in fsspec. ex: "gzip"
	SCREAMING_SNAKE_CASE					   =			None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz

	def __init__(  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	List[Any] = "" ,    _SCREAMING_SNAKE_CASE:	Optional[Any] = None ,    _SCREAMING_SNAKE_CASE:	List[Any] = None ,    **_SCREAMING_SNAKE_CASE:	Tuple)       -> Any:

				"""simple docstring"""

				super().__init__(self ,    **_SCREAMING_SNAKE_CASE)
				# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
				__lowerCAmelCase   :						int				= fsspec.open(
				    _SCREAMING_SNAKE_CASE ,    mode="rb" ,    protocol=_SCREAMING_SNAKE_CASE ,    compression=self.compression ,    client_kwargs={
				        "requote_redirect_url": False,  # see https://github.com/huggingface/datasets/pull/5459
				        "trust_env": True,  # Enable reading proxy env variables.
				        **(target_options or {}).pop("client_kwargs" ,    {}),  # To avoid issues if it was already passed.
				    } ,    **(target_options or {}) ,    )
				__lowerCAmelCase   :						Optional[Any]				= os.path.basename(self.file.path.split("::")[0])
				__lowerCAmelCase   :						Dict				= (
				    self.compressed_name[: self.compressed_name.rindex(".")]
				    if """.""" in self.compressed_name
				    else self.compressed_name
				)
				__lowerCAmelCase   :						List[Any]				= None

	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	Tuple ,    _SCREAMING_SNAKE_CASE:	Union[str, Any])       -> Optional[int]:

				"""simple docstring"""

				return super()._strip_protocol(_SCREAMING_SNAKE_CASE).lstrip("/")

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

				"""simple docstring"""

				if self.dir_cache is None:
							__lowerCAmelCase   :						Optional[int]				= {**self.file.fs.info(self.file.path), """name""": self.uncompressed_name}
							__lowerCAmelCase   :						Optional[Any]				= {f["""name"""]: f}

	def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	Tuple)       -> Optional[int]:

				"""simple docstring"""

				return self.file.open().read()







	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	int = "rb" ,    _SCREAMING_SNAKE_CASE:	Tuple=None ,    _SCREAMING_SNAKE_CASE:	Tuple=True ,    _SCREAMING_SNAKE_CASE:	List[str]=None ,    **_SCREAMING_SNAKE_CASE:	Optional[int] ,    )       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= self._strip_protocol(_SCREAMING_SNAKE_CASE)
				if mode != "rb":
							raise ValueError(F"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'""")
				return self.file.open()






class     A__     (						A_							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'''bz2'''
	SCREAMING_SNAKE_CASE					   =			'''bz2'''
	SCREAMING_SNAKE_CASE					   =			'''.bz2'''






class     A__     (						A_							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'''gzip'''
	SCREAMING_SNAKE_CASE					   =			'''gzip'''
	SCREAMING_SNAKE_CASE					   =			'''.gz'''






class     A__     (						A_							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'''lz4'''
	SCREAMING_SNAKE_CASE					   =			'''lz4'''
	SCREAMING_SNAKE_CASE					   =			'''.lz4'''






class     A__     (						A_							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'''xz'''
	SCREAMING_SNAKE_CASE					   =			'''xz'''
	SCREAMING_SNAKE_CASE					   =			'''.xz'''






class     A__     (						A_							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'''zstd'''
	SCREAMING_SNAKE_CASE					   =			'''zstd'''
	SCREAMING_SNAKE_CASE					   =			'''.zst'''

	def __init__(  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	Tuple = "rb" ,    _SCREAMING_SNAKE_CASE:	int = None ,    _SCREAMING_SNAKE_CASE:	Dict = None ,    _SCREAMING_SNAKE_CASE:	List[Any] = DEFAULT_BLOCK_SIZE ,    **_SCREAMING_SNAKE_CASE:	List[str] ,    )       -> Optional[Any]:

				"""simple docstring"""

				super().__init__(
				    fo=_SCREAMING_SNAKE_CASE ,    mode=_SCREAMING_SNAKE_CASE ,    target_protocol=_SCREAMING_SNAKE_CASE ,    target_options=_SCREAMING_SNAKE_CASE ,    block_size=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE ,    )
				# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
				#
				# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
				#     out.close = close
				# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
				#
				# see https://github.com/intake/filesystem_spec/issues/725
				__lowerCAmelCase   :						Optional[Any]				= self.file.__enter__



				class     A__     :




					'''simple docstring'''





					def __init__(  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	List[str])       -> str:

								"""simple docstring"""

								__lowerCAmelCase   :						List[Any]				= file_

					def __enter__(  self:	Dict)       -> Any:

								"""simple docstring"""

								self._file.__enter__()
								return self

					def __exit__(  self:	int ,    *_SCREAMING_SNAKE_CASE:	int ,    **_SCREAMING_SNAKE_CASE:	Tuple)       -> Union[str, Any]:

								"""simple docstring"""

								self._file.__exit__(*_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

					def __iter__(  self:	Optional[int])       -> List[str]:

								"""simple docstring"""

								return iter(self._file)

					def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Any:

								"""simple docstring"""

								return next(self._file)







					def __getattr__(  self:	Any ,    _SCREAMING_SNAKE_CASE:	List[Any])       -> Optional[int]:

								"""simple docstring"""

								return getattr(self._file ,    _SCREAMING_SNAKE_CASE)

				def fixed_enter(*_SCREAMING_SNAKE_CASE:	List[str] ,    **_SCREAMING_SNAKE_CASE:	Tuple):
							return WrappedFile(_enter(*_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE))

				__lowerCAmelCase   :						Optional[Any]				= fixed_enter




"""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     _lowercase       (						__snake_case			)		->     int:
			if hor == 128:
						__lowerCAmelCase   :						str				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						int				= (32, 128, 256)
						__lowerCAmelCase   :						Optional[Any]				= ("UpResnetBlock1D", "UpResnetBlock1D")

			elif hor == 32:
						__lowerCAmelCase   :						List[str]				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						Optional[Any]				= (32, 64, 128, 256)
						__lowerCAmelCase   :						Any				= ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
			__lowerCAmelCase   :						Union[str, Any]				= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""			)
			__lowerCAmelCase   :						List[Any]				= model.state_dict()
			__lowerCAmelCase   :						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": 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",
			}
			__lowerCAmelCase   :						Dict				= UNetaDModel(**__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				= dict(zip(model.state_dict().keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						int				= state_dict.pop(__snake_case			)
			hf_value_function.load_state_dict(__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(__snake_case      ,__snake_case			)


def     _lowercase       (						)		->     List[str]:
			__lowerCAmelCase   :						Union[str, Any]				= {
			    "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",
			}

			__lowerCAmelCase   :						int				= torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"			)
			__lowerCAmelCase   :						Any				= model
			__lowerCAmelCase   :						Optional[int]				= UNetaDModel(**__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   :						Any				= dict(zip(state_dict.keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						Union[str, Any]				= state_dict.pop(__snake_case			)

			hf_value_function.load_state_dict(__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(__snake_case      ,__snake_case			)


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