

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  lowerCamelCase__ (					_a):
	if hor == 128:
		SCREAMING_SNAKE_CASE   :      str				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (32, 128, 256)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       ("UpResnetBlock1D", "UpResnetBlock1D")

	elif hor == 32:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (32, 64, 128, 256)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
	SCREAMING_SNAKE_CASE   :      Any				     =       torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
	SCREAMING_SNAKE_CASE   :      int				     =       model.state_dict()
	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "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": 65536,
	    "mid_block_type": "MidResTemporalBlock1D",
	    "act_fn": "mish",
	}
	SCREAMING_SNAKE_CASE   :      str				     =       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())}")
	SCREAMING_SNAKE_CASE   :      List[str]				     =       dict(zip(model.state_dict().keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      int				     =       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  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       {
	    "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": 65536,
	    "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",
	}

	SCREAMING_SNAKE_CASE   :      Dict				     =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
	SCREAMING_SNAKE_CASE   :      Dict				     =       model
	SCREAMING_SNAKE_CASE   :      List[str]				     =       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())}")

	SCREAMING_SNAKE_CASE   :      int				     =       dict(zip(state_dict.keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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 itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


a_						     =    datasets.utils.logging.get_logger(__name__)






@dataclass
class _UpperCamelCase							(							datasets.BuilderConfig   ):
			'''simple docstring'''







			lowerCamelCase__           =10000
			lowerCamelCase__           =None
			lowerCamelCase__           =None






class _UpperCamelCase							(							datasets.ArrowBasedBuilder   ):
			'''simple docstring'''







			lowerCamelCase__           =ParquetConfig
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				return datasets.DatasetInfo(features=self.config.features	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: List[Any]	)							->       Tuple:

				"""simple docstring"""

				if not self.config.data_files:
					raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a      ,      (str, list, tuple)	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       data_files
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [dl_manager.iter_files(a	) for file in files]
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={"files": files}	)]
				SCREAMING_SNAKE_CASE   :      str				     =       []
				for split_name, files in data_files.items():
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [dl_manager.iter_files(a	) for file in files]
					# Infer features is they are stoed in the arrow schema
					if self.info.features is None:
						for file in itertools.chain.from_iterable(a	):
							with open(a      ,      "rb"	) as f:
								SCREAMING_SNAKE_CASE   :      Dict				     =       datasets.Features.from_arrow_schema(pq.read_schema(a	)	)
							break
					splits.append(datasets.SplitGenerator(name=a      ,      gen_kwargs={"files": files}	)	)
				return splits
			def 							__UpperCamelCase (			self							: Dict      ,      a							: pa.Table	)							->       pa.Table:

				"""simple docstring"""

				if self.info.features is not None:
					# more expensive cast to support nested features with keys in a different order
					# allows str <-> int/float or str to Audio for example
					SCREAMING_SNAKE_CASE   :      str				     =       table_cast(a      ,      self.info.features.arrow_schema	)
				return pa_table






			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.info.features.arrow_schema if self.info.features is not None else None
				if self.info.features is not None and self.config.columns is not None:
					if sorted(field.name for field in schema	) != sorted(self.config.columns	):
						raise ValueError(
						    F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"	)
				for file_idx, file in enumerate(itertools.chain.from_iterable(a	)	):
					with open(a      ,      "rb"	) as f:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pq.ParquetFile(a	)
						try:
							for batch_idx, record_batch in enumerate(
							    parquet_file.iter_batches(batch_size=self.config.batch_size      ,      columns=self.config.columns	)	):
								SCREAMING_SNAKE_CASE   :      int				     =       pa.Table.from_batches([record_batch]	)
								# Uncomment for debugging (will print the Arrow table size and elements)
								# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
								# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
								yield F"{file_idx}_{batch_idx}", self._cast_table(a	)
						except ValueError as e:
							logger.error(F"Failed to read file '{file}' with error {type(a	)}: {e}"	)
							raise