

'''simple docstring'''





import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast


@dataclass
class  UpperCAmelCase (							datasets.BuilderConfig    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					None


class  UpperCAmelCase (							datasets.ArrowBasedBuilder    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					PandasConfig
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							return datasets.DatasetInfo(features=self.config.features		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     Dict:






							'''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}'''		)
							lowerCamelCase_  				=      dl_manager.download_and_extract(self.config.data_files		)
							if isinstance(SCREAMING_SNAKE_CASE_   ,			(str, list, tuple)		):
												lowerCamelCase_  				=      data_files
												if isinstance(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		):
																	lowerCamelCase_  				=      [files]
												# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
												lowerCamelCase_  				=      [dl_manager.iter_files(SCREAMING_SNAKE_CASE_		) for file in files]
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,			gen_kwargs={'files': files}		)]
							lowerCamelCase_  				=      []
							for split_name, files in data_files.items():
												if isinstance(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		):
																	lowerCamelCase_  				=      [files]
												# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
												lowerCamelCase_  				=      [dl_manager.iter_files(SCREAMING_SNAKE_CASE_		) for file in files]
												splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE_   ,			gen_kwargs={'files': files}		)		)
							return splits
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     pa.Table:






							'''simple docstring'''



							if self.config.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
												lowerCamelCase_  				=      table_cast(SCREAMING_SNAKE_CASE_   ,			self.config.features.arrow_schema		)
							return pa_table






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



							for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_		)		):
												with open(SCREAMING_SNAKE_CASE_   ,			'rb'		) as f:
																	lowerCamelCase_  				=      pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE_		)		)
																	yield i, self._cast_table(SCREAMING_SNAKE_CASE_		)








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    (							__UpperCamelCase       )    ->   Union[str, Any]:
			if hor == 128:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D')

			elif hor == 32:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 64, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
			A__	  =     torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''       )
			A__	  =     model.state_dict()
			A__	  =     {
			    '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',
			}
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )
			A__	  =     dict(zip(model.state_dict().keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )
			hf_value_function.load_state_dict(__UpperCamelCase       )

			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(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							)    ->   List[str]:
			A__	  =     {
			    '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',
			}

			A__	  =     torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'       )
			A__	  =     model
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )

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

			hf_value_function.load_state_dict(__UpperCamelCase       )

			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(__UpperCamelCase			,						__UpperCamelCase       )


if __name__ == "__main__":
							unet(3_2)
							# unet(128)
							value_function()




