


'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


__snake_case											=					['''small''', '''medium''', '''large''']

__snake_case											=					'''lm_head.decoder.weight'''
__snake_case											=					'''lm_head.weight'''


def       a				(      __a			, __a       )  ->     Optional[int]:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			torch.load(__a       )
					UpperCamelCase__       :str							 =			d.pop(__a       )
					os.makedirs(__a			, exist_ok=__a       )
					torch.save(__a			, os.path.join(__a			, __a       )       )


if __name__ == "__main__":
		__snake_case											=					argparse.ArgumentParser()
		parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
		__snake_case											=					parser.parse_args()
		for MODEL in DIALOGPT_MODELS:
				__snake_case											=					os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
				__snake_case											=					F"""./DialoGPT-{MODEL}"""
				convert_dialogpt_checkpoint(
				    checkpoint_path,
				    pytorch_dump_folder_path,
				)



'''simple docstring'''
from math import ceil


def       a				(      __a			, __a       )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			list(range(0			, __a       )       )

					UpperCamelCase__       :Optional[int]							 =			[item for sublist in list(device_map.values()       ) for item in sublist]

					# Duplicate check
					UpperCamelCase__       :Optional[int]							 =			[]
					for i in device_map_blocks:
										if device_map_blocks.count(__a       ) > 1 and i not in duplicate_blocks:
															duplicate_blocks.append(__a       )
    # Missing blocks
					UpperCamelCase__       :List[str]							 =			[i for i in blocks if i not in device_map_blocks]
					UpperCamelCase__       :Optional[Any]							 =			[i for i in device_map_blocks if i not in blocks]

					if len(__a       ) != 0:
										raise ValueError(
										    '''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'''
										    ''' These attention blocks were specified more than once: ''' + str(__a       )       )
					if len(__a       ) != 0:
										raise ValueError(
										    '''There are attention blocks for this model that are not specified in the device_map. Add these attention '''
										    '''blocks to a device on the device_map: ''' + str(__a       )       )
					if len(__a       ) != 0:
										raise ValueError(
										    '''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
										    + str(__a       )       )







def       a				(      __a			, __a       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			list(range(__a       )       )
					UpperCamelCase__       :Any							 =			int(ceil(n_layers / len(__a       )       )       )
					UpperCamelCase__       :List[Any]							 =			[layers[i : i + n_blocks] for i in range(0			, __a			, __a       )]

					return dict(zip(__a			, __a       )       )