


from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def    __snake_case (							self	:  str    )     ->   List[str]:
														return [
														    {"col_1": 3, "col_2": "a"},
														    {"col_1": 2, "col_2": "b"},
														    {"col_1": 1, "col_2": "c"},
														    {"col_1": 0, "col_2": "d"},
														]

							def    __snake_case (							self	:  Optional[Any]    )     ->   Union[str, Any]:
														__snake_case		:      List[str]										=       {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
														return Dataset.from_dict(lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]    )     ->   int:
														__snake_case		:      List[str]										=       self._create_example_records()
														__snake_case		:      Tuple										=       Dataset.from_list(lowerCamelCase    )
														self.assertListEqual(dset.column_names   ,     ["col_1", "col_2"]    )
														for i, r in enumerate(lowerCamelCase    ):
																					self.assertDictEqual(lowerCamelCase   ,     example_records[i]    )

							def    __snake_case (							self	:  int    )     ->   Tuple:
														__snake_case		:      Dict										=       self._create_example_records()
														__snake_case		:      Union[str, Any]										=       Dataset.from_list(lowerCamelCase    )
														__snake_case		:      Optional[int]										=       Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}    )
														self.assertEqual(dset.info   ,     dset_from_dict.info    )

							def    __snake_case (							self	:  Optional[Any]    )     ->   int:  # checks what happens with missing columns
														__snake_case		:      Optional[Any]										=       [{"col_1": 1}, {"col_2": "x"}]
														__snake_case		:      Tuple										=       Dataset.from_list(lowerCamelCase    )
														self.assertDictEqual(dset[0]   ,     {"col_1": 1}    )
														self.assertDictEqual(dset[1]   ,     {"col_1": None}    )  # NB: first record is used for columns

							def    __snake_case (							self	:  Dict    )     ->   int:  # checks if the type can be inferred from the second record
														__snake_case		:      Optional[Any]										=       [{"col_1": []}, {"col_1": [1, 2]}]
														__snake_case		:      Dict										=       Dataset.from_list(lowerCamelCase    )
														self.assertEqual(dset.info.features["col_1"]   ,     Sequence(Value("int64"    )    )    )






							def    __snake_case (							self	:  Any    )     ->   Union[str, Any]:
														__snake_case		:      int										=       Dataset.from_list([]    )
														self.assertEqual(len(lowerCamelCase    )   ,     0    )
														self.assertListEqual(dset.column_names   ,     []    )





import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_snake_case					:		Union[str, Any]      =    ["small", "medium", "large"]

_snake_case					:		List[Any]      =    "lm_head.decoder.weight"
_snake_case					:		Optional[Any]      =    "lm_head.weight"







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Tuple										=       torch.load(__lowerCamelCase     )
							__snake_case		:      Dict										=       d.pop(__lowerCamelCase     )
							os.makedirs(__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							torch.save(__lowerCamelCase							,     os.path.join(__lowerCamelCase							,     __lowerCamelCase     )     )


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

