


import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil







@pytest.fixture
def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Optional[int]										=       tmp_path / "file.csv"
							__snake_case		:      str										=       textwrap.dedent(
							    "\\n        header1,header2\n        1,2\n        10,20\n        "     )
							with open(__lowerCamelCase							,     "w"     ) as f:
														f.write(__lowerCamelCase     )
							return str(__lowerCamelCase     )







@pytest.fixture
def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      int										=       tmp_path / "malformed_file.csv"
							__snake_case		:      List[Any]										=       textwrap.dedent(
							    "\\n        header1,header2\n        1,2\n        10,20,\n        "     )
							with open(__lowerCamelCase							,     "w"     ) as f:
														f.write(__lowerCamelCase     )
							return str(__lowerCamelCase     )







@pytest.fixture
def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Optional[int]										=       tmp_path / "csv_with_image.csv"
							__snake_case		:      Optional[Any]										=       textwrap.dedent(
							    F'\\n        image\n        {image_file}\n        '     )
							with open(__lowerCamelCase							,     "w"     ) as f:
														f.write(__lowerCamelCase     )
							return str(__lowerCamelCase     )







@pytest.fixture
def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      str										=       tmp_path / "csv_with_label.csv"
							__snake_case		:      Optional[int]										=       textwrap.dedent(
							    "\\n        label\n        good\n        bad\n        good\n        "     )
							with open(__lowerCamelCase							,     "w"     ) as f:
														f.write(__lowerCamelCase     )
							return str(__lowerCamelCase     )







@pytest.fixture
def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      int										=       tmp_path / "csv_with_int_list.csv"
							__snake_case		:      Tuple										=       textwrap.dedent(
							    "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "     )
							with open(__lowerCamelCase							,     "w"     ) as f:
														f.write(__lowerCamelCase     )
							return str(__lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      int										=       Csv()
							__snake_case		:      Union[str, Any]										=       csv._generate_tables([[csv_file, malformed_csv_file]]     )
							with pytest.raises(__lowerCamelCase							,     match="Error tokenizing data"     ):
														for _ in generator:
																					pass
							assert any(
							    record.levelname == "ERROR"
							    and "Failed to read file" in record.message
							    and os.path.basename(__lowerCamelCase     ) in record.message
							    for record in caplog.records     )







@require_pil
def     lowerCAmelCase_					( __lowerCamelCase     ):
							with open(__lowerCamelCase							,     encoding="utf-8"     ) as f:
														__snake_case		:      int										=       f.read().splitlines()[1]
							__snake_case		:      Dict										=       Csv(encoding="utf-8"							,     features=Features({"image": Image()}     )     )
							__snake_case		:      List[str]										=       csv._generate_tables([[csv_file_with_image]]     )
							__snake_case		:      Any										=       pa.concat_tables([table for _, table in generator]     )
							assert pa_table.schema.field("image"     ).type == Image()()
							__snake_case		:      str										=       pa_table.to_pydict()["image"]
							assert generated_content == [{"path": image_file, "bytes": None}]







def     lowerCAmelCase_					( __lowerCamelCase     ):
							with open(__lowerCamelCase							,     encoding="utf-8"     ) as f:
														__snake_case		:      Optional[int]										=       f.read().splitlines()[1:]
							__snake_case		:      Any										=       Csv(encoding="utf-8"							,     features=Features({"label": ClassLabel(names=["good", "bad"]     )}     )     )
							__snake_case		:      str										=       csv._generate_tables([[csv_file_with_label]]     )
							__snake_case		:      List[Any]										=       pa.concat_tables([table for _, table in generator]     )
							assert pa_table.schema.field("label"     ).type == ClassLabel(names=["good", "bad"]     )()
							__snake_case		:      Any										=       pa_table.to_pydict()["label"]
							assert generated_content == [ClassLabel(names=["good", "bad"]     ).straint(__lowerCamelCase     ) for label in labels]







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Tuple										=       Csv(encoding="utf-8"							,     sep=","							,     converters={"int_list": lambda __lowerCamelCase     : [int(__lowerCamelCase     ) for i in x.split()]}     )
							__snake_case		:      List[str]										=       csv._generate_tables([[csv_file_with_int_list]]     )
							__snake_case		:      str										=       pa.concat_tables([table for _, table in generator]     )
							assert pa.types.is_list(pa_table.schema.field("int_list"     ).type     )
							__snake_case		:      str										=       pa_table.to_pydict()["int_list"]
							assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]





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,
						)

