

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        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =tmp_path / 'file.csv'
			__a        =textwrap.dedent(
			    '\\n        header1,header2\n        1,2\n        10,20\n        '	)
			with open(_snake_case       ,     'w'	) as f:
						f.write(_snake_case	)
			return str(_snake_case	)






@pytest.fixture
def        UpperCamelCase_(							_snake_case   :  Optional[int]	):




			"""simple docstring"""






			__a        =tmp_path / 'malformed_file.csv'
			__a        =textwrap.dedent(
			    '\\n        header1,header2\n        1,2\n        10,20,\n        '	)
			with open(_snake_case       ,     'w'	) as f:
						f.write(_snake_case	)
			return str(_snake_case	)






@pytest.fixture
def        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =tmp_path / 'csv_with_image.csv'
			__a        =textwrap.dedent(
			    F'\\n        image\n        {image_file}\n        '	)
			with open(_snake_case       ,     'w'	) as f:
						f.write(_snake_case	)
			return str(_snake_case	)






@pytest.fixture
def        UpperCamelCase_(							_snake_case   :  List[Any]	):




			"""simple docstring"""






			__a        =tmp_path / 'csv_with_label.csv'
			__a        =textwrap.dedent(
			    '\\n        label\n        good\n        bad\n        good\n        '	)
			with open(_snake_case       ,     'w'	) as f:
						f.write(_snake_case	)
			return str(_snake_case	)






@pytest.fixture
def        UpperCamelCase_(							_snake_case   :  Optional[int]	):




			"""simple docstring"""






			__a        =tmp_path / 'csv_with_int_list.csv'
			__a        =textwrap.dedent(
			    '\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        '	)
			with open(_snake_case       ,     'w'	) as f:
						f.write(_snake_case	)
			return str(_snake_case	)






def        UpperCamelCase_(							_snake_case   :  Any       ,     _snake_case   :  Any       ,     _snake_case   :  Any	):




			"""simple docstring"""






			__a        =Csv()
			__a        =csv._generate_tables([[csv_file, malformed_csv_file]]	)
			with pytest.raises(_snake_case       ,     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(_snake_case	) in record.message
			    for record in caplog.records	)






@require_pil
def        UpperCamelCase_(							_snake_case   :  Optional[int]	):




			"""simple docstring"""






			with open(_snake_case       ,     encoding='utf-8'	) as f:
						__a        =f.read().splitlines()[1]
			__a        =Csv(encoding='utf-8'       ,     features=Features({'image': Image()}	)	)
			__a        =csv._generate_tables([[csv_file_with_image]]	)
			__a        =pa.concat_tables([table for _, table in generator]	)
			assert pa_table.schema.field('image'	).type == Image()()
			__a        =pa_table.to_pydict()['image']
			assert generated_content == [{"path": image_file, "bytes": None}]






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			with open(_snake_case       ,     encoding='utf-8'	) as f:
						__a        =f.read().splitlines()[1:]
			__a        =Csv(encoding='utf-8'       ,     features=Features({'label': ClassLabel(names=['good', 'bad']	)}	)	)
			__a        =csv._generate_tables([[csv_file_with_label]]	)
			__a        =pa.concat_tables([table for _, table in generator]	)
			assert pa_table.schema.field('label'	).type == ClassLabel(names=['good', 'bad']	)()
			__a        =pa_table.to_pydict()['label']
			assert generated_content == [ClassLabel(names=['good', 'bad']	).straint(_snake_case	) for label in labels]






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






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









import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  str	):




			"""simple docstring"""






			if openai_config_file == "":
						__a        =OpenAIGPTConfig()
			else:
						__a        =OpenAIGPTConfig.from_json_file(_snake_case	)
			__a        =OpenAIGPTModel(_snake_case	)

			# Load weights from numpy
			load_tf_weights_in_openai_gpt(_snake_case       ,     _snake_case       ,     _snake_case	)

			# Save pytorch-model
			__a        =pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			__a        =pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F'Save PyTorch model to {pytorch_weights_dump_path}'	)
			torch.save(model.state_dict()       ,     _snake_case	)
			print(F'Save configuration file to {pytorch_config_dump_path}'	)
			with open(_snake_case       ,     'w'       ,     encoding='utf-8'	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
						_lowerCAmelCase					:			Tuple  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--openai_checkpoint_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the TensorFlow checkpoint path.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--openai_config_file",
						    default="",
						    type=str,
						    help=(
						        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
						        "This specifies the model architecture."
						    ),
						)
						_lowerCAmelCase					:			int  				=			parser.parse_args()
						convert_openai_checkpoint_to_pytorch(
						    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
						)






