


import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowerCAmelCase__			:      int								=pytest.mark.integration






@pytest.mark.parametrize('path',      ['paws', 'csv']				)
def 						a__			(				A__,      A__				):
			inspect_dataset(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					path + '.py'
			assert script_name in os.listdir(A__				)
			assert "__pycache__" not in os.listdir(A__				)






@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning'				)
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'				)
@pytest.mark.parametrize('path',      ['accuracy']				)
def 						a__			(				A__,      A__				):
			inspect_metric(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					path + '.py'
			assert script_name in os.listdir(A__				)
			assert "__pycache__" not in os.listdir(A__				)






@pytest.mark.parametrize(
    'path, config_name, expected_splits',      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ],      )
def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     int					=					get_dataset_config_info(A__,      config_name=A__				)
			assert info.config_name == config_name
			assert list(info.splits.keys()				) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception',      [
        ('paws', None, ValueError),
    ],      )
def 						a__			(				A__,      A__,      A__				):
			with pytest.raises(A__				):
						get_dataset_config_info(A__,      config_name=A__				)






@pytest.mark.parametrize(
    'path, expected',      [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ],      )
def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					get_dataset_config_names(A__				)
			assert expected in config_names






@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config',      [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ],      )
def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					get_dataset_infos(A__				)
			assert list(infos.keys()				) == expected_configs
			SCREAMING_SNAKE_CASE_		:     List[str]					=					expected_configs[0]
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_		:     Any					=					infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()				) == expected_splits_in_first_config






@pytest.mark.parametrize(
    'path, expected_config, expected_splits',      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ],      )
def 						a__			(				A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					get_dataset_infos(A__				)
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_		:     int					=					infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()				) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception',      [
        ('paws', None, ValueError),
    ],      )
def 						a__			(				A__,      A__,      A__				):
			with pytest.raises(A__				):
						get_dataset_split_names(A__,      config_name=A__				)






'''simple docstring'''


import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def    lowerCAmelCase       (		UpperCamelCase__					: Optional[int]     ,       UpperCamelCase__					: List[str]     ,       UpperCamelCase__					: Optional[Any]							):


	"""simple docstring"""
	# Initialise PyTorch model
	__UpperCAmelCase      					=      BertConfig.from_json_file(UpperCamelCase__							)
	print(f"""Building PyTorch model from configuration: {config}"""							)
	__UpperCAmelCase      					=      BertForPreTraining(UpperCamelCase__							)

	# Load weights from tf checkpoint
	load_tf_weights_in_bert(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""							)
	torch.save(model.state_dict()     ,       UpperCamelCase__							)


if __name__ == "__main__":
					__lowerCAmelCase						:							Tuple    							=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
					)
					parser.add_argument(
					    "--bert_config_file",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        "The config json file corresponding to the pre-trained BERT model. \n"
					        "This specifies the model architecture."
					    ),
					)
					parser.add_argument(
					    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)
					__lowerCAmelCase						:							Optional[Any]    							=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
