




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def        __UpperCAmelCase						(							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     List[Any]  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      BigBirdConfig.from_json_file(lowerCamelCase_  )
			print(F'Building PyTorch model from configuration: {config}'  )

			if is_trivia_qa:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      BigBirdForQuestionAnswering(lowerCamelCase_  )
			else:
						SCREAMING_SNAKE_CASE_   : Any					  =      BigBirdForPreTraining(lowerCamelCase_  )

			# Load weights from tf checkpoint
			load_tf_weights_in_big_bird(lowerCamelCase_  ,							lowerCamelCase_  ,							is_trivia_qa=lowerCamelCase_  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			model.save_pretrained(lowerCamelCase_  )


if __name__ == "__main__":
						UpperCamelCase__ :   Optional[int]						=							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(
						    '''--big_bird_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.'''
						)
						parser.add_argument(
						    '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
						)
						UpperCamelCase__ :   Dict						=							parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
						)









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,
)


UpperCamelCase__ :   List[Any]						=							pytest.mark.integration





@pytest.mark.parametrize('path'  ,							['paws', 'csv']  )
def        __UpperCAmelCase						(							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     int  )       ->  Any:







			"""simple docstring"""





			inspect_dataset(lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      path + '.py'
			assert script_name in os.listdir(lowerCamelCase_  )
			assert "__pycache__" not in os.listdir(lowerCamelCase_  )





@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        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Dict  )       ->  Optional[Any]:







			"""simple docstring"""





			inspect_metric(lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      path + '.py'
			assert script_name in os.listdir(lowerCamelCase_  )
			assert "__pycache__" not in os.listdir(lowerCamelCase_  )





@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        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      get_dataset_config_info(lowerCamelCase_  ,							config_name=lowerCamelCase_  )
			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        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     int  )       ->  List[Any]:







			"""simple docstring"""





			with pytest.raises(lowerCamelCase_  ):
						get_dataset_config_info(lowerCamelCase_  ,							config_name=lowerCamelCase_  )





@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        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     List[Any]  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      get_dataset_config_names(lowerCamelCase_  )
			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        __UpperCAmelCase						(							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Union[str, Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      get_dataset_infos(lowerCamelCase_  )
			assert list(infos.keys()  ) == expected_configs
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     str  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      get_dataset_infos(lowerCamelCase_  )
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_   : Tuple					  =      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        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  )       ->  int:







			"""simple docstring"""





			with pytest.raises(lowerCamelCase_  ):
						get_dataset_split_names(lowerCamelCase_  ,							config_name=lowerCamelCase_  )



