





"""simple docstring"""




import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy





def 	a_    (							_lowercase	,						_lowercase       ):
						assert isinstance(_lowercase	,						_lowercase       )
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory'''	,						[False, True]       )
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[Any]         =   tmp_path / '''cache'''
						_UpperCamelCase      : Optional[int]         =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCamelCase      : Optional[int]         =   SqlDatasetReader(
												    '''dataset'''	,						'''sqlite:///''' + sqlite_path	,						cache_dir=_lowercase	,						keep_in_memory=_lowercase       ).read()
						_check_sql_dataset(_lowercase	,						_lowercase       )





@require_sqlalchemy
@pytest.mark.parametrize(
    '''features'''	,						[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]	,						)
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : str         =   tmp_path / '''cache'''
						_UpperCamelCase      : Union[str, Any]         =   {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						_UpperCamelCase      : str         =   features.copy() if features else default_expected_features
						_UpperCamelCase      : Any         =   (
						    Features({feature: Value(_lowercase       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCamelCase      : Tuple         =   SqlDatasetReader('''dataset'''	,						'''sqlite:///''' + sqlite_path	,						features=_lowercase	,						cache_dir=_lowercase       ).read()
						_check_sql_dataset(_lowercase	,						_lowercase       )





def 	a_    (							_lowercase       ):
						with contextlib.closing(sqlitea.connect(_lowercase       )       ) as con:
												_UpperCamelCase      : int         =   con.cursor()
												cur.execute('''SELECT * FROM dataset'''       )
												for row in cur:
																		yield row





@require_sqlalchemy
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : int         =   tmp_path / '''cache'''
						_UpperCamelCase      : Optional[Any]         =   os.path.join(_lowercase	,						'''tmp.sql'''       )
						_UpperCamelCase      : Optional[int]         =   SqlDatasetReader('''dataset'''	,						'''sqlite:///''' + sqlite_path	,						cache_dir=_lowercase       ).read()
						SqlDatasetWriter(_lowercase	,						'''dataset'''	,						'''sqlite:///''' + output_sqlite_path	,						num_proc=1       ).write()

						_UpperCamelCase      : List[str]         =   iter_sql_file(_lowercase       )
						_UpperCamelCase      : int         =   iter_sql_file(_lowercase       )

						for rowa, rowa in zip(_lowercase	,						_lowercase       ):
												assert rowa == rowa





@require_sqlalchemy
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[str]         =   tmp_path / '''cache'''
						_UpperCamelCase      : Optional[int]         =   os.path.join(_lowercase	,						'''tmp.sql'''       )
						_UpperCamelCase      : int         =   SqlDatasetReader('''dataset'''	,						'''sqlite:///''' + sqlite_path	,						cache_dir=_lowercase       ).read()
						SqlDatasetWriter(_lowercase	,						'''dataset'''	,						'''sqlite:///''' + output_sqlite_path	,						num_proc=2       ).write()

						_UpperCamelCase      : Tuple         =   iter_sql_file(_lowercase       )
						_UpperCamelCase      : Tuple         =   iter_sql_file(_lowercase       )

						for rowa, rowa in zip(_lowercase	,						_lowercase       ):
												assert rowa == rowa






@require_sqlalchemy
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Any         =   tmp_path / '''cache'''
						_UpperCamelCase      : Dict         =   os.path.join(_lowercase	,						'''tmp.sql'''       )
						_UpperCamelCase      : Any         =   SqlDatasetReader('''dataset'''	,						'''sqlite:///''' + sqlite_path	,						cache_dir=_lowercase       ).read()
						with pytest.raises(_lowercase       ):
												SqlDatasetWriter(_lowercase	,						'''dataset'''	,						'''sqlite:///''' + output_sqlite_path	,						num_proc=0       ).write()










"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						# Initialise PyTorch model
						_UpperCamelCase      : List[Any]         =   MobileBertConfig.from_json_file(_lowercase       )
						print(F"""Building PyTorch model from configuration: {config}"""       )
						_UpperCamelCase      : List[str]         =   MobileBertForPreTraining(_lowercase       )
						# Load weights from tf checkpoint
						_UpperCamelCase      : Union[str, Any]         =   load_tf_weights_in_mobilebert(_lowercase	,						_lowercase	,						_lowercase       )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
						torch.save(model.state_dict()	,						_lowercase       )


if __name__ == "__main__":
						UpperCamelCase_									=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(
						    """--mobilebert_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained MobileBERT 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."""
						)
						UpperCamelCase_									=parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



