





"""simple docstring"""





import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int] ,      lowercase_   :							Tuple		):

 '''simple docstring'''






 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




@pytest.mark.parametrize('''keep_in_memory''' ,      [False, True]		)
def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							int ,      lowercase_   :							List[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			List[Any]												=       tmp_path / '''cache'''
 __SCREAMING_SNAKE_CASE :			Dict												=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  __SCREAMING_SNAKE_CASE :			Tuple												=       CsvDatasetReader(lowercase_ ,      cache_dir=lowercase_ ,      keep_in_memory=lowercase_		).read()
 _check_csv_dataset(lowercase_ ,      lowercase_		)




@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 		lowerCAmelCase_	(  lowercase_   :							Optional[int] ,      lowercase_   :							Any ,      lowercase_   :							List[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			str												=       tmp_path / '''cache'''
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 __SCREAMING_SNAKE_CASE :			Any												=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 __SCREAMING_SNAKE_CASE :			List[str]												=       features.copy() if features else default_expected_features
 __SCREAMING_SNAKE_CASE :			Any												=       (
     Features({feature: Value(lowercase_		) for feature, dtype in features.items()}		) if features is not None else None
 )
 __SCREAMING_SNAKE_CASE :			Dict												=       CsvDatasetReader(lowercase_ ,      features=lowercase_ ,      cache_dir=lowercase_		).read()
 _check_csv_dataset(lowercase_ ,      lowercase_		)




@pytest.mark.parametrize('''split''' ,      [None, NamedSplit('''train'''		), '''train''', '''test''']		)
def 		lowerCAmelCase_	(  lowercase_   :							Tuple ,      lowercase_   :							int ,      lowercase_   :							List[str]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       tmp_path / '''cache'''
 __SCREAMING_SNAKE_CASE :			Tuple												=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 __SCREAMING_SNAKE_CASE :			List[Any]												=       CsvDatasetReader(lowercase_ ,      cache_dir=lowercase_ ,      split=lowercase_		).read()
 _check_csv_dataset(lowercase_ ,      lowercase_		)
 assert dataset.split == split if split else "train"




@pytest.mark.parametrize('''path_type''' ,      [str, list]		)
def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							str ,      lowercase_   :							str		):

 '''simple docstring'''






 if issubclass(lowercase_ ,      lowercase_		):
  __SCREAMING_SNAKE_CASE :			List[str]												=       csv_path
 elif issubclass(lowercase_ ,      lowercase_		):
  __SCREAMING_SNAKE_CASE :			Tuple												=       [csv_path]
 __SCREAMING_SNAKE_CASE :			Dict												=       tmp_path / '''cache'''
 __SCREAMING_SNAKE_CASE :			str												=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       CsvDatasetReader(lowercase_ ,      cache_dir=lowercase_		).read()
 _check_csv_dataset(lowercase_ ,      lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							Tuple ,      lowercase_   :							Tuple=("train",)		):

 '''simple docstring'''






 assert isinstance(lowercase_ ,      lowercase_		)
 for split in splits:
  __SCREAMING_SNAKE_CASE :			List[str]												=       dataset_dict[split]
  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




@pytest.mark.parametrize('''keep_in_memory''' ,      [False, True]		)
def 		lowerCAmelCase_	(  lowercase_   :							Tuple ,      lowercase_   :							Optional[Any] ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       tmp_path / '''cache'''
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       CsvDatasetReader({'''train''': csv_path} ,      cache_dir=lowercase_ ,      keep_in_memory=lowercase_		).read()
 _check_csv_datasetdict(lowercase_ ,      lowercase_		)




@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 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							str ,      lowercase_   :							List[str]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       tmp_path / '''cache'''
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 __SCREAMING_SNAKE_CASE :			Any												=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 __SCREAMING_SNAKE_CASE :			str												=       features.copy() if features else default_expected_features
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       (
     Features({feature: Value(lowercase_		) for feature, dtype in features.items()}		) if features is not None else None
 )
 __SCREAMING_SNAKE_CASE :			List[Any]												=       CsvDatasetReader({'''train''': csv_path} ,      features=lowercase_ ,      cache_dir=lowercase_		).read()
 _check_csv_datasetdict(lowercase_ ,      lowercase_		)




@pytest.mark.parametrize('''split''' ,      [None, NamedSplit('''train'''		), '''train''', '''test''']		)
def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							Any ,      lowercase_   :							Optional[int]		):

 '''simple docstring'''






 if split:
  __SCREAMING_SNAKE_CASE :			Any												=       {split: csv_path}
 else:
  __SCREAMING_SNAKE_CASE :			str												=       {'''train''': csv_path, '''test''': csv_path}
 __SCREAMING_SNAKE_CASE :			List[str]												=       tmp_path / '''cache'''
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
 __SCREAMING_SNAKE_CASE :			Any												=       CsvDatasetReader(lowercase_ ,      cache_dir=lowercase_		).read()
 _check_csv_datasetdict(lowercase_ ,      lowercase_ ,      splits=list(path.keys()		)		)
 assert all(dataset[split].split == split for split in path.keys()		)




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any]		):

 '''simple docstring'''






 with open(lowercase_ ,      encoding='''utf-8'''		) as csvfile:
  yield from csv.reader(lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							int		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			str												=       tmp_path / '''cache'''
 __SCREAMING_SNAKE_CASE :			List[Any]												=       os.path.join(lowercase_ ,      '''tmp.csv'''		)
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       CsvDatasetReader({'''train''': csv_path} ,      cache_dir=lowercase_		).read()
 CsvDatasetWriter(dataset['''train'''] ,      lowercase_ ,      num_proc=1		).write()

 __SCREAMING_SNAKE_CASE :			Tuple												=       iter_csv_file(lowercase_		)
 __SCREAMING_SNAKE_CASE :			int												=       iter_csv_file(lowercase_		)

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




def 		lowerCAmelCase_	(  lowercase_   :							Union[str, Any] ,      lowercase_   :							List[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       tmp_path / '''cache'''
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       os.path.join(lowercase_ ,      '''tmp.csv'''		)
 __SCREAMING_SNAKE_CASE :			str												=       CsvDatasetReader({'''train''': csv_path} ,      cache_dir=lowercase_		).read()
 CsvDatasetWriter(dataset['''train'''] ,      lowercase_ ,      num_proc=2		).write()

 __SCREAMING_SNAKE_CASE :			List[str]												=       iter_csv_file(lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       iter_csv_file(lowercase_		)

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






def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							List[str]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       tmp_path / '''cache'''
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       os.path.join(lowercase_ ,      '''tmp.csv'''		)
 __SCREAMING_SNAKE_CASE :			Tuple												=       CsvDatasetReader({'''train''': csv_path} ,      cache_dir=lowercase_		).read()
 with pytest.raises(lowercase_		):
  CsvDatasetWriter(dataset['''train'''] ,      lowercase_ ,      num_proc=0		)








"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

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


if __name__ == "__main__":
    _lowerCamelCase       =	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(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

