

import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def       A_     (							a    ,      a     ):

      """simple docstring"""



      assert isinstance(_UpperCAmelCase    ,      _UpperCAmelCase     )
      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       A_     (							a    ,      a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_ :     Tuple							=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE_ :     int							=   JsonDatasetReader(_UpperCAmelCase    ,      cache_dir=_UpperCAmelCase    ,      keep_in_memory=_UpperCAmelCase     ).read()
      _check_json_dataset(_UpperCAmelCase    ,      _UpperCAmelCase     )

@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_     (							a    ,      a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE_ :     int							=   features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE_ :     List[Any]							=   (
          Features({feature: Value(_UpperCAmelCase     ) for feature, dtype in features.items()}     ) if features is not None else None
      )
      SCREAMING_SNAKE_CASE_ :     str							=   JsonDatasetReader(_UpperCAmelCase    ,      features=_UpperCAmelCase    ,      cache_dir=_UpperCAmelCase     ).read()
      _check_json_dataset(_UpperCAmelCase    ,      _UpperCAmelCase     )

@pytest.mark.parametrize(
    'features'    ,      [
        None,
        {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
    ]    ,      )
def       A_     (							a    ,      a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     Dict							=   tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_ :     Tuple							=   {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
      SCREAMING_SNAKE_CASE_ :     List[Any]							=   features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE_ :     Dict							=   (
          Features({feature: Value(_UpperCAmelCase     ) for feature, dtype in features.items()}     ) if features is not None else None
      )
      SCREAMING_SNAKE_CASE_ :     List[str]							=   JsonDatasetReader(_UpperCAmelCase    ,      features=_UpperCAmelCase    ,      cache_dir=_UpperCAmelCase     ).read()
      assert isinstance(_UpperCAmelCase    ,      _UpperCAmelCase     )
      assert dataset.num_rows == 2
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_3", "col_1", "col_2"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype

def       A_     (							a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     int							=   {'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
      SCREAMING_SNAKE_CASE_ :     Tuple							=   features.copy()
      SCREAMING_SNAKE_CASE_ :     Tuple							=   (
          Features({feature: Value(_UpperCAmelCase     ) for feature, dtype in features.items()}     ) if features is not None else None
      )
      SCREAMING_SNAKE_CASE_ :     Optional[int]							=   tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_ :     int							=   JsonDatasetReader(_UpperCAmelCase    ,      features=_UpperCAmelCase    ,      cache_dir=_UpperCAmelCase     ).read()
      assert isinstance(_UpperCAmelCase    ,      _UpperCAmelCase     )
      assert dataset.num_rows == 2
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_2", "col_3", "col_1"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('split'    ,      [None, NamedSplit('train'     ), 'train', 'test']     )
def       A_     (							a    ,      a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_ :     int							=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   JsonDatasetReader(_UpperCAmelCase    ,      cache_dir=_UpperCAmelCase    ,      split=_UpperCAmelCase     ).read()
      _check_json_dataset(_UpperCAmelCase    ,      _UpperCAmelCase     )
      assert dataset.split == split if split else "train"

@pytest.mark.parametrize('path_type'    ,      [str, list]     )
def       A_     (							a    ,      a    ,      a     ):

      """simple docstring"""



      if issubclass(_UpperCAmelCase    ,      _UpperCAmelCase     ):
            SCREAMING_SNAKE_CASE_ :     int							=   jsonl_path
      elif issubclass(_UpperCAmelCase    ,      _UpperCAmelCase     ):
            SCREAMING_SNAKE_CASE_ :     Any							=   [jsonl_path]
      SCREAMING_SNAKE_CASE_ :     List[Any]							=   tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_ :     int							=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   JsonDatasetReader(_UpperCAmelCase    ,      cache_dir=_UpperCAmelCase     ).read()
      _check_json_dataset(_UpperCAmelCase    ,      _UpperCAmelCase     )

def       A_     (							a    ,      a    ,      a=("train",)     ):

      """simple docstring"""



      assert isinstance(_UpperCAmelCase    ,      _UpperCAmelCase     )
      for split in splits:
            SCREAMING_SNAKE_CASE_ :     Optional[int]							=   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       A_     (							a    ,      a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     Tuple							=   tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_ :     str							=   {'col_1': 'string', '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]							=   JsonDatasetReader({'train': jsonl_path}    ,      cache_dir=_UpperCAmelCase    ,      keep_in_memory=_UpperCAmelCase     ).read()
      _check_json_datasetdict(_UpperCAmelCase    ,      _UpperCAmelCase     )

@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_     (							a    ,      a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     List[Any]							=   tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_ :     Optional[int]							=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE_ :     List[str]							=   features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE_ :     List[str]							=   (
          Features({feature: Value(_UpperCAmelCase     ) for feature, dtype in features.items()}     ) if features is not None else None
      )
      SCREAMING_SNAKE_CASE_ :     Tuple							=   JsonDatasetReader({'train': jsonl_path}    ,      features=_UpperCAmelCase    ,      cache_dir=_UpperCAmelCase     ).read()
      _check_json_datasetdict(_UpperCAmelCase    ,      _UpperCAmelCase     )

@pytest.mark.parametrize('split'    ,      [None, NamedSplit('train'     ), 'train', 'test']     )
def       A_     (							a    ,      a    ,      a     ):

      """simple docstring"""



      if split:
            SCREAMING_SNAKE_CASE_ :     Dict							=   {split: jsonl_path}
      else:
            SCREAMING_SNAKE_CASE_ :     List[str]							=   'train'
            SCREAMING_SNAKE_CASE_ :     int							=   {'train': jsonl_path, 'test': jsonl_path}
      SCREAMING_SNAKE_CASE_ :     Any							=   tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_ :     Tuple							=   {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE_ :     List[Any]							=   JsonDatasetReader(_UpperCAmelCase    ,      cache_dir=_UpperCAmelCase     ).read()
      _check_json_datasetdict(_UpperCAmelCase    ,      _UpperCAmelCase    ,      splits=list(path.keys()     )     )
      assert all(dataset[split].split == split for split in path.keys()     )

def       A_     (							a     ):

      """simple docstring"""



      return json.load(_UpperCAmelCase     )

def       A_     (							a     ):

      """simple docstring"""



      return [json.loads(_UpperCAmelCase     ) for line in buffer]



class        _A						:

  @pytest.mark.parametrize('lines, load_json_function'       ,    [(True, load_json_lines), (False, load_json)]    )
  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        with io.BytesIO() as buffer:
              JsonDatasetWriter(snake_case_       ,    snake_case_       ,    lines=snake_case_    ).write()
              buffer.seek(0    )
              SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   load_json_function(snake_case_    )
        assert isinstance(snake_case_       ,    snake_case_    )
        assert isinstance(exported_content[0]       ,    snake_case_    )
        assert len(snake_case_    ) == 10

  @pytest.mark.parametrize(
      'orient, container, keys, len_at'       ,    [
          ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
          ('split', dict, {'columns', 'data'}, 'data'),
          ('index', dict, set('0123456789'    ), None),
          ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
          ('values', list, None, None),
          ('table', dict, {'schema', 'data'}, 'data'),
      ]       ,    )
  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        with io.BytesIO() as buffer:
              JsonDatasetWriter(snake_case_       ,    snake_case_       ,    lines=snake_case_       ,    orient=snake_case_    ).write()
              buffer.seek(0    )
              SCREAMING_SNAKE_CASE_ :     str							=   load_json(snake_case_    )
        assert isinstance(snake_case_       ,    snake_case_    )
        if keys:
              if container is dict:
                    assert exported_content.keys() == keys
              else:
                    assert exported_content[0].keys() == keys
        else:
              assert not hasattr(snake_case_       ,    'keys'    ) and not hasattr(exported_content[0]       ,    'keys'    )
        if len_at:
              assert len(exported_content[len_at]    ) == 10
        else:
              assert len(snake_case_    ) == 10

  @pytest.mark.parametrize('lines, load_json_function'       ,    [(True, load_json_lines), (False, load_json)]    )
  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        with io.BytesIO() as buffer:
              JsonDatasetWriter(snake_case_       ,    snake_case_       ,    lines=snake_case_       ,    num_proc=2    ).write()
              buffer.seek(0    )
              SCREAMING_SNAKE_CASE_ :     List[Any]							=   load_json_function(snake_case_    )
        assert isinstance(snake_case_       ,    snake_case_    )
        assert isinstance(exported_content[0]       ,    snake_case_    )
        assert len(snake_case_    ) == 10

  @pytest.mark.parametrize(
      'orient, container, keys, len_at'       ,    [
          ('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
          ('split', dict, {'columns', 'data'}, 'data'),
          ('index', dict, set('0123456789'    ), None),
          ('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
          ('values', list, None, None),
          ('table', dict, {'schema', 'data'}, 'data'),
      ]       ,    )
  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        with io.BytesIO() as buffer:
              JsonDatasetWriter(snake_case_       ,    snake_case_       ,    lines=snake_case_       ,    orient=snake_case_       ,    num_proc=2    ).write()
              buffer.seek(0    )
              SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   load_json(snake_case_    )
        assert isinstance(snake_case_       ,    snake_case_    )
        if keys:
              if container is dict:
                    assert exported_content.keys() == keys
              else:
                    assert exported_content[0].keys() == keys
        else:
              assert not hasattr(snake_case_       ,    'keys'    ) and not hasattr(exported_content[0]       ,    'keys'    )
        if len_at:
              assert len(exported_content[len_at]    ) == 10
        else:
              assert len(snake_case_    ) == 10

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        with pytest.raises(snake_case_    ):
              with io.BytesIO() as buffer:
                    JsonDatasetWriter(snake_case_       ,    snake_case_       ,    num_proc=0    )



  @pytest.mark.parametrize('compression, extension'       ,    [('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')]    )
  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Any							=   tmp_path_factory.mktemp('data'    ) / f"test.json.{extension}"
        SCREAMING_SNAKE_CASE_ :     Any							=   str(shared_datadir / f"test_file.json.{extension}"    )
        JsonDatasetWriter(snake_case_       ,    snake_case_       ,    compression=snake_case_    ).write()

        with fsspec.open(snake_case_       ,    'rb'       ,    compression='infer'    ) as f:
              SCREAMING_SNAKE_CASE_ :     Optional[int]							=   f.read()
        with fsspec.open(snake_case_       ,    'rb'       ,    compression='infer'    ) as f:
              SCREAMING_SNAKE_CASE_ :     str							=   f.read()
        assert exported_content == original_content













"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase_     :						Tuple						=					{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowerCamelCase_     :						int						=					AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)