code: 
  
  
  '''simple docstring'''
  
  
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Value
  from datasets.packaged_modules.json.json import Json
  
  
  
  @pytest.fixture
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Optional[Any]							)  ->	Union[str, Any]:
       __lowerCamelCase     :			Optional[Any]              =	tmp_path / 'file.jsonl'
       __lowerCamelCase     :			int              =	textwrap.dedent(
           '\\n        {"col_1": -1}\n        {"col_1": 1, "col_2": 2}\n        {"col_1": 10, "col_2": 20}\n        '							)
       with open(UpperCAmelCase_   ,					'w'							) as f:
            f.write(UpperCAmelCase_							)
       return str(UpperCAmelCase_							)
  
  
  
  @pytest.fixture
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]							)  ->	Tuple:
       __lowerCamelCase     :			Tuple              =	tmp_path / 'file_utf16_encoded.jsonl'
       __lowerCamelCase     :			Optional[Any]              =	textwrap.dedent(
           '\\n        {"col_1": -1}\n        {"col_1": 1, "col_2": 2}\n        {"col_1": 10, "col_2": 20}\n        '							)
       with open(UpperCAmelCase_   ,					'w'   ,					encoding='utf-16'							) as f:
            f.write(UpperCAmelCase_							)
       return str(UpperCAmelCase_							)
  
  
  
  @pytest.fixture
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Union[str, Any]							)  ->	str:
       __lowerCamelCase     :			List[str]              =	tmp_path / 'file_with_list_of_dicts.json'
       __lowerCamelCase     :			int              =	textwrap.dedent(
           '\\n        [\n            {"col_1": -1},\n            {"col_1": 1, "col_2": 2},\n            {"col_1": 10, "col_2": 20}\n        ]\n        '							)
       with open(UpperCAmelCase_   ,					'w'							) as f:
            f.write(UpperCAmelCase_							)
       return str(UpperCAmelCase_							)
  
  
  
  @pytest.fixture
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Optional[int]							)  ->	Tuple:
       __lowerCamelCase     :			int              =	tmp_path / 'file_with_list_of_dicts_field.json'
       __lowerCamelCase     :			Union[str, Any]              =	textwrap.dedent(
           '\\n        {\n            "field1": 1,\n            "field2": "aabb",\n            "field3": [\n                {"col_1": -1},\n                {"col_1": 1, "col_2": 2},\n                {"col_1": 10, "col_2": 20}\n            ]\n        }\n        '							)
       with open(UpperCAmelCase_   ,					'w'							) as f:
            f.write(UpperCAmelCase_							)
       return str(UpperCAmelCase_							)
  
  
  
  @pytest.mark.parametrize(
      'file_fixture, config_kwargs'   ,					[
          ('jsonl_file', {}),
          ('jsonl_file_utf16_encoded', {'encoding': 'utf-16'}),
          ('json_file_with_list_of_dicts', {}),
          ('json_file_with_list_of_dicts_field', {'field': 'field3'}),
      ]   ,					)
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       int   ,					UpperCAmelCase_					:       int							)  ->	Any:
       __lowerCamelCase     :			str              =	Json(**UpperCAmelCase_							)
       __lowerCamelCase     :			Dict              =	json._generate_tables([[request.getfixturevalue(UpperCAmelCase_							)]]							)
       __lowerCamelCase     :			Tuple              =	pa.concat_tables([table for _, table in generator]							)
       assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}
  
  
  
  @pytest.mark.parametrize(
      'file_fixture, config_kwargs'   ,					[
          (
              'jsonl_file',
              {'features': Features({'col_1': Value('int64'							), 'col_2': Value('int64'							), 'missing_col': Value('string'							)}							)},
          ),
          (
              'json_file_with_list_of_dicts',
              {'features': Features({'col_1': Value('int64'							), 'col_2': Value('int64'							), 'missing_col': Value('string'							)}							)},
          ),
          (
              'json_file_with_list_of_dicts_field',
              {
                  'field': 'field3',
                  'features': Features(
                      {'col_1': Value('int64'							), 'col_2': Value('int64'							), 'missing_col': Value('string'							)}							),
              },
          ),
      ]   ,					)
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Union[str, Any]   ,					UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       Dict							)  ->	List[Any]:
       __lowerCamelCase     :			List[str]              =	Json(**UpperCAmelCase_							)
       __lowerCamelCase     :			str              =	json._generate_tables([[request.getfixturevalue(UpperCAmelCase_							)]]							)
       __lowerCamelCase     :			Optional[int]              =	pa.concat_tables([table for _, table in generator]							)
       assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
  
  
  A__	:      Optional[Any]				=						{
      """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
  }
  
  try:
        if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      List[Any]				=						["""XLMProphetNetTokenizer"""]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      Optional[Any]				=						[
            """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """XLMProphetNetDecoder""",
            """XLMProphetNetEncoder""",
            """XLMProphetNetForCausalLM""",
            """XLMProphetNetForConditionalGeneration""",
            """XLMProphetNetModel""",
            """XLMProphetNetPreTrainedModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
  
        try:
              if not is_sentencepiece_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_xlm_prophetnet import (
                  XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XLMProphetNetDecoder,
                  XLMProphetNetEncoder,
                  XLMProphetNetForCausalLM,
                  XLMProphetNetForConditionalGeneration,
                  XLMProphetNetModel,
                  XLMProphetNetPreTrainedModel,
              )
  
  else:
        import sys
  
        A__	:      List[str]				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  