





import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil






@pytest.fixture
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   )      ->   Tuple:
 __lowerCAmelCase					:	int      =       tmp_path / 'file.csv'
 __lowerCAmelCase					:	int      =       textwrap.dedent(
     """\\n        header1,header2\n        1,2\n        10,20\n        """   )
 with open(lowercase__   ,   """w"""   ) as f:
  f.write(lowercase__   )
 return str(lowercase__   )






@pytest.fixture
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[Any]   )      ->   Optional[Any]:
 __lowerCAmelCase					:	Union[str, Any]      =       tmp_path / 'malformed_file.csv'
 __lowerCAmelCase					:	Dict      =       textwrap.dedent(
     """\\n        header1,header2\n        1,2\n        10,20,\n        """   )
 with open(lowercase__   ,   """w"""   ) as f:
  f.write(lowercase__   )
 return str(lowercase__   )






@pytest.fixture
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:str   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Optional[Any]:
 __lowerCAmelCase					:	Union[str, Any]      =       tmp_path / 'csv_with_image.csv'
 __lowerCAmelCase					:	List[Any]      =       textwrap.dedent(
     F'''\
        image
        {image_file}
        '''   )
 with open(lowercase__   ,   """w"""   ) as f:
  f.write(lowercase__   )
 return str(lowercase__   )






@pytest.fixture
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   )      ->   int:
 __lowerCAmelCase					:	Optional[int]      =       tmp_path / 'csv_with_label.csv'
 __lowerCAmelCase					:	int      =       textwrap.dedent(
     """\\n        label\n        good\n        bad\n        good\n        """   )
 with open(lowercase__   ,   """w"""   ) as f:
  f.write(lowercase__   )
 return str(lowercase__   )






@pytest.fixture
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   )      ->   Tuple:
 __lowerCAmelCase					:	str      =       tmp_path / 'csv_with_int_list.csv'
 __lowerCAmelCase					:	List[Any]      =       textwrap.dedent(
     """\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        """   )
 with open(lowercase__   ,   """w"""   ) as f:
  f.write(lowercase__   )
 return str(lowercase__   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[Any]   ,   SCREAMING_SNAKE_CASE		:Any   ,   SCREAMING_SNAKE_CASE		:List[Any]   )      ->   Tuple:
 __lowerCAmelCase					:	Any      =       Csv()
 __lowerCAmelCase					:	Union[str, Any]      =       csv._generate_tables([[csv_file, malformed_csv_file]]   )
 with pytest.raises(lowercase__   ,   match="""Error tokenizing data"""   ):
  for _ in generator:
   pass
 assert any(
     record.levelname == """ERROR"""
     and """Failed to read file""" in record.message
     and os.path.basename(lowercase__   ) in record.message
     for record in caplog.records   )






@require_pil
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Union[str, Any]   )      ->   int:
 with open(lowercase__   ,   encoding="""utf-8"""   ) as f:
  __lowerCAmelCase					:	Optional[int]      =       f.read().splitlines()[1]
 __lowerCAmelCase					:	Tuple      =       Csv(encoding="""utf-8"""   ,   features=Features({"""image""": Image()}   )   )
 __lowerCAmelCase					:	Optional[int]      =       csv._generate_tables([[csv_file_with_image]]   )
 __lowerCAmelCase					:	Union[str, Any]      =       pa.concat_tables([table for _, table in generator]   )
 assert pa_table.schema.field("""image"""   ).type == Image()()
 __lowerCAmelCase					:	Tuple      =       pa_table.to_pydict()['image']
 assert generated_content == [{"path": image_file, "bytes": None}]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 with open(lowercase__   ,   encoding="""utf-8"""   ) as f:
  __lowerCAmelCase					:	Dict      =       f.read().splitlines()[1:]
 __lowerCAmelCase					:	Union[str, Any]      =       Csv(encoding="""utf-8"""   ,   features=Features({"""label""": ClassLabel(names=["""good""", """bad"""]   )}   )   )
 __lowerCAmelCase					:	Optional[Any]      =       csv._generate_tables([[csv_file_with_label]]   )
 __lowerCAmelCase					:	Optional[Any]      =       pa.concat_tables([table for _, table in generator]   )
 assert pa_table.schema.field("""label"""   ).type == ClassLabel(names=["""good""", """bad"""]   )()
 __lowerCAmelCase					:	Union[str, Any]      =       pa_table.to_pydict()['label']
 assert generated_content == [ClassLabel(names=["""good""", """bad"""]   ).straint(lowercase__   ) for label in labels]







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[Any]   )      ->   List[str]:
 __lowerCAmelCase					:	List[str]      =       Csv(encoding="""utf-8"""   ,   sep=""","""   ,   converters={"""int_list""": lambda SCREAMING_SNAKE_CASE   : [int(lowercase__   ) for i in x.split()]}   )
 __lowerCAmelCase					:	List[str]      =       csv._generate_tables([[csv_file_with_int_list]]   )
 __lowerCAmelCase					:	str      =       pa.concat_tables([table for _, table in generator]   )
 assert pa.types.is_list(pa_table.schema.field("""int_list"""   ).type   )
 __lowerCAmelCase					:	str      =       pa_table.to_pydict()['int_list']
 assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_UpperCAmelCase             =							{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCAmelCase             =							[
          'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
          'GPTBigCodeForSequenceClassification',
          'GPTBigCodeForTokenClassification',
          'GPTBigCodeForCausalLM',
          'GPTBigCodeModel',
          'GPTBigCodePreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_bigcode import (
                GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTBigCodeForCausalLM,
                GPTBigCodeForSequenceClassification,
                GPTBigCodeForTokenClassification,
                GPTBigCodeModel,
                GPTBigCodePreTrainedModel,
            )


else:
      import sys

      _UpperCAmelCase             =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)