
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 			UpperCamelCase					(    __lowerCamelCase     : List[Any]    ):
       snake_case       :   Dict							=      tmp_path / "file.csv"
       snake_case       :   List[str]							=      textwrap.dedent(
           "\\n        header1,header2\n        1,2\n        10,20\n        "    )
       with open(__lowerCamelCase				,					"w"    ) as f:
              f.write(__lowerCamelCase    )
       return str(__lowerCamelCase    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]    ):
       snake_case       :   Optional[int]							=      tmp_path / "malformed_file.csv"
       snake_case       :   Union[str, Any]							=      textwrap.dedent(
           "\\n        header1,header2\n        1,2\n        10,20,\n        "    )
       with open(__lowerCamelCase				,					"w"    ) as f:
              f.write(__lowerCamelCase    )
       return str(__lowerCamelCase    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Union[str, Any]    ):
       snake_case       :   Any							=      tmp_path / "csv_with_image.csv"
       snake_case       :   Union[str, Any]							=      textwrap.dedent(
           f"""\
        image
        {image_file}
        """    )
       with open(__lowerCamelCase				,					"w"    ) as f:
              f.write(__lowerCamelCase    )
       return str(__lowerCamelCase    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Dict    ):
       snake_case       :   Dict							=      tmp_path / "csv_with_label.csv"
       snake_case       :   int							=      textwrap.dedent(
           "\\n        label\n        good\n        bad\n        good\n        "    )
       with open(__lowerCamelCase				,					"w"    ) as f:
              f.write(__lowerCamelCase    )
       return str(__lowerCamelCase    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       snake_case       :   str							=      tmp_path / "csv_with_int_list.csv"
       snake_case       :   Dict							=      textwrap.dedent(
           "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "    )
       with open(__lowerCamelCase				,					"w"    ) as f:
              f.write(__lowerCamelCase    )
       return str(__lowerCamelCase    )


def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : List[Any]				,					__lowerCamelCase     : str    ):
       snake_case       :   Dict							=      Csv()
       snake_case       :   str							=      csv._generate_tables([[csv_file, malformed_csv_file]]    )
       with pytest.raises(__lowerCamelCase				,					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(__lowerCamelCase    ) in record.message
           for record in caplog.records    )


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


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




def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]    ):
       snake_case       :   Union[str, Any]							=      Csv(encoding="utf-8"				,					sep=","				,					converters={"int_list": lambda __lowerCamelCase    : [int(__lowerCamelCase    ) for i in x.split()]}    )
       snake_case       :   Any							=      csv._generate_tables([[csv_file_with_int_list]]    )
       snake_case       :   Optional[Any]							=      pa.concat_tables([table for _, table in generator]    )
       assert pa.types.is_list(pa_table.schema.field("int_list"    ).type    )
       snake_case       :   List[Any]							=      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_tokenizers_available, is_torch_available


__lowerCamelCase							=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BioGptForCausalLM""",
       """BioGptForTokenClassification""",
       """BioGptForSequenceClassification""",
       """BioGptModel""",
       """BioGptPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





