





'''simple docstring'''

import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil





@pytest.fixture
def lowerCamelCase						(					__lowerCamelCase			:   Any   )						->int:
    _SCREAMING_SNAKE_CASE     =      tmp_path / """text.txt"""
    _SCREAMING_SNAKE_CASE     =      textwrap.dedent(
        """\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        """   )
    with open(__lowerCamelCase ,	"""w""" ,	encoding="""utf-8"""   ) as f:
        f.write(__lowerCamelCase   )
    return str(__lowerCamelCase   )





@pytest.fixture
def lowerCamelCase						(					__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   Tuple   )						->Optional[int]:
    _SCREAMING_SNAKE_CASE     =      tmp_path / """text_with_image.txt"""
    with open(__lowerCamelCase ,	"""w""" ,	encoding="""utf-8"""   ) as f:
        f.write(__lowerCamelCase   )
    return str(__lowerCamelCase   )





@pytest.mark.parametrize("""keep_linebreaks""" ,	[True, False]   )
def lowerCamelCase						(					__lowerCamelCase			:   Optional[Any] ,	__lowerCamelCase			:   Optional[int]   )						->Optional[Any]:
    with open(__lowerCamelCase ,	encoding="""utf-8"""   ) as f:
        _SCREAMING_SNAKE_CASE     =      f.read().splitlines(keepends=__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      Text(keep_linebreaks=__lowerCamelCase ,	encoding="""utf-8"""   )
    _SCREAMING_SNAKE_CASE     =      text._generate_tables([[text_file]]   )
    _SCREAMING_SNAKE_CASE     =      pa.concat_tables([table for _, table in generator]   ).to_pydict()["""text"""]
    assert generated_content == expected_content





@require_pil
def lowerCamelCase						(					__lowerCamelCase			:   Dict   )						->Any:
    with open(__lowerCamelCase ,	encoding="""utf-8"""   ) as f:
        _SCREAMING_SNAKE_CASE     =      f.read().splitlines()[0]
    _SCREAMING_SNAKE_CASE     =      Text(encoding="""utf-8""" ,	features=Features({"""image""": Image()}   )   )
    _SCREAMING_SNAKE_CASE     =      text._generate_tables([[text_file_with_image]]   )
    _SCREAMING_SNAKE_CASE     =      pa.concat_tables([table for _, table in generator]   )
    assert pa_table.schema.field("""image"""   ).type == Image()()
    _SCREAMING_SNAKE_CASE     =      pa_table.to_pydict()["""image"""]
    assert generated_content == [{"path": image_file, "bytes": None}]






@pytest.mark.parametrize("""sample_by""" ,	["""line""", """paragraph""", """document"""]   )
def lowerCamelCase						(					__lowerCamelCase			:   Dict ,	__lowerCamelCase			:   Union[str, Any]   )						->List[Any]:
    with open(__lowerCamelCase ,	encoding="""utf-8"""   ) as f:
        _SCREAMING_SNAKE_CASE     =      f.read()
    if sample_by == "line":
        _SCREAMING_SNAKE_CASE     =      expected_content.splitlines()
    elif sample_by == "paragraph":
        _SCREAMING_SNAKE_CASE     =      expected_content.split("""\n\n"""   )
    elif sample_by == "document":
        _SCREAMING_SNAKE_CASE     =      [expected_content]
    _SCREAMING_SNAKE_CASE     =      Text(sample_by=__lowerCamelCase ,	encoding="""utf-8""" ,	chunksize=100   )
    _SCREAMING_SNAKE_CASE     =      text._generate_tables([[text_file]]   )
    _SCREAMING_SNAKE_CASE     =      pa.concat_tables([table for _, table in generator]   ).to_pydict()["""text"""]
    assert generated_content == expected_content










'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase_         =			{
    """configuration_esm""": ["""ESM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """EsmConfig"""],
    """tokenization_esm""": ["""EsmTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EsmForMaskedLM""",
          """EsmForSequenceClassification""",
          """EsmForTokenClassification""",
          """EsmModel""",
          """EsmPreTrainedModel""",
      ]
      lowercase_         =			["""EsmForProteinFolding""", """EsmFoldPreTrainedModel"""]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEsmForMaskedLM""",
          """TFEsmForSequenceClassification""",
          """TFEsmForTokenClassification""",
          """TFEsmModel""",
          """TFEsmPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
      from .tokenization_esm import EsmTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_esm import (
                ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                EsmForMaskedLM,
                EsmForSequenceClassification,
                EsmForTokenClassification,
                EsmModel,
                EsmPreTrainedModel,
            )
            from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_esm import (
                TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEsmForMaskedLM,
                TFEsmForSequenceClassification,
                TFEsmForTokenClassification,
                TFEsmModel,
                TFEsmPreTrainedModel,
            )


else:
      import sys

      lowercase_         =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



