






'''simple docstring'''






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  (				A  :       str  )    -> str:







			"""simple docstring"""
			__snake_case				:      Optional[int]     							= tmp_path / 'file.csv'
			__snake_case				:      Optional[int]     							= textwrap.dedent(
			    '\\n        header1,header2\n        1,2\n        10,20\n        '  )
			with open(A  ,		'w'  ) as f:
						f.write(A  )
			return str(A  )




@pytest.fixture
def        _SCREAMING_SNAKE_CASE  (				A  :       List[str]  )    -> Dict:







			"""simple docstring"""
			__snake_case				:      Union[str, Any]     							= tmp_path / 'malformed_file.csv'
			__snake_case				:      Dict     							= textwrap.dedent(
			    '\\n        header1,header2\n        1,2\n        10,20,\n        '  )
			with open(A  ,		'w'  ) as f:
						f.write(A  )
			return str(A  )




@pytest.fixture
def        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  ,		A  :       Any  )    -> Union[str, Any]:







			"""simple docstring"""
			__snake_case				:      Union[str, Any]     							= tmp_path / 'csv_with_image.csv'
			__snake_case				:      Optional[int]     							= textwrap.dedent(
			    F"""\
        image
        {image_file}
        """  )
			with open(A  ,		'w'  ) as f:
						f.write(A  )
			return str(A  )




@pytest.fixture
def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> str:







			"""simple docstring"""
			__snake_case				:      int     							= tmp_path / 'csv_with_label.csv'
			__snake_case				:      Optional[int]     							= textwrap.dedent(
			    '\\n        label\n        good\n        bad\n        good\n        '  )
			with open(A  ,		'w'  ) as f:
						f.write(A  )
			return str(A  )




@pytest.fixture
def        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  )    -> Dict:







			"""simple docstring"""
			__snake_case				:      str     							= tmp_path / 'csv_with_int_list.csv'
			__snake_case				:      Optional[int]     							= textwrap.dedent(
			    '\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        '  )
			with open(A  ,		'w'  ) as f:
						f.write(A  )
			return str(A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       List[str]  ,		A  :       int  ,		A  :       Tuple  )    -> Tuple:







			"""simple docstring"""
			__snake_case				:      Union[str, Any]     							= Csv()
			__snake_case				:      Dict     							= csv._generate_tables([[csv_file, malformed_csv_file]]  )
			with pytest.raises(A  ,		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(A  ) in record.message
			    for record in caplog.records  )




@require_pil
def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  )    -> Optional[int]:







			"""simple docstring"""
			with open(A  ,		encoding='utf-8'  ) as f:
						__snake_case				:      int     							= f.read().splitlines()[1]
			__snake_case				:      List[Any]     							= Csv(encoding='utf-8'  ,		features=Features({'image': Image()}  )  )
			__snake_case				:      List[Any]     							= csv._generate_tables([[csv_file_with_image]]  )
			__snake_case				:      int     							= pa.concat_tables([table for _, table in generator]  )
			assert pa_table.schema.field('image'  ).type == Image()()
			__snake_case				:      Optional[Any]     							= pa_table.to_pydict()['image']
			assert generated_content == [{"path": image_file, "bytes": None}]




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  )    -> List[Any]:







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





def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> int:







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







'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A    	=   {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''TimmBackbone''']


if TYPE_CHECKING:
      from .configuration_timm_backbone import TimmBackboneConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_timm_backbone import TimmBackbone

else:
      import sys

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