"""simple docstring"""



import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Dict	,      A_							:     str    )    ->       List[str]:

         with open(A_	,      encoding='''utf-8'''    ) as input_file:
              __snake_case				=							re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''    )
              __snake_case				=							input_file.read()
              __snake_case				=							regexp.search(A_    )

         return match
    def  lowercase					(		self							:     Optional[int]	,      A_							:     str    )    ->       int:

         with open(A_	,      encoding='''utf-8'''    ) as input_file:
              __snake_case				=							re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'''	,      re.DOTALL    )
              __snake_case				=							input_file.read()
              # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
              __snake_case				=							regexp.finditer(A_    )

         __snake_case				=							[match for match in matches if match is not None and match.group(1    ) is not None]
         return matches[0] if matches else None
    def  lowercase					(		self							:     int    )    ->       List[Any]:
         __snake_case				=							Path('''./datasets'''    )
         __snake_case				=							list(dataset_paths.absolute().glob('''**/*.py'''    )    )

         for dataset in dataset_files:
              if self._no_encoding_on_file_open(str(A_    )    ):
                   raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}"    )







    def  lowercase					(		self							:     int    )    ->       Tuple:
         __snake_case				=							Path('''./datasets'''    )
         __snake_case				=							list(dataset_paths.absolute().glob('''**/*.py'''    )    )

         for dataset in dataset_files:
              if self._no_print_statements(str(A_    )    ):
                   raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead."    )
"""simple docstring"""



from typing import TYPE_CHECKING

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


__lowercase		:		Dict														=							{
    "configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
    "processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Optional[int]														=							["VisionTextDualEncoderModel"]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Optional[int]														=							["FlaxVisionTextDualEncoderModel"]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Any														=							["TFVisionTextDualEncoderModel"]


if TYPE_CHECKING:
       from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
       from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel


else:
       import sys

       __lowercase		:		Tuple														=							_LazyModule(__name__, globals()["__file__"], _import_structure)