





from typing import TYPE_CHECKING

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


__UpperCAmelCase : Union[str, Any] 			=				{
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase : List[Any] 			=				[
      "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ResNetForImageClassification",
      "ResNetModel",
      "ResNetPreTrainedModel",
      "ResNetBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase : Dict 			=				[
      "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFResNetForImageClassification",
      "TFResNetModel",
      "TFResNetPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase : List[str] 			=				[
      "FlaxResNetForImageClassification",
      "FlaxResNetModel",
      "FlaxResNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_resnet import (
        RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        ResNetBackbone,
        ResNetForImageClassification,
        ResNetModel,
        ResNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_resnet import (
        TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFResNetForImageClassification,
        TFResNetModel,
        TFResNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
  import sys

  __UpperCAmelCase : int 			=				_LazyModule(__name__, globals()["__file__"], _import_structure)





import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              with open(__SCREAMING_SNAKE_CASE ,					encoding='''utf-8'''						) as input_file:
                     UpperCamelCase    :	str   	=		re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''						)
                     UpperCamelCase    :	Optional[int]   	=		input_file.read()
                     UpperCamelCase    :	Union[str, Any]   	=		regexp.search(__SCREAMING_SNAKE_CASE						)

              return match






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




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

              UpperCamelCase    :	int   	=		[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						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		Path('''./datasets'''						)
              UpperCamelCase    :	Tuple   	=		list(dataset_paths.absolute().glob('''**/*.py'''						)						)

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






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		Path('''./datasets'''						)
              UpperCamelCase    :	Tuple   	=		list(dataset_paths.absolute().glob('''**/*.py'''						)						)

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

