






'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCamelCase   :							Union[str, Any]         =   {
    """configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase   :							Optional[Any]         =   ["""ConvNextFeatureExtractor"""]
 __UpperCamelCase   :							Dict         =   ["""ConvNextImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase   :							List[str]         =   [
     """CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ConvNextForImageClassification""",
     """ConvNextModel""",
     """ConvNextPreTrainedModel""",
     """ConvNextBackbone""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase   :							str         =   [
     """TFConvNextForImageClassification""",
     """TFConvNextModel""",
     """TFConvNextPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_convnext import ConvNextFeatureExtractor
  from .image_processing_convnext import ConvNextImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convnext import (
      CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvNextBackbone,
      ConvNextForImageClassification,
      ConvNextModel,
      ConvNextPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
 import sys

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







'''simple docstring'''



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   if not all(x.isalpha() for x in string     ):
      raise ValueError('String must only contain alphabetic characters.'     )

   __a					  =     sorted(string.lower()     )
   return len(SCREAMING_SNAKE_CASE__     ) == len(set(SCREAMING_SNAKE_CASE__     )     )


if __name__ == "__main__":
 __UpperCamelCase   :							int         =   input("""Enter a string """).strip()

 __UpperCamelCase   :							Tuple         =   is_isogram(input_str)
 print(f"""{input_str} is {"an" if isogram else "not an"} isogram.""")