"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase_:       Any			     =       {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Optional[int]			     =       ["SegformerFeatureExtractor"]
 lowerCAmelCase_:       Union[str, Any]			     =       ["SegformerImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Tuple			     =       [
     "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "SegformerDecodeHead",
     "SegformerForImageClassification",
     "SegformerForSemanticSegmentation",
     "SegformerLayer",
     "SegformerModel",
     "SegformerPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Optional[int]			     =       [
     "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFSegformerDecodeHead",
     "TFSegformerForImageClassification",
     "TFSegformerForSemanticSegmentation",
     "TFSegformerModel",
     "TFSegformerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_segformer import SegformerFeatureExtractor
  from .image_processing_segformer import SegformerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_segformer import (
      SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      SegformerDecodeHead,
      SegformerForImageClassification,
      SegformerForSemanticSegmentation,
      SegformerLayer,
      SegformerModel,
      SegformerPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_segformer import (
      TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSegformerDecodeHead,
      TFSegformerForImageClassification,
      TFSegformerForSemanticSegmentation,
      TFSegformerModel,
      TFSegformerPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase_:       List[Any]			     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







"""simple docstring"""
from collections import defaultdict







def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     dict:



       """simple docstring"""







       __magic_name__			:		defaultdict[str, int]        =     defaultdict(lowerCamelCase   )
       # Creating a dictionary containing count of each word
       for word in sentence.split():
              occurrence[word] += 1
       return occurrence


if __name__ == "__main__":
 for word, count in word_occurrence("""INPUT STRING""").items():
  print(F"""{word}: {count}""")
