


from typing import TYPE_CHECKING

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


_snake_case					=					{
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					['''SegformerFeatureExtractor''']
   _snake_case					=					['''SegformerImageProcessor''']

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

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''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

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







import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
   from PIL import Image
class      UpperCAmelCase_ ( unittest.TestCase		,			UpperCamelCase   ):

   '''simple docstring'''




   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    load_tool("image-segmentation"			)
     self.tool.setup()
     lowerCamelCase			: Optional[Any]   =    load_tool("image-segmentation"						,    remote=__A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[str]   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Dict   =    self.tool(__A						,    "cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: int   =    self.remote_tool(__A						,    "cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Optional[Any]   =    self.tool(image=__A						,    label="cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: str   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Dict   =    self.remote_tool(image=__A						,    label="cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)



