






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 							a_  (		unittest.TestCase					, _snake_case				):

						def 	__a       (    self	:Union[str, Any])		->					Optional[Any]:
												UpperCAmelCase_     							=     load_tool('''image-question-answering''')
												self.tool.setup()
												UpperCAmelCase_     							=     load_tool('''image-question-answering'''   ,						remote=_lowercase)

						def 	__a       (    self	:int)		->					Union[str, Any]:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.tool(_lowercase   ,						'''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')

						def 	__a       (    self	:int)		->					Dict:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.remote_tool(_lowercase   ,						'''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')

						def 	__a       (    self	:Optional[int])		->					int:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.tool(image=_lowercase   ,						question='''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')




						def 	__a       (    self	:Optional[Any])		->					List[str]:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.remote_tool(image=_lowercase   ,						question='''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')





