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 						__snake_case						(			unittest.TestCase			,	__lowerCAmelCase		):





      '''simple docstring'''






      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->str:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    load_tool("""image-segmentation""")
          self.tool.setup()
          _lowerCamelCase  :		Any			    =    load_tool("""image-segmentation"""     ,	remote=_UpperCamelCase)



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		Optional[Any]			    =    Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""")) / """000000039769.png""")
          _lowerCamelCase  :		List[str]			    =    self.tool(_UpperCamelCase     ,	"""cat""")
          self.assertTrue(isinstance(_UpperCamelCase     ,	Image.Image))



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""")) / """000000039769.png""")
          _lowerCamelCase  :		List[str]			    =    self.remote_tool(_UpperCamelCase     ,	"""cat""")
          self.assertTrue(isinstance(_UpperCamelCase     ,	Image.Image))



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->int:


          """simple docstring"""


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




      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		str			    =    Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""")) / """000000039769.png""")
          _lowerCamelCase  :		Optional[int]			    =    self.remote_tool(image=_UpperCamelCase     ,	label="""cat""")
          self.assertTrue(isinstance(_UpperCamelCase     ,	Image.Image))





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase      :				List[Any]     			=logging.get_logger(__name__)

lowerCAmelCase      :				Optional[int]     			={
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							'vit-hybrid'



      def __init__(       self  :					str     ,	_UpperCamelCase  :					int=None     ,	_UpperCamelCase  :					Union[str, Any]=768     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					Optional[int]=3072     ,	_UpperCamelCase  :					Optional[int]="gelu"     ,	_UpperCamelCase  :					int=0.0     ,	_UpperCamelCase  :					Tuple=0.0     ,	_UpperCamelCase  :					Dict=0.0_2     ,	_UpperCamelCase  :					Dict=1E-1_2     ,	_UpperCamelCase  :					Union[str, Any]=224     ,	_UpperCamelCase  :					str=1     ,	_UpperCamelCase  :					Optional[int]=3     ,	_UpperCamelCase  :					Dict=[1, 1024, 24, 24]     ,	_UpperCamelCase  :					Optional[Any]=True     ,	**_UpperCamelCase  :					List[Any]     ,	)					->str:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          if backbone_config is None:
              logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone.""")
              _lowerCamelCase  :		Tuple			    =    {
                  """global_padding""": """same""",
                  """layer_type""": """bottleneck""",
                  """depths""": [3, 4, 9],
                  """out_features""": ["""stage3"""],
                  """embedding_dynamic_padding""": True,
              }

          if isinstance(_UpperCamelCase     ,	_UpperCamelCase):
              if "model_type" in backbone_config:
                  _lowerCamelCase  :		int			    =    CONFIG_MAPPING[backbone_config["""model_type"""]]
              else:
                  logger.info(
                      """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.""")
                  _lowerCamelCase  :		Optional[int]			    =    BitConfig
              _lowerCamelCase  :		List[Any]			    =    backbone_config_class(**_UpperCamelCase)

          _lowerCamelCase  :		Any			    =    backbone_featmap_shape
          _lowerCamelCase  :		Dict			    =    backbone_config
          _lowerCamelCase  :		Tuple			    =    hidden_size
          _lowerCamelCase  :		Union[str, Any]			    =    num_hidden_layers
          _lowerCamelCase  :		Optional[Any]			    =    num_attention_heads
          _lowerCamelCase  :		Optional[Any]			    =    intermediate_size
          _lowerCamelCase  :		List[Any]			    =    hidden_act
          _lowerCamelCase  :		Dict			    =    hidden_dropout_prob
          _lowerCamelCase  :		Optional[Any]			    =    attention_probs_dropout_prob
          _lowerCamelCase  :		Union[str, Any]			    =    initializer_range
          _lowerCamelCase  :		Any			    =    layer_norm_eps
          _lowerCamelCase  :		Optional[int]			    =    image_size
          _lowerCamelCase  :		Any			    =    patch_size
          _lowerCamelCase  :		Optional[Any]			    =    num_channels
          _lowerCamelCase  :		List[str]			    =    qkv_bias




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Dict[str, any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    copy.deepcopy(self.__dict__)
          _lowerCamelCase  :		List[Any]			    =    self.backbone_config.to_dict()
          _lowerCamelCase  :		List[str]			    =    self.__class__.model_type
          return output




