'''simple docstring'''

import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput





@dataclass
class 				lowercase    (    __lowerCamelCase			):
   lowerCamelCase_            =42
   lowerCamelCase_            =None
   lowerCamelCase_            =None





class 				lowercase    (    unittest.TestCase			):


   def __UpperCAmelCase							( self					:						Tuple)   ->       Dict:
         lowercase_     			=   ModelOutputTest(a=30)
         self.assertEqual(x.a ,		30)
         self.assertIsNone(x.b)
         self.assertIsNone(x.c)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.d


   def __UpperCAmelCase							( self					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(x[0] ,		30)
         self.assertEqual(x[1] ,		10)
         self.assertEqual(x[:2] ,		(30, 10))
         self.assertEqual(x[:] ,		(30, 10))

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(x[0] ,		30)
         self.assertEqual(x[1] ,		10)
         self.assertEqual(x[:2] ,		(30, 10))
         self.assertEqual(x[:] ,		(30, 10))


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[str]:
         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(x["a"] ,		30)
         self.assertEqual(x["b"] ,		10)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x["c"]

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(x["a"] ,		30)
         self.assertEqual(x["c"] ,		10)
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x["b"]


   def __UpperCAmelCase							( self					:						List[str])   ->       List[str]:
         lowercase_     			=   ModelOutputTest(a=30)
         self.assertEqual(list(x.keys()) ,		["a"])
         self.assertEqual(list(x.values()) ,		[30])
         self.assertEqual(list(x.items()) ,		[("a", 30)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a"])

         lowercase_     			=   ModelOutputTest(a=30 ,		b=10)
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(list(x.values()) ,		[30, 10])
         self.assertEqual(list(x.items()) ,		[("a", 30), ("b", 10)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a", "b"])

         lowercase_     			=   ModelOutputTest(a=30 ,		c=10)
         self.assertEqual(list(x.keys()) ,		["a", "c"])
         self.assertEqual(list(x.values()) ,		[30, 10])
         self.assertEqual(list(x.items()) ,		[("a", 30), ("c", 10)])
         self.assertEqual(list(__lowerCAmelCase) ,		["a", "c"])

         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.update({"d": 20})
         with self.assertRaises(__lowerCAmelCase):
               del x["a"]
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.pop("a")
         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   x.setdefault("d" ,		32)


   def __UpperCAmelCase							( self					:						List[Any])   ->       Optional[int]:
         lowercase_     			=   ModelOutputTest(a=30)
         lowercase_     			=   10
         self.assertEqual(x.a ,		10)
         self.assertEqual(x["a"] ,		10)


   def __UpperCAmelCase							( self					:						List[Any])   ->       Any:
         lowercase_     			=   ModelOutputTest(a=30)
         lowercase_     			=   10
         self.assertEqual(x.a ,		10)
         self.assertEqual(x["a"] ,		10)


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[Any]:
         lowercase_     			=   ModelOutputTest({"a": 30, "b": 10})
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(x.a ,		30)
         self.assertEqual(x.b ,		10)


   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         lowercase_     			=   ModelOutputTest([("a", 30), ("b", 10)])
         self.assertEqual(list(x.keys()) ,		["a", "b"])
         self.assertEqual(x.a ,		30)
         self.assertEqual(x.b ,		10)

         with self.assertRaises(__lowerCAmelCase):
               lowercase_     			=   ModelOutputTest([("a", 30), (10, 10)])

         lowercase_     			=   ModelOutputTest(a=(30, 30))
         self.assertEqual(list(x.keys()) ,		["a"])
         self.assertEqual(x.a ,		(30, 30))







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_			:			Tuple    	=   {"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"]}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Optional[int]    	=   ["ViltFeatureExtractor"]
       lowerCAmelCase_			:			str    	=   ["ViltImageProcessor"]
       lowerCAmelCase_			:			Any    	=   ["ViltProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Any    	=   [
           "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ViltForImageAndTextRetrieval",
           "ViltForImagesAndTextClassification",
           "ViltForTokenClassification",
           "ViltForMaskedLM",
           "ViltForQuestionAnswering",
           "ViltLayer",
           "ViltModel",
           "ViltPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vilt import ViltFeatureExtractor
              from .image_processing_vilt import ViltImageProcessor
              from .processing_vilt import ViltProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vilt import (
                  VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViltForImageAndTextRetrieval,
                  ViltForImagesAndTextClassification,
                  ViltForMaskedLM,
                  ViltForQuestionAnswering,
                  ViltForTokenClassification,
                  ViltLayer,
                  ViltModel,
                  ViltPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_			:			str    	=   _LazyModule(__name__, globals()["__file__"], _import_structure)






