





import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
     from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Dict:



   '''simple docstring'''



   lowerCamelCase__:   Tuple    						=self.config_class(**self.inputs_dict)
   self.parent.assertTrue(hasattr(UpperCAmelCase_ ,				"hidden_sizes"))
   self.parent.assertTrue(hasattr(UpperCAmelCase_ ,				"neck_hidden_sizes"))
   self.parent.assertTrue(hasattr(UpperCAmelCase_ ,				"num_attention_heads"))



class      _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def __init__(self       :     List[str] ,				UpperCAmelCase_       :     str ,				UpperCAmelCase_       :     Optional[Any]=13 ,				UpperCAmelCase_       :     Optional[int]=32 ,				UpperCAmelCase_       :     str=2 ,				UpperCAmelCase_       :     Dict=3 ,				UpperCAmelCase_       :     Tuple=640 ,				UpperCAmelCase_       :     Union[str, Any]=4 ,				UpperCAmelCase_       :     List[str]="silu" ,				UpperCAmelCase_       :     Tuple=3 ,				UpperCAmelCase_       :     Dict=32 ,				UpperCAmelCase_       :     List[Any]=0.1 ,				UpperCAmelCase_       :     str=0.1 ,				UpperCAmelCase_       :     Any=0.1 ,				UpperCAmelCase_       :     Union[str, Any]=0.02 ,				UpperCAmelCase_       :     int=True ,				UpperCAmelCase_       :     List[str]=True ,				UpperCAmelCase_       :     Union[str, Any]=10 ,				UpperCAmelCase_       :     Optional[Any]=None ,				) ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:   Tuple    						=parent
   lowerCamelCase__:   Optional[Any]    						=batch_size
   lowerCamelCase__:   Optional[int]    						=image_size
   lowerCamelCase__:   Optional[Any]    						=patch_size
   lowerCamelCase__:   Tuple    						=num_channels
   lowerCamelCase__:   List[str]    						=last_hidden_size
   lowerCamelCase__:   Union[str, Any]    						=num_attention_heads
   lowerCamelCase__:   Union[str, Any]    						=hidden_act
   lowerCamelCase__:   Union[str, Any]    						=conv_kernel_size
   lowerCamelCase__:   Any    						=output_stride
   lowerCamelCase__:   Optional[Any]    						=hidden_dropout_prob
   lowerCamelCase__:   Optional[int]    						=attention_probs_dropout_prob
   lowerCamelCase__:   Optional[Any]    						=classifier_dropout_prob
   lowerCamelCase__:   Tuple    						=use_labels
   lowerCamelCase__:   List[Any]    						=is_training
   lowerCamelCase__:   Tuple    						=num_labels
   lowerCamelCase__:   Optional[int]    						=initializer_range
   lowerCamelCase__:   List[Any]    						=scope

 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:   str    						=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

   lowerCamelCase__:   Any    						=None
   lowerCamelCase__:   str    						=None
   if self.use_labels:
     lowerCamelCase__:   Dict    						=ids_tensor([self.batch_size] ,				self.num_labels)
     lowerCamelCase__:   Tuple    						=ids_tensor([self.batch_size, self.image_size, self.image_size] ,				self.num_labels)

   lowerCamelCase__:   Optional[Any]    						=self.get_config()

   return config, pixel_values, labels, pixel_labels

 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->str:



   '''simple docstring'''



   return MobileViTConfig(
       image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				num_attention_heads=self.num_attention_heads ,				hidden_act=self.hidden_act ,				conv_kernel_size=self.conv_kernel_size ,				output_stride=self.output_stride ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				classifier_dropout_prob=self.classifier_dropout_prob ,				initializer_range=self.initializer_range ,				)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any] ,				UpperCAmelCase_       :     Dict ,				UpperCAmelCase_       :     Union[str, Any] ,				UpperCAmelCase_       :     Optional[int] ,				UpperCAmelCase_       :     Optional[int]) ->Dict:



   '''simple docstring'''



   lowerCamelCase__:   Any    						=MobileViTModel(config=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:   Any    						=model(UpperCAmelCase_)
   self.parent.assertEqual(
       result.last_hidden_state.shape ,				(
           self.batch_size,
           self.last_hidden_size,
           self.image_size // self.output_stride,
           self.image_size // self.output_stride,
       ) ,				)

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any] ,				UpperCAmelCase_       :     Optional[int] ,				UpperCAmelCase_       :     Any ,				UpperCAmelCase_       :     Optional[int] ,				UpperCAmelCase_       :     List[str]) ->int:



   '''simple docstring'''



   lowerCamelCase__:   str    						=self.num_labels
   lowerCamelCase__:   Dict    						=MobileViTForImageClassification(UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:   List[str]    						=model(UpperCAmelCase_ ,				labels=UpperCAmelCase_)
   self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels))

 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict ,				UpperCAmelCase_       :     int ,				UpperCAmelCase_       :     List[Any] ,				UpperCAmelCase_       :     List[str] ,				UpperCAmelCase_       :     int) ->str:



   '''simple docstring'''



   lowerCamelCase__:   int    						=self.num_labels
   lowerCamelCase__:   Optional[int]    						=MobileViTForSemanticSegmentation(UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:   List[str]    						=model(UpperCAmelCase_)
   self.parent.assertEqual(
       result.logits.shape ,				(
           self.batch_size,
           self.num_labels,
           self.image_size // self.output_stride,
           self.image_size // self.output_stride,
       ) ,				)
   lowerCamelCase__:   Tuple    						=model(UpperCAmelCase_ ,				labels=UpperCAmelCase_)
   self.parent.assertEqual(
       result.logits.shape ,				(
           self.batch_size,
           self.num_labels,
           self.image_size // self.output_stride,
           self.image_size // self.output_stride,
       ) ,				)







 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:   Optional[int]    						=self.prepare_config_and_inputs()
   lowerCamelCase__:   str    						=config_and_inputs
   lowerCamelCase__:   str    						={"pixel_values": pixel_values}
   return config, inputs_dict



@require_torch
class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							unittest.TestCase					):







 '''simple docstring'''
 lowercase_	=    (
     (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 lowercase_	=    (
     {
         "feature-extraction": MobileViTModel,
         "image-classification": MobileViTForImageClassification,
         "image-segmentation": MobileViTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowercase_	=    False
 lowercase_	=    False
 lowercase_	=    False
 lowercase_	=    False

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any]) ->Dict:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=MobileViTModelTester(self)
   lowerCamelCase__:   int    						=MobileViTConfigTester(self ,				config_class=UpperCAmelCase_ ,				has_text_modality=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->List[str]:



   '''simple docstring'''



   self.config_tester.run_common_tests()

 @unittest.skip(reason="MobileViT does not use inputs_embeds")
 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->str:



   '''simple docstring'''



   pass

 @unittest.skip(reason="MobileViT does not support input and output embeddings")
 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->Any:



   '''simple docstring'''



   pass

 @unittest.skip(reason="MobileViT does not output attentions")
 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->Optional[int]:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int]) ->Any:



   '''simple docstring'''



   lowerCamelCase__:   int    						=self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__:   str    						=model_class(UpperCAmelCase_)
     lowerCamelCase__:   Optional[Any]    						=inspect.signature(model.forward)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     lowerCamelCase__:   Union[str, Any]    						=[*signature.parameters.keys()]

     lowerCamelCase__:   Optional[int]    						=["pixel_values"]
     self.assertListEqual(arg_names[:1] ,				UpperCAmelCase_)

 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->Dict:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->int:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase_)

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



   '''simple docstring'''



   def check_hidden_states_output(UpperCAmelCase_       :     Union[str, Any] ,				UpperCAmelCase_       :     Any ,				UpperCAmelCase_       :     Union[str, Any]):
     lowerCamelCase__:   Optional[Any]    						=model_class(UpperCAmelCase_)
     model.to(UpperCAmelCase_)
     model.eval()

     with torch.no_grad():
       lowerCamelCase__:   List[str]    						=model(**self._prepare_for_class(UpperCAmelCase_ ,				UpperCAmelCase_))

     lowerCamelCase__:   List[Any]    						=outputs.hidden_states

     lowerCamelCase__:   Any    						=5
     self.assertEqual(len(UpperCAmelCase_) ,				UpperCAmelCase_)

     # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
     # with the width and height being successively divided by 2.
     lowerCamelCase__:   Tuple    						=2
     for i in range(len(UpperCAmelCase_)):
       self.assertListEqual(
           list(hidden_states[i].shape[-2:]) ,				[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] ,				)
       divisor *= 2

     self.assertEqual(self.model_tester.output_stride ,				divisor // 2)

   lowerCamelCase__:   List[str]    						=self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__:   List[Any]    						=True
     check_hidden_states_output(UpperCAmelCase_ ,				UpperCAmelCase_ ,				UpperCAmelCase_)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     lowerCamelCase__:   Optional[int]    						=True

     check_hidden_states_output(UpperCAmelCase_ ,				UpperCAmelCase_ ,				UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any]) ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->str:



   '''simple docstring'''



   lowerCamelCase__:   Any    						=self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_)







 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->List[Any]:



   '''simple docstring'''



   for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowerCamelCase__:   str    						=MobileViTModel.from_pretrained(UpperCAmelCase_)
     self.assertIsNotNone(UpperCAmelCase_)





def   lowerCAmelCase_				( )  ->	Dict:







  """simple docstring"""





  lowerCamelCase__:   List[str]    						=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
  return image






@require_torch
@require_vision
class      _SCREAMING_SNAKE_CASE		(			unittest.TestCase					):







 '''simple docstring'''

 @cached_property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int]) ->Any:



   '''simple docstring'''



   return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small") if is_vision_available() else None

 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->int:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small").to(UpperCAmelCase_)

   lowerCamelCase__:   Tuple    						=self.default_image_processor
   lowerCamelCase__:   int    						=prepare_img()
   lowerCamelCase__:   Optional[Any]    						=image_processor(images=UpperCAmelCase_ ,				return_tensors="pt").to(UpperCAmelCase_)

   # forward pass
   with torch.no_grad():
     lowerCamelCase__:   Dict    						=model(**UpperCAmelCase_)

   # verify the logits
   lowerCamelCase__:   Optional[int]    						=torch.Size((1, 1_000))
   self.assertEqual(outputs.logits.shape ,				UpperCAmelCase_)

   lowerCamelCase__:   Optional[Any]    						=torch.tensor([-1.9364, -1.2327, -0.4653]).to(UpperCAmelCase_)

   self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				UpperCAmelCase_ ,				atol=1E-4))

 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
   lowerCamelCase__:   Dict    						=model.to(UpperCAmelCase_)

   lowerCamelCase__:   Any    						=MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")

   lowerCamelCase__:   Tuple    						=prepare_img()
   lowerCamelCase__:   int    						=image_processor(images=UpperCAmelCase_ ,				return_tensors="pt").to(UpperCAmelCase_)

   # forward pass
   with torch.no_grad():
     lowerCamelCase__:   Dict    						=model(**UpperCAmelCase_)
   lowerCamelCase__:   str    						=outputs.logits

   # verify the logits
   lowerCamelCase__:   Tuple    						=torch.Size((1, 21, 32, 32))
   self.assertEqual(logits.shape ,				UpperCAmelCase_)

   lowerCamelCase__:   Dict    						=torch.tensor(
       [
           [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
           [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
           [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
       ] ,				device=UpperCAmelCase_ ,				)

   self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,				UpperCAmelCase_ ,				atol=1E-4))







 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->Any:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
   lowerCamelCase__:   Union[str, Any]    						=model.to(UpperCAmelCase_)

   lowerCamelCase__:   Union[str, Any]    						=MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")

   lowerCamelCase__:   Any    						=prepare_img()
   lowerCamelCase__:   Optional[int]    						=image_processor(images=UpperCAmelCase_ ,				return_tensors="pt").to(UpperCAmelCase_)

   # forward pass
   with torch.no_grad():
     lowerCamelCase__:   int    						=model(**UpperCAmelCase_)

   lowerCamelCase__:   Dict    						=outputs.logits.detach().cpu()

   lowerCamelCase__:   List[Any]    						=image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_ ,				target_sizes=[(50, 60)])
   lowerCamelCase__:   Optional[Any]    						=torch.Size((50, 60))
   self.assertEqual(segmentation[0].shape ,				UpperCAmelCase_)

   lowerCamelCase__:   Optional[Any]    						=image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_)
   lowerCamelCase__:   str    						=torch.Size((32, 32))
   self.assertEqual(segmentation[0].shape ,				UpperCAmelCase_)












def   lowerCAmelCase_				( __a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   List[str]    						=""
  for ch in key:
    if ch == " " or ch not in key_no_dups and ch.isalpha():
      key_no_dups += ch
  return key_no_dups





def   lowerCAmelCase_				( __a	)  ->	dict[str, str]:







  """simple docstring"""





  lowerCamelCase__:   Dict    						=[chr(i + 65	) for i in range(26	)]
  # Remove duplicate characters from key
  lowerCamelCase__:   Union[str, Any]    						=remove_duplicates(key.upper()	)
  lowerCamelCase__:   List[Any]    						=len(__a	)
  # First fill cipher with key characters
  lowerCamelCase__:   Optional[Any]    						={alphabet[i]: char for i, char in enumerate(__a	)}
  # Then map remaining characters in alphabet to
  # the alphabet from the beginning
  for i in range(len(__a	)			,					26	):
    lowerCamelCase__:   Optional[int]    						=alphabet[i - offset]
    # Ensure we are not mapping letters to letters previously mapped
    while char in key:
      offset -= 1
      lowerCamelCase__:   List[Any]    						=alphabet[i - offset]
    lowerCamelCase__:   str    						=char
  return cipher_alphabet





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





  return "".join(cipher_map.get(__a			,					__a	) for ch in message.upper()	)





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   List[Any]    						={v: k for k, v in cipher_map.items()}
  return "".join(rev_cipher_map.get(__a			,					__a	) for ch in message.upper()	)





def   lowerCAmelCase_				( )  ->	None:







  """simple docstring"""





  lowerCamelCase__:   Optional[Any]    						=input("Enter message to encode or decode: "	).strip()
  lowerCamelCase__:   Dict    						=input("Enter keyword: "	).strip()
  lowerCamelCase__:   Any    						=input("Encipher or decipher? E/D:"	).strip()[0].lower()
  try:
    lowerCamelCase__:   Union[str, Any]    						={"e": encipher, "d": decipher}[option]
  except KeyError:
    raise KeyError("invalid input option"	)
  lowerCamelCase__:   List[Any]    						=create_cipher_map(__a	)
  print(func(__a			,					__a	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

