





import inspect
import unittest

from transformers import ViTMSNConfig
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 torch import nn

    from transformers import ViTMSNForImageClassification, ViTMSNModel
    from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor



class 					A__   :





      def __init__( self       ,							A_       ,							A_=13       ,							A_=30       ,							A_=2       ,							A_=3       ,							A_=True       ,							A_=True       ,							A_=32       ,							A_=5       ,							A_=4       ,							A_=37       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=10       ,							A_=0.02       ,							A_=None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   parent
       UpperCamelCase	:					Optional[Any]       	=   batch_size
       UpperCamelCase	:					Optional[Any]       	=   image_size
       UpperCamelCase	:					Any       	=   patch_size
       UpperCamelCase	:					Any       	=   num_channels
       UpperCamelCase	:					Union[str, Any]       	=   is_training
       UpperCamelCase	:					Union[str, Any]       	=   use_labels
       UpperCamelCase	:					int       	=   hidden_size
       UpperCamelCase	:					Tuple       	=   num_hidden_layers
       UpperCamelCase	:					Union[str, Any]       	=   num_attention_heads
       UpperCamelCase	:					Dict       	=   intermediate_size
       UpperCamelCase	:					int       	=   hidden_act
       UpperCamelCase	:					Optional[int]       	=   hidden_dropout_prob
       UpperCamelCase	:					Dict       	=   attention_probs_dropout_prob
       UpperCamelCase	:					Union[str, Any]       	=   type_sequence_label_size
       UpperCamelCase	:					Tuple       	=   initializer_range
       UpperCamelCase	:					List[str]       	=   scope

       # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase	:					Any       	=   (image_size // patch_size) ** 2
       UpperCamelCase	:					List[str]       	=   num_patches + 1





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

       UpperCamelCase	:					str       	=   None
       if self.use_labels:
        UpperCamelCase	:					Union[str, Any]       	=   ids_tensor([self.batch_size]       ,							self.type_sequence_label_size   )

       UpperCamelCase	:					Optional[int]       	=   self.get_config()

       return config, pixel_values, labels





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return ViTMSNConfig(
           image_size=self.image_size       ,							patch_size=self.patch_size       ,							num_channels=self.num_channels       ,							hidden_size=self.hidden_size       ,							num_hidden_layers=self.num_hidden_layers       ,							num_attention_heads=self.num_attention_heads       ,							intermediate_size=self.intermediate_size       ,							hidden_act=self.hidden_act       ,							hidden_dropout_prob=self.hidden_dropout_prob       ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,							initializer_range=self.initializer_range       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   ViTMSNModel(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					Union[str, Any]       	=   model(A_   )
       self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.type_sequence_label_size
       UpperCamelCase	:					Optional[int]       	=   ViTMSNForImageClassification(A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					str       	=   model(A_       ,							labels=A_   )
       print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"   )
       print("Labels: {labels}"   )
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)   )

       # test greyscale images
       UpperCamelCase	:					Tuple       	=   1
       UpperCamelCase	:					List[str]       	=   ViTMSNForImageClassification(A_   )
       model.to(A_   )
       model.eval()

       UpperCamelCase	:					List[Any]       	=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
       UpperCamelCase	:					str       	=   model(A_   )
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.prepare_config_and_inputs()
       UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   config_and_inputs
       UpperCamelCase	:					List[Any]       	=   {"pixel_values": pixel_values}
       return config, inputs_dict



@require_torch
class 					A__   (       __snake_case       ,   __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Optional[Any]						    =   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
      _UpperCAmelCase	:Optional[Any]						    =   (
          {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
          if is_torch_available()
          else {}
      )

      _UpperCAmelCase	:int						    =   False
      _UpperCAmelCase	:Union[str, Any]						    =   False
      _UpperCAmelCase	:Any						    =   False
      _UpperCAmelCase	:Union[str, Any]						    =   False





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   ViTMSNModelTester(self   )
       UpperCamelCase	:					Dict       	=   ConfigTester(self       ,							config_class=A_       ,							has_text_modality=A_       ,							hidden_size=37   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      @unittest.skip(reason="ViTMSN does not use inputs_embeds"   )
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       pass





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCamelCase	:					Union[str, Any]       	=   model_class(A_   )
        self.assertIsInstance(model.get_input_embeddings()       ,							(nn.Module)   )
        UpperCamelCase	:					List[str]       	=   model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(A_       ,							nn.Linear   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	:					List[Any]       	=   self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase	:					Any       	=   ["pixel_values"]
        self.assertListEqual(arg_names[:1]       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase	:					Union[str, Any]       	=   ViTMSNModel.from_pretrained(A_   )
        self.assertIsNotNone(A_   )



def 		A_				(     )						-> Tuple:
 UpperCamelCase	:					Optional[int]       	=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
 return image



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





      @cached_property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"   ) if is_vision_available() else None





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       torch.manual_seed(2   )
       UpperCamelCase	:					Union[str, Any]       	=   ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"   ).to(A_   )

       UpperCamelCase	:					Any       	=   self.default_image_processor
       UpperCamelCase	:					List[str]       	=   prepare_img()
       UpperCamelCase	:					Union[str, Any]       	=   image_processor(images=A_       ,							return_tensors="pt"   ).to(A_   )

       # forward pass
       with torch.no_grad():
        UpperCamelCase	:					Optional[int]       	=   model(**A_   )

       # verify the logits
       UpperCamelCase	:					Dict       	=   torch.Size((1, 1000)   )
       self.assertEqual(outputs.logits.shape       ,							A_   )

       UpperCamelCase	:					Any       	=   torch.tensor([-0.08_03, -0.44_54, -0.23_75]   ).to(A_   )

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







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



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 return "".join(cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Union[str, Any]       	=   {v: k for k, v in cipher_map.items()}
 return "".join(rev_cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     )						-> None:
 UpperCamelCase	:					int       	=   input("Enter message to encode or decode: "     ).strip()
 UpperCamelCase	:					str       	=   input("Enter keyword: "     ).strip()
 UpperCamelCase	:					Union[str, Any]       	=   input("Encipher or decipher? E/D:"     ).strip()[0].lower()
 try:
  UpperCamelCase	:					List[str]       	=   {"e": encipher, "d": decipher}[option]
 except KeyError:
  raise KeyError("invalid input option"     )
 UpperCamelCase	:					str       	=   create_cipher_map(_lowerCAmelCase     )
 print(func(_lowerCAmelCase							,   _lowerCAmelCase     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
