


import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType



@require_torch
class 							a       :
     UpperCAmelCase__			:	Tuple       =						()
     UpperCAmelCase__			:	List[str]       =						True





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int				):
        __lowerCamelCase:					Tuple	 =						self.config_class

        # test default config
        __lowerCamelCase:					Dict	 =						config_class()
        self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Tuple	 =						["""stem"""] + [F'stage{idx}' for idx in range(1     ,						len(config.depths				) + 1				)]
        self.assertEqual(config.stage_names     ,						SCREAMING_SNAKE_CASE_				)
        self.assertTrue(set(config.out_features				).issubset(set(config.stage_names				)				)				)

        # Test out_features and out_indices are correctly set
        # out_features and out_indices both None
        __lowerCamelCase:					Tuple	 =						config_class(out_features=SCREAMING_SNAKE_CASE_     ,						out_indices=SCREAMING_SNAKE_CASE_				)
        self.assertEqual(config.out_features     ,						[config.stage_names[-1]]				)
        self.assertEqual(config.out_indices     ,						[len(config.stage_names				) - 1]				)

        # out_features and out_indices both set
        __lowerCamelCase:					Tuple	 =						config_class(out_features=["""stem""", """stage1"""]     ,						out_indices=[0, 1]				)
        self.assertEqual(config.out_features     ,						["""stem""", """stage1"""]				)
        self.assertEqual(config.out_indices     ,						[0, 1]				)

        # Only out_features set
        __lowerCamelCase:					List[Any]	 =						config_class(out_features=["""stage1""", """stage3"""]				)
        self.assertEqual(config.out_features     ,						["""stage1""", """stage3"""]				)
        self.assertEqual(config.out_indices     ,						[1, 3]				)

        # Only out_indices set
        __lowerCamelCase:					Any	 =						config_class(out_indices=[0, 2]				)
        self.assertEqual(config.out_features     ,						[config.stage_names[0], config.stage_names[2]]				)
        self.assertEqual(config.out_indices     ,						[0, 2]				)

        # Error raised when out_indices do not correspond to out_features
        with self.assertRaises(SCREAMING_SNAKE_CASE_				):
           __lowerCamelCase:					str	 =						config_class(out_features=["""stage1""", """stage2"""]     ,						out_indices=[0, 2]				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        __lowerCamelCase ,  __lowerCamelCase:					str	 =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:					List[Any]	 =						model_class(SCREAMING_SNAKE_CASE_				)
           __lowerCamelCase:					Optional[int]	 =						inspect.signature(model.forward				)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           __lowerCamelCase:					str	 =						[*signature.parameters.keys()]
           __lowerCamelCase:					Optional[Any]	 =						["""pixel_values"""]
           self.assertListEqual(arg_names[:1]     ,						SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        __lowerCamelCase ,  __lowerCamelCase:					Optional[int]	 =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:					Optional[Any]	 =						model_class(SCREAMING_SNAKE_CASE_				)
           self.assertEqual(len(model.channels				)     ,						len(config.out_features				)				)
           __lowerCamelCase:					Optional[int]	 =						model.num_features
           __lowerCamelCase:					Any	 =						[config.stage_names.index(SCREAMING_SNAKE_CASE_				) for feat in config.out_features]
           __lowerCamelCase:					Tuple	 =						[num_features[idx] for idx in out_indices]
           self.assertListEqual(model.channels     ,						SCREAMING_SNAKE_CASE_				)

           __lowerCamelCase:					Optional[Any]	 =						copy.deepcopy(SCREAMING_SNAKE_CASE_				)
           __lowerCamelCase:					int	 =						None
           __lowerCamelCase:					Any	 =						model_class(SCREAMING_SNAKE_CASE_				)
           self.assertEqual(len(model.channels				)     ,						1				)
           self.assertListEqual(model.channels     ,						[num_features[-1]]				)

           __lowerCamelCase:					Tuple	 =						copy.deepcopy(SCREAMING_SNAKE_CASE_				)
           __lowerCamelCase:					str	 =						None
           __lowerCamelCase:					List[Any]	 =						model_class(SCREAMING_SNAKE_CASE_				)
           self.assertEqual(len(model.channels				)     ,						1				)
           self.assertListEqual(model.channels     ,						[num_features[-1]]				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        __lowerCamelCase ,  __lowerCamelCase:					Tuple	 =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:					List[Any]	 =						model_class(SCREAMING_SNAKE_CASE_				)
           model.to(SCREAMING_SNAKE_CASE_				)
           model.eval()
           __lowerCamelCase:					Tuple	 =						model(**SCREAMING_SNAKE_CASE_				)

           self.assertEqual(len(result.feature_maps				)     ,						len(config.out_features				)				)
           self.assertEqual(len(model.channels				)     ,						len(config.out_features				)				)
           self.assertEqual(len(result.feature_maps				)     ,						len(config.out_indices				)				)
           self.assertEqual(len(model.channels				)     ,						len(config.out_indices				)				)

           # Check output of last stage is taken if out_features=None, out_indices=None
           __lowerCamelCase:					List[Any]	 =						copy.deepcopy(SCREAMING_SNAKE_CASE_				)
           __lowerCamelCase:					Optional[Any]	 =						None
           __lowerCamelCase:					List[str]	 =						model_class(SCREAMING_SNAKE_CASE_				)
           model.to(SCREAMING_SNAKE_CASE_				)
           model.eval()
           __lowerCamelCase:					Optional[Any]	 =						model(**SCREAMING_SNAKE_CASE_				)

           self.assertEqual(len(result.feature_maps				)     ,						1				)
           self.assertEqual(len(model.channels				)     ,						1				)

           __lowerCamelCase:					str	 =						copy.deepcopy(SCREAMING_SNAKE_CASE_				)
           __lowerCamelCase:					int	 =						None
           __lowerCamelCase:					str	 =						model_class(SCREAMING_SNAKE_CASE_				)
           model.to(SCREAMING_SNAKE_CASE_				)
           model.eval()
           __lowerCamelCase:					Union[str, Any]	 =						model(**SCREAMING_SNAKE_CASE_				)

           self.assertEqual(len(result.feature_maps				)     ,						1				)
           self.assertEqual(len(model.channels				)     ,						1				)

           # Check backbone can be initialized with fresh weights
           __lowerCamelCase:					int	 =						copy.deepcopy(SCREAMING_SNAKE_CASE_				)
           __lowerCamelCase:					Optional[int]	 =						False
           __lowerCamelCase:					Optional[Any]	 =						model_class(SCREAMING_SNAKE_CASE_				)
           model.to(SCREAMING_SNAKE_CASE_				)
           model.eval()
           __lowerCamelCase:					str	 =						model(**SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int				):
        __lowerCamelCase ,  __lowerCamelCase:					List[Any]	 =						self.model_tester.prepare_config_and_inputs_for_common()

        for backbone_class in self.all_model_classes:
           __lowerCamelCase:					Optional[Any]	 =						backbone_class(SCREAMING_SNAKE_CASE_				)

           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""backbone_type"""				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""stage_names"""				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""num_features"""				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""out_indices"""				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""out_features"""				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""out_feature_channels"""				)				)
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,						"""channels"""				)				)

           self.assertIsInstance(backbone.backbone_type     ,						SCREAMING_SNAKE_CASE_				)
           # Verify num_features has been initialized in the backbone init
           self.assertIsNotNone(backbone.num_features				)
           self.assertTrue(len(backbone.channels				) == len(backbone.out_indices				)				)
           self.assertTrue(len(backbone.stage_names				) == len(backbone.num_features				)				)
           self.assertTrue(len(backbone.channels				) <= len(backbone.num_features				)				)
           self.assertTrue(len(backbone.out_feature_channels				) == len(backbone.stage_names				)				)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        __lowerCamelCase ,  __lowerCamelCase:					Union[str, Any]	 =						self.model_tester.prepare_config_and_inputs_for_common()
        __lowerCamelCase:					str	 =						inputs_dict["""pixel_values"""].shape[0]

        for backbone_class in self.all_model_classes:
           __lowerCamelCase:					Optional[Any]	 =						backbone_class(SCREAMING_SNAKE_CASE_				)
           backbone.to(SCREAMING_SNAKE_CASE_				)
           backbone.eval()

           __lowerCamelCase:					List[str]	 =						backbone(**SCREAMING_SNAKE_CASE_				)

           # Test default outputs and verify feature maps
           self.assertIsInstance(outputs.feature_maps     ,						SCREAMING_SNAKE_CASE_				)
           self.assertTrue(len(outputs.feature_maps				) == len(backbone.channels				)				)
           for feature_map, n_channels in zip(outputs.feature_maps     ,						backbone.channels				):
              self.assertTrue(feature_map.shape[:2]     ,						(batch_size, n_channels)				)
           self.assertIsNone(outputs.hidden_states				)
           self.assertIsNone(outputs.attentions				)

           # Test output_hidden_states=True
           __lowerCamelCase:					Any	 =						backbone(**SCREAMING_SNAKE_CASE_     ,						output_hidden_states=SCREAMING_SNAKE_CASE_				)
           self.assertIsNotNone(outputs.hidden_states				)
           self.assertTrue(len(outputs.hidden_states				)     ,						len(backbone.stage_names				)				)
           for hidden_state, n_channels in zip(outputs.hidden_states     ,						backbone.channels				):
              self.assertTrue(hidden_state.shape[:2]     ,						(batch_size, n_channels)				)

           # Test output_attentions=True
           if self.has_attentions:
              __lowerCamelCase:					int	 =						backbone(**SCREAMING_SNAKE_CASE_     ,						output_attentions=SCREAMING_SNAKE_CASE_				)
              self.assertIsNotNone(outputs.attentions				)






from string import ascii_uppercase



def      __lowerCAmelCase    (				snake_case						:     str    ,   snake_case						:     str    ,   snake_case						:     bool = False    ,   snake_case						:     str = ascii_uppercase			)							->				str:
   __lowerCamelCase:					List[str]	 =						keyword.upper()
   __lowerCamelCase:					Tuple	 =						plaintext.upper()
   __lowerCamelCase:					Optional[Any]	 =						set(snake_case			)

   # create a list of unique characters in the keyword - their order matters
   # it determines how we will map plaintext characters to the ciphertext
   __lowerCamelCase:					Tuple	 =						[]
   for char in keyword:
      if char in alphabet_set and char not in unique_chars:
         unique_chars.append(snake_case			)
    # the number of those unique characters will determine the number of rows
   __lowerCamelCase:					Tuple	 =						len(snake_case			)

   # create a shifted version of the alphabet
   __lowerCamelCase:					List[str]	 =						unique_chars + [
       char for char in alphabet if char not in unique_chars
   ]

   # create a modified alphabet by splitting the shifted alphabet into rows
   __lowerCamelCase:					List[str]	 =						[
       shifted_alphabet[k : k + num_unique_chars_in_keyword]
       for k in range(0    ,   26    ,   snake_case			)
   ]

   # map the alphabet characters to the modified alphabet characters
   # going 'vertically' through the modified alphabet - consider columns first
   __lowerCamelCase:					Union[str, Any]	 =						{}
   __lowerCamelCase:					str	 =						0
   for column in range(snake_case			):
      for row in modified_alphabet:
         # if current row (the last one) is too short, break out of loop
         if len(snake_case			) <= column:
            break

         # map current letter to letter in modified alphabet
         __lowerCamelCase:					Union[str, Any]	 =						row[column]
         letter_index += 1

   if verbose:
      print(snake_case			)
   # create the encrypted text by mapping the plaintext to the modified alphabet
   return "".join(mapping[char] if char in mapping else char for char in plaintext			)


if __name__ == "__main__":
   # example use
   print(mixed_keyword('''college''', '''UNIVERSITY'''))


