code: 
  
  
  
  
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import DebertaConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
       import tensorflow as tf
  
       from transformers import (
           TFDebertaForMaskedLM,
           TFDebertaForQuestionAnswering,
           TFDebertaForSequenceClassification,
           TFDebertaForTokenClassification,
           TFDebertaModel,
       )
  
  
  
  
  
  
  
  class        __lowerCAmelCase :
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=1_3	,   lowerCAmelCase__=7	,   lowerCAmelCase__=True	,   lowerCAmelCase__=True	,   lowerCAmelCase__=True	,   lowerCAmelCase__=True	,   lowerCAmelCase__=9_9	,   lowerCAmelCase__=3_2	,   lowerCAmelCase__=2	,   lowerCAmelCase__=4	,   lowerCAmelCase__=3_7	,   lowerCAmelCase__="gelu"	,   lowerCAmelCase__=0.1	,   lowerCAmelCase__=0.1	,   lowerCAmelCase__=5_1_2	,   lowerCAmelCase__=1_6	,   lowerCAmelCase__=2	,   lowerCAmelCase__=0.02	,   lowerCAmelCase__=3	,   lowerCAmelCase__=4	,   lowerCAmelCase__=None	,   )     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =parent
          a__			:      int            =1_3
          a__			:      Optional[int]            =7
          a__			:      Optional[Any]            =True
          a__			:      Tuple            =True
          a__			:      str            =True
          a__			:      Union[str, Any]            =True
          a__			:      Optional[Any]            =9_9
          a__			:      int            =3_2
          a__			:      Any            =2
          a__			:      List[Any]            =4
          a__			:      Optional[int]            =3_7
          a__			:      List[Any]            ="gelu"
          a__			:      Dict            =0.1
          a__			:      Optional[Any]            =0.1
          a__			:      int            =5_1_2
          a__			:      Any            =1_6
          a__			:      int            =False
          a__			:      Dict            =-1
          a__			:      int            =True
          a__			:      int            =2
          a__			:      int            =0.02
          a__			:      Optional[int]            =3
          a__			:      Any            =4
          a__			:      List[Any]            =None
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size							)
  
          a__			:      Any            =None
          if self.use_input_mask:
            a__			:      str            =random_attention_mask([self.batch_size, self.seq_length]							)
  
          a__			:      Any            =None
          if self.use_token_type_ids:
            a__			:      List[str]            =ids_tensor([self.batch_size, self.seq_length]	,   self.type_vocab_size							)
  
          a__			:      List[Any]            =None
          a__			:      Tuple            =None
          a__			:      List[str]            =None
          if self.use_labels:
            a__			:      Optional[int]            =ids_tensor([self.batch_size]	,   self.type_sequence_label_size							)
            a__			:      int            =ids_tensor([self.batch_size, self.seq_length]	,   self.num_labels							)
  
          a__			:      int            =DebertaConfig(
              vocab_size=self.vocab_size	,   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	,   max_position_embeddings=self.max_position_embeddings	,   type_vocab_size=self.type_vocab_size	,   relative_attention=self.relative_attention	,   max_relative_positions=self.max_relative_positions	,   position_biased_input=self.position_biased_input	,   initializer_range=self.initializer_range	,   return_dict=lowerCAmelCase__	,   )
  
          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[int]            =TFDebertaModel(config=lowerCAmelCase__							)
          a__			:      Any            ={"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
  
          a__			:      Any            =[input_ids, input_mask]
          a__			:      Tuple            =model(lowerCAmelCase__							)
  
          a__			:      str            =model(lowerCAmelCase__							)
  
          self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =TFDebertaForMaskedLM(config=lowerCAmelCase__							)
          a__			:      str            ={
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          a__			:      Optional[int]            =model(lowerCAmelCase__							)
          self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.vocab_size)							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =self.num_labels
          a__			:      List[Any]            =TFDebertaForSequenceClassification(config=lowerCAmelCase__							)
          a__			:      Union[str, Any]            ={
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
  
          a__			:      str            =model(lowerCAmelCase__							)
          self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels)							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =self.num_labels
          a__			:      Tuple            =TFDebertaForTokenClassification(config=lowerCAmelCase__							)
          a__			:      Optional[int]            ={
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          a__			:      Optional[int]            =model(lowerCAmelCase__							)
          self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.num_labels)							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[int]            =TFDebertaForQuestionAnswering(config=lowerCAmelCase__							)
          a__			:      int            ={
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
  
          a__			:      Optional[int]            =model(lowerCAmelCase__							)
          self.parent.assertEqual(result.start_logits.shape	,   (self.batch_size, self.seq_length)							)
          self.parent.assertEqual(result.end_logits.shape	,   (self.batch_size, self.seq_length)							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =self.prepare_config_and_inputs()
          (
              (
              a__
          )      ,							(
              a__
          )      ,							(
              a__
          )      ,							(
              a__
          )      ,							(
              a__
          )      ,							(
              a__
          )      ,							(
              a__
          )      ,							
          )			:      int            =config_and_inputs
          a__			:      List[str]            ={"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class        __lowerCAmelCase (					UpperCamelCase__		,       UpperCamelCase__		,       unittest.TestCase):
        _lowercase       :      str        =    (
            (
                TFDebertaModel,
                TFDebertaForMaskedLM,
                TFDebertaForQuestionAnswering,
                TFDebertaForSequenceClassification,
                TFDebertaForTokenClassification,
            )
            if is_tf_available()
            else ()
        )
        _lowercase       :      Dict        =    (
            {
                """feature-extraction""": TFDebertaModel,
                """fill-mask""": TFDebertaForMaskedLM,
                """question-answering""": TFDebertaForQuestionAnswering,
                """text-classification""": TFDebertaForSequenceClassification,
                """token-classification""": TFDebertaForTokenClassification,
                """zero-shot""": TFDebertaForSequenceClassification,
            }
            if is_tf_available()
            else {}
        )
  
        _lowercase       :      str        =    False
        _lowercase       :      Optional[Any]        =    False
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =TFDebertaModelTester(self							)
          a__			:      Tuple            =ConfigTester(self	,   config_class=lowerCAmelCase__	,   hidden_size=3_7							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          self.config_tester.run_common_tests()
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__							)
  
  
  
  
  
  
  
        @slow
        def      _lowercase						(					self							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =TFDebertaModel.from_pretrained("kamalkraj/deberta-base"							)
          self.assertIsNotNone(lowerCAmelCase__							)
  
  
  
  
  
  
  
  @require_tf
  class        __lowerCAmelCase (					unittest.TestCase):
  
  
  
  
  
  
  
        @unittest.skip(reason="Model not available yet"							)
        def      _lowercase						(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          pass
  
  
  
  
  
  
  
        @slow
        def      _lowercase						(					self							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =TFDebertaModel.from_pretrained("kamalkraj/deberta-base"							)
          a__			:      Optional[int]            =tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]							)
          a__			:      List[str]            =tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
          a__			:      Tuple            =model(lowerCAmelCase__	,   attention_mask=lowerCAmelCase__							)[0]
  
          a__			:      Dict            =tf.constant(
              [
                  [
                      [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
                      [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
                      [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
                  ]
              ]							)
          tf.debugging.assert_near(output[:, 1:4, 1:4]	,   lowerCAmelCase__	,   atol=1E-4							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__			:      Optional[int]            ="", "", ""
    a__			:      str            =[]
  
    for character in message_part:
      tmp.append(character_to_number[character]		)
  
    for each in tmp:
      one += each[0]
      two += each[1]
      three += each[2]
  
    return one + two + three
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__			:      Any            ="", ""
    a__			:      Optional[int]            =[]
  
    for character in message_part:
      this_part += character_to_number[character]
  
    for digit in this_part:
      tmp += digit
      if len(SCREAMING_SNAKE_CASE		) == len(SCREAMING_SNAKE_CASE		):
        result.append(SCREAMING_SNAKE_CASE		)
        a__			:      str            =""
  
    return result[0], result[1], result[2]
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[Any]            =alphabet.replace(" " , ""		).upper()
    a__			:      List[str]            =message.replace(" " , ""		).upper()
  
    # Check length and characters
    if len(SCREAMING_SNAKE_CASE		) != 27:
      raise KeyError("Length of alphabet has to be 27."		)
    for each in message:
      if each not in alphabet:
        raise ValueError("Each message character has to be included in alphabet!"		)
  
      # Generate dictionares
    a__			:      Any            =(
        "111",
        "112",
        "113",
        "121",
        "122",
        "123",
        "131",
        "132",
        "133",
        "211",
        "212",
        "213",
        "221",
        "222",
        "223",
        "231",
        "232",
        "233",
        "311",
        "312",
        "313",
        "321",
        "322",
        "323",
        "331",
        "332",
        "333",
    )
    a__			:      Tuple            ={}
    a__			:      List[str]            ={}
    for letter, number in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		):
      a__			:      Dict            =number
      a__			:      Union[str, Any]            =letter
  
    return message, alphabet, character_to_number, number_to_character
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__      ,							a__			:      Union[str, Any]            =__prepare(
        SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    a__      ,							a__			:      Union[str, Any]            ="", ""
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
      encrypted_numeric += __encrypt_part(
          message[i : i + period] , SCREAMING_SNAKE_CASE		)
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) , 3		):
      encrypted += number_to_character[encrypted_numeric[i : i + 3]]
  
    return encrypted
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__      ,							a__			:      Dict            =__prepare(
        SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    a__			:      Optional[Any]            =[]
    a__			:      Any            =""
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
      a__      ,							a__      ,							a__			:      Union[str, Any]            =__decrypt_part(message[i : i + period] , SCREAMING_SNAKE_CASE		)
  
      for j in range(0 , len(SCREAMING_SNAKE_CASE		)		):
        decrypted_numeric.append(a[j] + b[j] + c[j]		)
  
    for each in decrypted_numeric:
      decrypted += number_to_character[each]
  
    return decrypted
  
  
  if __name__ == "__main__":
       UpperCAmelCase			:			List[str]	  = """DEFEND THE EAST WALL OF THE CASTLE."""
       UpperCAmelCase			:			Optional[Any]	  = encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
       UpperCAmelCase			:			Optional[int]	  = decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
       print(F"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")
  
  
  
  
  
  
  