
from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
			    TFDebertaVaForMaskedLM,
			    TFDebertaVaForQuestionAnswering,
			    TFDebertaVaForSequenceClassification,
			    TFDebertaVaForTokenClassification,
			    TFDebertaVaModel,
			)


class        lowercase      :



		def __init__(				self       ,   A_       ,   A_=13       ,   A_=7       ,   A_=True       ,   A_=True       ,   A_=True       ,   A_=True       ,   A_=99       ,   A_=32       ,   A_=2       ,   A_=4       ,   A_=37       ,   A_="gelu"       ,   A_=0.1       ,   A_=0.1       ,   A_=512       ,   A_=16       ,   A_=2       ,   A_=0.02       ,   A_=False       ,   A_=True       ,   A_="None"       ,   A_=3       ,   A_=4       ,   A_=None       ,   )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  seq_length
				UpperCamelCase						=  is_training
				UpperCamelCase						=  use_input_mask
				UpperCamelCase						=  use_token_type_ids
				UpperCamelCase						=  use_labels
				UpperCamelCase						=  vocab_size
				UpperCamelCase						=  hidden_size
				UpperCamelCase						=  num_hidden_layers
				UpperCamelCase						=  num_attention_heads
				UpperCamelCase						=  intermediate_size
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  hidden_dropout_prob
				UpperCamelCase						=  attention_probs_dropout_prob
				UpperCamelCase						=  max_position_embeddings
				UpperCamelCase						=  type_vocab_size
				UpperCamelCase						=  type_sequence_label_size
				UpperCamelCase						=  initializer_range
				UpperCamelCase						=  num_labels
				UpperCamelCase						=  num_choices
				UpperCamelCase						=  relative_attention
				UpperCamelCase						=  position_biased_input
				UpperCamelCase						=  pos_att_type
				UpperCamelCase						=  scope



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size )

				UpperCamelCase						=  None
				if self.use_input_mask:
						UpperCamelCase						=  random_attention_mask([self.batch_size, self.seq_length] )

				UpperCamelCase						=  None
				if self.use_token_type_ids:
						UpperCamelCase						=  ids_tensor([self.batch_size, self.seq_length]       ,   self.type_vocab_size )

				UpperCamelCase						=  None
				UpperCamelCase						=  None
				UpperCamelCase						=  None
				if self.use_labels:
						UpperCamelCase						=  ids_tensor([self.batch_size]       ,   self.type_sequence_label_size )
						UpperCamelCase						=  ids_tensor([self.batch_size, self.seq_length]       ,   self.num_labels )

				UpperCamelCase						=  DebertaVaConfig(
				    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       ,   position_biased_input=self.position_biased_input       ,   initializer_range=self.initializer_range       ,   return_dict=__UpperCamelCase       ,   )

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_ )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  TFDebertaVaModel(config=__UpperCamelCase )
				UpperCamelCase						=  {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

				UpperCamelCase						=  [input_ids, input_mask]
				UpperCamelCase						=  model(__UpperCamelCase )

				UpperCamelCase						=  model(__UpperCamelCase )

				self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  TFDebertaVaForMaskedLM(config=__UpperCamelCase )
				UpperCamelCase						=  {
				    'input_ids': input_ids,
				    'attention_mask': input_mask,
				    'token_type_ids': token_type_ids,
				}
				UpperCamelCase						=  model(__UpperCamelCase )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.vocab_size) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.num_labels
				UpperCamelCase						=  TFDebertaVaForSequenceClassification(config=__UpperCamelCase )
				UpperCamelCase						=  {
				    'input_ids': input_ids,
				    'attention_mask': input_mask,
				    'token_type_ids': token_type_ids,
				}

				UpperCamelCase						=  model(__UpperCamelCase )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.num_labels
				UpperCamelCase						=  TFDebertaVaForTokenClassification(config=__UpperCamelCase )
				UpperCamelCase						=  {
				    'input_ids': input_ids,
				    'attention_mask': input_mask,
				    'token_type_ids': token_type_ids,
				}
				UpperCamelCase						=  model(__UpperCamelCase )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.num_labels) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_ )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  TFDebertaVaForQuestionAnswering(config=__UpperCamelCase )
				UpperCamelCase						=  {
				    'input_ids': input_ids,
				    'attention_mask': input_mask,
				    'token_type_ids': token_type_ids,
				}

				UpperCamelCase						=  model(__UpperCamelCase )
				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 __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				(
				    (
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						(
				    UpperCamelCase
				)			,						
				)						=  config_and_inputs
				UpperCamelCase						=  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
				return config, inputs_dict






@require_tf
class        lowercase      (	_UpperCAmelCase						,     _UpperCAmelCase						,     unittest.TestCase     ):
		__lowercase : List[str]			  =       (
		    (
		        TFDebertaVaModel,
		        TFDebertaVaForMaskedLM,
		        TFDebertaVaForQuestionAnswering,
		        TFDebertaVaForSequenceClassification,
		        TFDebertaVaForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		__lowercase : str			  =       (
		    {
		        'feature-extraction': TFDebertaVaModel,
		        'fill-mask': TFDebertaVaForMaskedLM,
		        'question-answering': TFDebertaVaForQuestionAnswering,
		        'text-classification': TFDebertaVaForSequenceClassification,
		        'token-classification': TFDebertaVaForTokenClassification,
		        'zero-shot': TFDebertaVaForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)

		__lowercase : int			  =       False
		__lowercase : Tuple			  =       False



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  TFDebertaVaModelTester(self )
				UpperCamelCase						=  ConfigTester(self       ,   config_class=__UpperCamelCase       ,   hidden_size=37 )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )



		@slow
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
				self.assertIsNotNone(__UpperCamelCase )






@require_tf
class        lowercase      (	unittest.TestCase     ):



		@unittest.skip(reason='Model not available yet' )
		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
				UpperCamelCase						=  tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
				UpperCamelCase						=  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
				UpperCamelCase						=  model(__UpperCamelCase       ,   attention_mask=__UpperCamelCase )[0]

				UpperCamelCase						=  tf.constant(
				    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
				tf.debugging.assert_near(output[:, 1:4, 1:4]       ,   __UpperCamelCase       ,   atol=1e-4 )


from string import ascii_uppercase

_UpperCAmelCase      :				Dict          = {char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase      :				Tuple          = dict(enumerate(ascii_uppercase))


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  len(lowercase				)
		UpperCamelCase						=  0
		while True:
				if x == i:
						UpperCamelCase						=  0
				if len(lowercase				) == len(lowercase				):
						break
				key += key[i]
				i += 1
		return key


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''
		UpperCamelCase						=  0
		for letter in message:
				if letter == " ":
						cipher_text += " "
				else:
						UpperCamelCase						=  (dicta[letter] - dicta[key_new[i]]) % 26
						i += 1
						cipher_text += dicta[x]
		return cipher_text


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''
		UpperCamelCase						=  0
		for letter in cipher_text:
				if letter == " ":
						or_txt += " "
				else:
						UpperCamelCase						=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
						i += 1
						or_txt += dicta[x]
		return or_txt


def    A (      )     ->						None:
		'''simple docstring'''

		UpperCamelCase						=  'THE GERMAN ATTACK'
		UpperCamelCase						=  'SECRET'
		UpperCamelCase						=  generate_key(lowercase					, lowercase				)
		UpperCamelCase						=  cipher_text(lowercase					, lowercase				)
		print(f'''Encrypted Text = {s}'''				)
		print(f'''Original Text = {original_text(lowercase					, lowercase				)}'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
