



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      UpperCamelCase    :



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



	def 			__A							(       self		):
							A__							=       ids_tensor([self.batch_size, self.seq_length]			,	self.vocab_size		)

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

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

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

							A__							=       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 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       TFDebertaVaModel(config=UpperCAmelCase__		)
							A__							=       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

							A__							=       [input_ids, input_mask]
							A__							=       model(UpperCAmelCase__		)

							A__							=       model(UpperCAmelCase__		)

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



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       TFDebertaVaForMaskedLM(config=UpperCAmelCase__		)
							A__							=       {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							A__							=       model(UpperCAmelCase__		)
							self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.vocab_size)		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       self.num_labels
							A__							=       TFDebertaVaForSequenceClassification(config=UpperCAmelCase__		)
							A__							=       {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							A__							=       model(UpperCAmelCase__		)
							self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels)		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       self.num_labels
							A__							=       TFDebertaVaForTokenClassification(config=UpperCAmelCase__		)
							A__							=       {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							A__							=       model(UpperCAmelCase__		)
							self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.num_labels)		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       TFDebertaVaForQuestionAnswering(config=UpperCAmelCase__		)
							A__							=       {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							A__							=       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 			__A							(       self		):
							A__							=       self.prepare_config_and_inputs()
							(
							    (
							    A__
							)						,       (
							    A__
							)						,       (
							    A__
							)						,       (
							    A__
							)						,       (
							    A__
							)						,       (
							    A__
							)						,       (
							    A__
							)						,       
							)							=       config_and_inputs
							A__							=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict






@require_tf
class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase       ,    unittest.TestCase     ):
	lowerCAmelCase	:   Optional[Any]				=	(
	    (
	        TFDebertaVaModel,
	        TFDebertaVaForMaskedLM,
	        TFDebertaVaForQuestionAnswering,
	        TFDebertaVaForSequenceClassification,
	        TFDebertaVaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	lowerCAmelCase	:   Union[str, Any]				=	(
	    {
	        """feature-extraction""": TFDebertaVaModel,
	        """fill-mask""": TFDebertaVaForMaskedLM,
	        """question-answering""": TFDebertaVaForQuestionAnswering,
	        """text-classification""": TFDebertaVaForSequenceClassification,
	        """token-classification""": TFDebertaVaForTokenClassification,
	        """zero-shot""": TFDebertaVaForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	lowerCAmelCase	:   Any				=	False
	lowerCAmelCase	:   Optional[Any]				=	False



	def 			__A							(       self		):
							A__							=       TFDebertaVaModelTester(self		)
							A__							=       ConfigTester(self			,	config_class=UpperCAmelCase__			,	hidden_size=37		)



	def 			__A							(       self		):
							self.config_tester.run_common_tests()



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__		)



	@slow
	def 			__A							(       self		):
							A__							=       TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"		)
							self.assertIsNotNone(UpperCAmelCase__		)






@require_tf
class      UpperCamelCase    (						unittest.TestCase     ):



	@unittest.skip(reason="Model not available yet"		)
	def 			__A							(       self		):
							pass



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

							A__							=       tf.constant(
							    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]		)
							tf.debugging.assert_near(output[:, 1:4, 1:4]			,	UpperCAmelCase__			,	atol=1e-4		)










def  UpperCamelCase						(       _A			:				str    )->       str:





						"""simple docstring"""





						A__							=       ""
						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  UpperCamelCase						(       _A			:				str    )->       dict[str, str]:





						"""simple docstring"""





						A__							=       [chr(i + 65    ) for i in range(26    )]
						# Remove duplicate characters from key
						A__							=       remove_duplicates(key.upper()    )
						A__							=       len(_A    )
						# First fill cipher with key characters
						A__							=       {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    ):
												A__							=       alphabet[i - offset]
												# Ensure we are not mapping letters to letters previously mapped
												while char in key:
																		offset -= 1
																		A__							=       alphabet[i - offset]
												A__							=       char
						return cipher_alphabet
def  UpperCamelCase						(       _A			:				str			,							_A			:				dict[str, str]    )->       str:





						"""simple docstring"""





						return "".join(cipher_map.get(_A			,							_A    ) for ch in message.upper()    )
def  UpperCamelCase						(       _A			:				str			,							_A			:				dict[str, str]    )->       str:





						"""simple docstring"""





						A__							=       {v: k for k, v in cipher_map.items()}
						return "".join(rev_cipher_map.get(_A			,							_A    ) for ch in message.upper()    )
def  UpperCamelCase						(       )->       None:





						"""simple docstring"""





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


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()





