






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 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		Optional[int]						,   __a :		List[str]						,   __a :		int=13						,   __a :		List[str]=7						,   __a :		str=True						,   __a :		Optional[int]=True						,   __a :		int=True						,   __a :		Tuple=True						,   __a :		int=99						,   __a :		str=32						,   __a :		Union[str, Any]=2						,   __a :		Tuple=4						,   __a :		str=37						,   __a :		List[Any]="gelu"						,   __a :		List[Any]=0.1						,   __a :		List[Any]=0.1						,   __a :		str=512						,   __a :		Any=16						,   __a :		Tuple=2						,   __a :		Optional[Any]=0.02						,   __a :		Union[str, Any]=False						,   __a :		int=True						,   __a :		Dict="None"						,   __a :		Tuple=3						,   __a :		int=4						,   __a :		str=None						,   )	->     List[str]:


												"""simple docstring"""

												__lowercase					:							List[str]			=   parent
												__lowercase					:							List[str]			=   batch_size
												__lowercase					:							List[str]			=   seq_length
												__lowercase					:							Dict			=   is_training
												__lowercase					:							Any			=   use_input_mask
												__lowercase					:							Optional[int]			=   use_token_type_ids
												__lowercase					:							str			=   use_labels
												__lowercase					:							str			=   vocab_size
												__lowercase					:							int			=   hidden_size
												__lowercase					:							Optional[int]			=   num_hidden_layers
												__lowercase					:							str			=   num_attention_heads
												__lowercase					:							int			=   intermediate_size
												__lowercase					:							Tuple			=   hidden_act
												__lowercase					:							int			=   hidden_dropout_prob
												__lowercase					:							Any			=   attention_probs_dropout_prob
												__lowercase					:							Optional[Any]			=   max_position_embeddings
												__lowercase					:							Union[str, Any]			=   type_vocab_size
												__lowercase					:							str			=   type_sequence_label_size
												__lowercase					:							Optional[int]			=   initializer_range
												__lowercase					:							Dict			=   num_labels
												__lowercase					:							Tuple			=   num_choices
												__lowercase					:							List[Any]			=   relative_attention
												__lowercase					:							Optional[Any]			=   position_biased_input
												__lowercase					:							int			=   pos_att_type
												__lowercase					:							Optional[Any]			=   scope



					def 					lowerCAmelCase (     self :		List[str]							)	->     Any:


												"""simple docstring"""

												__lowercase					:							Any			=   ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size							)

												__lowercase					:							Optional[int]			=   None
												if self.use_input_mask:
																			__lowercase					:							List[Any]			=   random_attention_mask([self.batch_size, self.seq_length]							)

												__lowercase					:							List[str]			=   None
												if self.use_token_type_ids:
																			__lowercase					:							Union[str, Any]			=   ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size							)

												__lowercase					:							str			=   None
												__lowercase					:							Tuple			=   None
												__lowercase					:							Dict			=   None
												if self.use_labels:
																			__lowercase					:							Optional[Any]			=   ids_tensor([self.batch_size]						,   self.type_sequence_label_size							)
																			__lowercase					:							Optional[Any]			=   ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels							)

												__lowercase					:							List[str]			=   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=__a						,   )

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



					def 					lowerCAmelCase (     self :		Dict						,   __a :		Union[str, Any]						,   __a :		List[str]						,   __a :		Tuple						,   __a :		Dict						,   __a :		List[str]						,   __a :		Optional[int]						,   __a :		Dict							)	->     str:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   TFDebertaVaModel(config=__a							)
												__lowercase					:							Union[str, Any]			=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

												__lowercase					:							Any			=   [input_ids, input_mask]
												__lowercase					:							Union[str, Any]			=   model(__a							)

												__lowercase					:							Dict			=   model(__a							)

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



					def 					lowerCAmelCase (     self :		int						,   __a :		Union[str, Any]						,   __a :		List[Any]						,   __a :		str						,   __a :		Any						,   __a :		int						,   __a :		Dict						,   __a :		Dict							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Optional[int]			=   TFDebertaVaForMaskedLM(config=__a							)
												__lowercase					:							List[Any]			=   {
												    """input_ids""": input_ids,
												    """attention_mask""": input_mask,
												    """token_type_ids""": token_type_ids,
												}
												__lowercase					:							Any			=   model(__a							)
												self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		Dict						,   __a :		Optional[int]						,   __a :		List[str]						,   __a :		Any						,   __a :		List[Any]						,   __a :		Any						,   __a :		Any							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Tuple			=   self.num_labels
												__lowercase					:							int			=   TFDebertaVaForSequenceClassification(config=__a							)
												__lowercase					:							Optional[Any]			=   {
												    """input_ids""": input_ids,
												    """attention_mask""": input_mask,
												    """token_type_ids""": token_type_ids,
												}

												__lowercase					:							int			=   model(__a							)
												self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)							)



					def 					lowerCAmelCase (     self :		int						,   __a :		List[Any]						,   __a :		Dict						,   __a :		Optional[Any]						,   __a :		List[Any]						,   __a :		Union[str, Any]						,   __a :		Optional[Any]						,   __a :		List[str]							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							str			=   self.num_labels
												__lowercase					:							List[Any]			=   TFDebertaVaForTokenClassification(config=__a							)
												__lowercase					:							Optional[Any]			=   {
												    """input_ids""": input_ids,
												    """attention_mask""": input_mask,
												    """token_type_ids""": token_type_ids,
												}
												__lowercase					:							Dict			=   model(__a							)
												self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.num_labels)							)



					def 					lowerCAmelCase (     self :		Dict						,   __a :		int						,   __a :		Dict						,   __a :		Any						,   __a :		int						,   __a :		Tuple						,   __a :		List[str]						,   __a :		int							)	->     int:


												"""simple docstring"""

												__lowercase					:							Dict			=   TFDebertaVaForQuestionAnswering(config=__a							)
												__lowercase					:							Any			=   {
												    """input_ids""": input_ids,
												    """attention_mask""": input_mask,
												    """token_type_ids""": token_type_ids,
												}

												__lowercase					:							Dict			=   model(__a							)
												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 					lowerCAmelCase (     self :		int							)	->     Optional[int]:


												"""simple docstring"""

												__lowercase					:							Any			=   self.prepare_config_and_inputs()
												(
												    (
												    __lowercase
												)	,	(
												    __lowercase
												)	,	(
												    __lowercase
												)	,	(
												    __lowercase
												)	,	(
												    __lowercase
												)	,	(
												    __lowercase
												)	,	(
												    __lowercase
												)	,	
												)					:							str			=   config_and_inputs
												__lowercase					:							List[Any]			=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
												return config, inputs_dict







@require_tf
class 						lowerCAmelCase   (       __a					,				__a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	Dict     =					(
					    (
					        TFDebertaVaModel,
					        TFDebertaVaForMaskedLM,
					        TFDebertaVaForQuestionAnswering,
					        TFDebertaVaForSequenceClassification,
					        TFDebertaVaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					_A  :	Tuple     =					(
					    {
					        '''feature-extraction''': TFDebertaVaModel,
					        '''fill-mask''': TFDebertaVaForMaskedLM,
					        '''question-answering''': TFDebertaVaForQuestionAnswering,
					        '''text-classification''': TFDebertaVaForSequenceClassification,
					        '''token-classification''': TFDebertaVaForTokenClassification,
					        '''zero-shot''': TFDebertaVaForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					_A  :	Dict     =					False
					_A  :	Any     =					False



					def 					lowerCAmelCase (     self :		Dict							)	->     str:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   TFDebertaVaModelTester(self							)
												__lowercase					:							Union[str, Any]			=   ConfigTester(self						,   config_class=__a						,   hidden_size=37							)



					def 					lowerCAmelCase (     self :		List[str]							)	->     Optional[Any]:


												"""simple docstring"""

												self.config_tester.run_common_tests()



					def 					lowerCAmelCase (     self :		Tuple							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Any			=   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__a							)



					def 					lowerCAmelCase (     self :		Tuple							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*__a							)



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Optional[int]			=   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*__a							)



					def 					lowerCAmelCase (     self :		List[str]							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							str			=   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*__a							)



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     str:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*__a							)



					@slow
					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     List[str]:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""							)
												self.assertIsNotNone(__a							)







@require_tf
class 						lowerCAmelCase   (       unittest.TestCase	):

					'''simple docstring'''

					@unittest.skip(reason="""Model not available yet"""							)
					def 					lowerCAmelCase (     self :		int							)	->     Tuple:


												"""simple docstring"""

												pass



					@slow
					def 					lowerCAmelCase (     self :		List[Any]							)	->     str:


												"""simple docstring"""

												__lowercase					:							Optional[int]			=   TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""							)
												__lowercase					:							Tuple			=   tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							)
												__lowercase					:							List[str]			=   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
												__lowercase					:							List[Any]			=   model(__a						,   attention_mask=__a							)[0]

												__lowercase					:							List[Any]			=   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]						,   __a						,   atol=1E-4							)







lowerCamelCase       :       Optional[Any]        =				'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def 					snake_case_						(   ):
							__lowercase					:							List[str]			=   input("""Enter message: """					)
							__lowercase					:							int			=   input("""Enter key [alphanumeric]: """					)
							__lowercase					:							Optional[Any]			=   input("""Encrypt/Decrypt [e/d]: """					)

							if mode.lower().startswith("""e"""					):
														__lowercase					:							Optional[int]			=   """encrypt"""
														__lowercase					:							Dict			=   encrypt_message(lowerCAmelCase_				,				lowerCAmelCase_					)
							elif mode.lower().startswith("""d"""					):
														__lowercase					:							Union[str, Any]			=   """decrypt"""
														__lowercase					:							Optional[int]			=   decrypt_message(lowerCAmelCase_				,				lowerCAmelCase_					)

							print(F"\n{mode.title()}ed message:"					)
							print(lowerCAmelCase_					)
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  str					):
							return translate_message(lowerCAmelCase_				,				lowerCAmelCase_				,				"""encrypt"""					)
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  str					):
							return translate_message(lowerCAmelCase_				,				lowerCAmelCase_				,				"""decrypt"""					)
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  str					):
							__lowercase					:							Union[str, Any]			=   []
							__lowercase					:							Tuple			=   0
							__lowercase					:							Dict			=   key.upper()

							for symbol in message:
														__lowercase					:							Optional[Any]			=   LETTERS.find(symbol.upper()					)
														if num != -1:
																					if mode == "encrypt":
																												num += LETTERS.find(key[key_index]					)
																					elif mode == "decrypt":
																												num -= LETTERS.find(key[key_index]					)

																					num %= len(lowerCAmelCase_					)

																					if symbol.isupper():
																												translated.append(LETTERS[num]					)
																					elif symbol.islower():
																												translated.append(LETTERS[num].lower()					)

																					key_index += 1
																					if key_index == len(lowerCAmelCase_					):
																												__lowercase					:							str			=   0
														else:
																					translated.append(lowerCAmelCase_					)
							return "".join(lowerCAmelCase_					)


if __name__ == "__main__":
	main()