"""simple docstring"""






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        snake_case :




					"""simple docstring"""

					def __init__(       self     :			str					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Union[str, Any]=13					,lowerCamelCase__     :			Union[str, Any]=7					,lowerCamelCase__     :			Optional[Any]=True					,lowerCamelCase__     :			List[Any]=True					,lowerCamelCase__     :			str=True					,lowerCamelCase__     :			Optional[int]=True					,lowerCamelCase__     :			List[str]=99					,lowerCamelCase__     :			Optional[int]=32					,lowerCamelCase__     :			Dict=2					,lowerCamelCase__     :			Optional[Any]=4					,lowerCamelCase__     :			Optional[Any]=37					,lowerCamelCase__     :			Optional[Any]="gelu"					,lowerCamelCase__     :			Optional[int]=0.1					,lowerCamelCase__     :			Dict=0.1					,lowerCamelCase__     :			Dict=512					,lowerCamelCase__     :			Optional[int]=16					,lowerCamelCase__     :			Optional[Any]=2					,lowerCamelCase__     :			Optional[int]=0.0_2					,lowerCamelCase__     :			str=False					,lowerCamelCase__     :			Any=True					,lowerCamelCase__     :			int="None"					,lowerCamelCase__     :			Dict=3					,lowerCamelCase__     :			List[Any]=4					,lowerCamelCase__     :			List[str]=None					,):
									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 	__lowerCAmelCase		(       self     :			str    ):
									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=lowerCamelCase__					,)

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







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Optional[Any]    ):
									UpperCAmelCase__    =							TFDebertaVaModel(config=lowerCamelCase__    )
									UpperCAmelCase__    =							{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

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

									UpperCAmelCase__    =							model(lowerCamelCase__    )

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







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Tuple    ):
									UpperCAmelCase__    =							TFDebertaVaForMaskedLM(config=lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									UpperCAmelCase__    =							model(lowerCamelCase__    )
									self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Union[str, Any]    ):
									UpperCAmelCase__    =							self.num_labels
									UpperCAmelCase__    =							TFDebertaVaForSequenceClassification(config=lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

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







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							self.num_labels
									UpperCAmelCase__    =							TFDebertaVaForTokenClassification(config=lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									UpperCAmelCase__    =							model(lowerCamelCase__    )
									self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.num_labels)    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			Any    ):
									UpperCAmelCase__    =							TFDebertaVaForQuestionAnswering(config=lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									UpperCAmelCase__    =							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 	__lowerCAmelCase		(       self     :			List[Any]    ):
									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        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

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

					snake_case__       					=				False
					snake_case__       					=				False







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							TFDebertaVaModelTester(self    )
									UpperCAmelCase__    =							ConfigTester(self					,config_class=lowerCamelCase__					,hidden_size=37    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									self.config_tester.run_common_tests()







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'    )
									self.assertIsNotNone(lowerCamelCase__    )






@require_tf
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					@unittest.skip(reason='Model not available yet'    )
					def 	__lowerCAmelCase		(       self     :			Any    ):
									pass







					@slow
					def 	__lowerCAmelCase		(       self     :			int    ):
									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(lowerCamelCase__					,attention_mask=lowerCamelCase__    )[0]

									UpperCAmelCase__    =							tf.constant(
									    [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]    )
									tf.debugging.assert_near(output[:, 1:4, 1:4]					,lowerCamelCase__					,atol=1e-4    )




"""simple docstring"""






from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowerCAmelCase__						:						str		  =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCAmelCase__						:						list[int]		  =			[ord(letter) for letter in string.ascii_lowercase]
lowerCAmelCase__						:						set[int]		  =			{ord(char) for char in VALID_CHARS}

lowerCAmelCase__						:						list[str]		  =			["the", "be", "to", "of", "and", "in", "that", "have"]



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							""
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42

				for keychar, cipherchar in zip(cycle(lowerCamelCase )      ,    lowerCamelCase ):
								UpperCAmelCase__    =							cipherchar ^ keychar
								if decodedchar not in VALID_INTS:
												return None
								decoded += chr(lowerCamelCase )

				return decoded



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							[]
				for key in product(lowerCamelCase      ,    repeat=3 ):
								UpperCAmelCase__    =							try_key(lowerCamelCase      ,    lowerCamelCase )
								if encoded is not None:
												possibles.append(lowerCamelCase )
				return possibles



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return [possible for possible in possibles if common_word in possible.lower()]



def 						a_		(      lowerCamelCase = "p059_cipher.txt" ):
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							42
				UpperCAmelCase__    =							Path(lowerCamelCase ).parent.joinpath(lowerCamelCase ).read_text(encoding='utf-8' )

				UpperCAmelCase__    =							[int(lowerCamelCase ) for number in data.strip().split(',' )]

				UpperCAmelCase__    =							filter_valid_chars(lowerCamelCase )
				for common_word in COMMON_WORDS:
								UpperCAmelCase__    =							filter_common_word(lowerCamelCase      ,    lowerCamelCase )
								if len(lowerCamelCase ) == 1:
												break

				UpperCAmelCase__    =							possibles[0]
				return sum(ord(lowerCamelCase ) for char in decoded_text )


if __name__ == "__main__":
						print(F"""{solution() = }""")



