


from __future__ import annotations

import unittest

from transformers import MPNetConfig, 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.models.mpnet.modeling_tf_mpnet import (
						    TFMPNetForMaskedLM,
						    TFMPNetForMultipleChoice,
						    TFMPNetForQuestionAnswering,
						    TFMPNetForSequenceClassification,
						    TFMPNetForTokenClassification,
						    TFMPNetModel,
						)
class 	_lowercase				:






				'''simple docstring'''



				def __init__(	self      :Union[str, Any]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :List[Any]=13      ,      lowerCAmelCase__      :Optional[int]=7      ,      lowerCAmelCase__      :List[str]=True      ,      lowerCAmelCase__      :Optional[Any]=True      ,      lowerCAmelCase__      :str=False      ,      lowerCAmelCase__      :Dict=True      ,      lowerCAmelCase__      :List[Any]=99      ,      lowerCAmelCase__      :int=64      ,      lowerCAmelCase__      :Union[str, Any]=2      ,      lowerCAmelCase__      :Tuple=4      ,      lowerCAmelCase__      :Union[str, Any]=64      ,      lowerCAmelCase__      :List[str]="gelu"      ,      lowerCAmelCase__      :int=0.1      ,      lowerCAmelCase__      :Optional[int]=0.1      ,      lowerCAmelCase__      :int=512      ,      lowerCAmelCase__      :List[Any]=16      ,      lowerCAmelCase__      :Optional[int]=2      ,      lowerCAmelCase__      :int=0.02      ,      lowerCAmelCase__      :Union[str, Any]=3      ,      lowerCAmelCase__      :List[Any]=4      ,      lowerCAmelCase__      :List[str]=None      ,      )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		parent
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		batch_size
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		seq_length
										__SCREAMING_SNAKE_CASE						:					Any		      =		is_training
										__SCREAMING_SNAKE_CASE						:					Dict		      =		use_input_mask
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		use_token_type_ids
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		use_labels
										__SCREAMING_SNAKE_CASE						:					Dict		      =		vocab_size
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		hidden_size
										__SCREAMING_SNAKE_CASE						:					Any		      =		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		num_attention_heads
										__SCREAMING_SNAKE_CASE						:					str		      =		intermediate_size
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		hidden_act
										__SCREAMING_SNAKE_CASE						:					int		      =		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		max_position_embeddings
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		type_vocab_size
										__SCREAMING_SNAKE_CASE						:					Any		      =		type_sequence_label_size
										__SCREAMING_SNAKE_CASE						:					str		      =		initializer_range
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		num_labels
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		num_choices
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		scope


				def 		__magic_name__(	self      :Union[str, Any]     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		None
										if self.use_input_mask:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		random_attention_mask([self.batch_size, self.seq_length]     )

										__SCREAMING_SNAKE_CASE						:					Any		      =		None
										__SCREAMING_SNAKE_CASE						:					int		      =		None
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		None
										if self.use_labels:
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		ids_tensor([self.batch_size]      ,      self.type_sequence_label_size     )
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		ids_tensor([self.batch_size, self.seq_length]      ,      self.num_labels     )
																__SCREAMING_SNAKE_CASE						:					int		      =		ids_tensor([self.batch_size]      ,      self.num_choices     )

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		MPNetConfig(
										    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      ,      initializer_range=self.initializer_range      ,      )
										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


				def 		__magic_name__(	self      :Optional[Any]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Optional[int]     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					Any		      =		TFMPNetModel(config=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		[input_ids, input_mask]
										__SCREAMING_SNAKE_CASE						:					Dict		      =		model(lowerCAmelCase__     )
										self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)     )


				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :int     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		TFMPNetForMaskedLM(config=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		model(lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)     )


				def 		__magic_name__(	self      :Any      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :Tuple     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		TFMPNetForQuestionAnswering(config=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		{
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										}
										__SCREAMING_SNAKE_CASE						:					Any		      =		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 		__magic_name__(	self      :List[Any]      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Any     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.num_labels
										__SCREAMING_SNAKE_CASE						:					Dict		      =		TFMPNetForSequenceClassification(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
										__SCREAMING_SNAKE_CASE						:					Dict		      =		model(lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)     )


				def 		__magic_name__(	self      :List[Any]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :str     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					str		      =		self.num_choices
										__SCREAMING_SNAKE_CASE						:					str		      =		TFMPNetForMultipleChoice(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		tf.tile(tf.expand_dims(lowerCAmelCase__      ,      1     )      ,      (1, self.num_choices, 1)     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		tf.tile(tf.expand_dims(lowerCAmelCase__      ,      1     )      ,      (1, self.num_choices, 1)     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		{
										    '''input_ids''': multiple_choice_inputs_ids,
										    '''attention_mask''': multiple_choice_input_mask,
										}
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		model(lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_choices)     )


				def 		__magic_name__(	self      :Any      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Dict     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.num_labels
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		TFMPNetForTokenClassification(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
										__SCREAMING_SNAKE_CASE						:					int		      =		model(lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.num_labels)     )



				def 		__magic_name__(	self      :int     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.prepare_config_and_inputs()
										((__SCREAMING_SNAKE_CASE)      ,      (__SCREAMING_SNAKE_CASE)      ,      (__SCREAMING_SNAKE_CASE)      ,      (__SCREAMING_SNAKE_CASE)      ,      (__SCREAMING_SNAKE_CASE)      ,      (__SCREAMING_SNAKE_CASE))						:					Dict		      =		config_and_inputs
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict




@require_tf
class 	_lowercase				(      A__						,      A__						,      unittest.TestCase	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		(
				    (
				        TFMPNetForMaskedLM,
				        TFMPNetForMultipleChoice,
				        TFMPNetForQuestionAnswering,
				        TFMPNetForSequenceClassification,
				        TFMPNetForTokenClassification,
				        TFMPNetModel,
				    )
				    if is_tf_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__    :      Tuple      =		(
				    {
				        '''feature-extraction''': TFMPNetModel,
				        '''fill-mask''': TFMPNetForMaskedLM,
				        '''question-answering''': TFMPNetForQuestionAnswering,
				        '''text-classification''': TFMPNetForSequenceClassification,
				        '''token-classification''': TFMPNetForTokenClassification,
				        '''zero-shot''': TFMPNetForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE__    :      Union[str, Any]      =		False
				SCREAMING_SNAKE_CASE__    :      Union[str, Any]      =		False


				def 		__magic_name__(	self      :Tuple     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		TFMPNetModelTester(self     )
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		ConfigTester(self      ,      config_class=lowerCAmelCase__      ,      hidden_size=37     )


				def 		__magic_name__(	self      :List[Any]     )		->      Optional[int]:
										self.config_tester.run_common_tests()


				def 		__magic_name__(	self      :Optional[int]     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_model(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :Dict     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_masked_lm(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :int     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[Any]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					str		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :Union[str, Any]     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[Any]     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCAmelCase__     )



				@slow
				def 		__magic_name__(	self      :int     )		->      str:
										for model_name in ["microsoft/mpnet-base"]:
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		TFMPNetModel.from_pretrained(lowerCAmelCase__     )
																self.assertIsNotNone(lowerCAmelCase__     )




@require_tf
class 	_lowercase				(      unittest.TestCase	):






				'''simple docstring'''



				@slow
				def 		__magic_name__(	self      :Any     )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		tf.constant([[0, 1, 2, 3, 4, 5]]     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		model(lowerCAmelCase__     )[0]

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		[1, 6, 768]
										self.assertEqual(output.shape      ,      lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					str		      =		tf.constant(
										    [
										        [
										            [-0.106_7172, 0.0821_6473, 0.002_4543],
										            [-0.0346_5879, 0.835_4118, -0.0325_2288],
										            [-0.0656_9476, -0.1242_4111, -0.049_4436],
										        ]
										    ]     )
										tf.debugging.assert_near(output[:, :3, :3]      ,      lowerCAmelCase__      ,      atol=1E-4     )




def 	_UpperCamelCase							(				lowercase__				):
						barcode //= 10  # exclude the last digit
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		False
						__SCREAMING_SNAKE_CASE						:					Dict		      =		0

						# extract and check each digit
						while barcode != 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		1 if checker else 3
												s += mult * (barcode % 10)
												barcode //= 10
												__SCREAMING_SNAKE_CASE						:					Dict		      =		not checker

						return (10 - (s % 10)) % 10


def 	_UpperCamelCase							(				lowercase__				):
						return len(str(lowercase__				)				) == 13 and get_check_digit(lowercase__				) == barcode % 10


def 	_UpperCamelCase							(				lowercase__				):
						if str(lowercase__				).isalpha():
												__SCREAMING_SNAKE_CASE						:					str		      =		F'''Barcode \'{barcode}\' has alphabetic characters.'''
												raise ValueError(lowercase__				)
						elif int(lowercase__				) < 0:
												raise ValueError('''The entered barcode has a negative value. Try again.'''				)
						else:
												return int(lowercase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase     :			Union[str, Any]      =get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(f"""'{barcode}' is a valid barcode.""")
						else:
												print(f"""'{barcode}' is NOT a valid barcode.""")
