






"""simple docstring"""

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     A_				:







				def __init__(				self				:    Any    ,     __lowerCamelCase				:    int    ,     __lowerCamelCase				:    List[str]=1_3    ,     __lowerCamelCase				:    List[Any]=7    ,     __lowerCamelCase				:    Optional[int]=True    ,     __lowerCamelCase				:    Tuple=True    ,     __lowerCamelCase				:    Any=False    ,     __lowerCamelCase				:    Union[str, Any]=True    ,     __lowerCamelCase				:    Any=9_9    ,     __lowerCamelCase				:    Optional[Any]=6_4    ,     __lowerCamelCase				:    List[Any]=2    ,     __lowerCamelCase				:    Union[str, Any]=4    ,     __lowerCamelCase				:    int=6_4    ,     __lowerCamelCase				:    Union[str, Any]="gelu"    ,     __lowerCamelCase				:    Dict=0.1    ,     __lowerCamelCase				:    List[str]=0.1    ,     __lowerCamelCase				:    List[Any]=5_1_2    ,     __lowerCamelCase				:    Tuple=1_6    ,     __lowerCamelCase				:    Optional[int]=2    ,     __lowerCamelCase				:    str=0.02    ,     __lowerCamelCase				:    Union[str, Any]=3    ,     __lowerCamelCase				:    Dict=4    ,     __lowerCamelCase				:    int=None    ,     )					->       str:
										__magic_name__      =  parent
										__magic_name__      =  batch_size
										__magic_name__      =  seq_length
										__magic_name__      =  is_training
										__magic_name__      =  use_input_mask
										__magic_name__      =  use_token_type_ids
										__magic_name__      =  use_labels
										__magic_name__      =  vocab_size
										__magic_name__      =  hidden_size
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  intermediate_size
										__magic_name__      =  hidden_act
										__magic_name__      =  hidden_dropout_prob
										__magic_name__      =  attention_probs_dropout_prob
										__magic_name__      =  max_position_embeddings
										__magic_name__      =  type_vocab_size
										__magic_name__      =  type_sequence_label_size
										__magic_name__      =  initializer_range
										__magic_name__      =  num_labels
										__magic_name__      =  num_choices
										__magic_name__      =  scope







				def      _snake_case       (				self				:    str						)					->       int:
										__magic_name__      =  ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size						)

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

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

										__magic_name__      =  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      _snake_case       (				self				:    Optional[int]    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Optional[Any]						)					->       Union[str, Any]:
										__magic_name__      =  TFMPNetModel(config=__lowerCamelCase						)
										__magic_name__      =  {"input_ids": input_ids, "attention_mask": input_mask}
										__magic_name__      =  model(__lowerCamelCase						)
										__magic_name__      =  [input_ids, input_mask]
										__magic_name__      =  model(__lowerCamelCase						)
										self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)						)







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







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







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







				def      _snake_case       (				self				:    List[Any]    ,     __lowerCamelCase				:    List[Any]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    int    ,     __lowerCamelCase				:    List[Any]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    Dict						)					->       List[str]:
										__magic_name__      =  self.num_labels
										__magic_name__      =  TFMPNetForTokenClassification(__lowerCamelCase						)
										__magic_name__      =  {"input_ids": input_ids, "attention_mask": input_mask}
										__magic_name__      =  model(__lowerCamelCase						)
										self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels)						)







				def      _snake_case       (				self				:    str						)					->       Union[str, Any]:
										__magic_name__      =  self.prepare_config_and_inputs()
										((__magic_name__)   ,     (__magic_name__)   ,     (__magic_name__)   ,     (__magic_name__)   ,     (__magic_name__)   ,     (__magic_name__))      =  config_and_inputs
										__magic_name__      =  {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict





@require_tf
class     A_				(   snake_case_       ,				snake_case_       ,				unittest.TestCase	):
				UpperCAmelCase__										=					(
				    (
				        TFMPNetForMaskedLM,
				        TFMPNetForMultipleChoice,
				        TFMPNetForQuestionAnswering,
				        TFMPNetForSequenceClassification,
				        TFMPNetForTokenClassification,
				        TFMPNetModel,
				    )
				    if is_tf_available()
				    else ()
				)
				UpperCAmelCase__										=					(
				    {
				        '''feature-extraction''': TFMPNetModel,
				        '''fill-mask''': TFMPNetForMaskedLM,
				        '''question-answering''': TFMPNetForQuestionAnswering,
				        '''text-classification''': TFMPNetForSequenceClassification,
				        '''token-classification''': TFMPNetForTokenClassification,
				        '''zero-shot''': TFMPNetForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				UpperCAmelCase__										=					False
				UpperCAmelCase__										=					False







				def      _snake_case       (				self				:    List[Any]						)					->       str:
										__magic_name__      =  TFMPNetModelTester(self						)
										__magic_name__      =  ConfigTester(self    ,     config_class=__lowerCamelCase    ,     hidden_size=3_7						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Optional[int]:
										self.config_tester.run_common_tests()







				def      _snake_case       (				self				:    Dict						)					->       List[str]:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_model(*__lowerCamelCase						)







				def      _snake_case       (				self				:    List[str]						)					->       Dict:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_masked_lm(*__lowerCamelCase						)







				def      _snake_case       (				self				:    str						)					->       int:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_question_answering(*__lowerCamelCase						)







				def      _snake_case       (				self				:    Tuple						)					->       List[Any]:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowerCamelCase						)







				def      _snake_case       (				self				:    Optional[int]						)					->       Optional[Any]:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowerCamelCase						)







				def      _snake_case       (				self				:    Dict						)					->       Union[str, Any]:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_token_classification(*__lowerCamelCase						)







				@slow
				def      _snake_case       (				self				:    Optional[Any]						)					->       Union[str, Any]:
										for model_name in ["microsoft/mpnet-base"]:
																__magic_name__      =  TFMPNetModel.from_pretrained(__lowerCamelCase						)
																self.assertIsNotNone(__lowerCamelCase						)





@require_tf
class     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Optional[int]						)					->       Union[str, Any]:
										__magic_name__      =  TFMPNetModel.from_pretrained("microsoft/mpnet-base"						)
										__magic_name__      =  tf.constant([[0, 1, 2, 3, 4, 5]]						)
										__magic_name__      =  model(__lowerCamelCase						)[0]

										__magic_name__      =  [1, 6, 7_6_8]
										self.assertEqual(output.shape    ,     __lowerCamelCase						)

										__magic_name__      =  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						)











"""simple docstring"""

import math






def _lowerCAmelCase						(	__lowerCamelCase:int ):

						'''simple docstring'''






						if number == 0:
												__magic_name__      =  "Zero"
												return words
						else:
												__magic_name__      =  math.logaa(__lowerCamelCase )
												__magic_name__      =  digits + 1
												__magic_name__      =  {}
												__magic_name__      =  ""
												__magic_name__      =  "One"
												__magic_name__      =  "Two"
												__magic_name__      =  "Three"
												__magic_name__      =  "Four"
												__magic_name__      =  "Five"
												__magic_name__      =  "Six"
												__magic_name__      =  "Seven"
												__magic_name__      =  "Eight"
												__magic_name__      =  "Nine"

												__magic_name__      =  {}
												__magic_name__      =  ""
												__magic_name__      =  "Twenty"
												__magic_name__      =  "Thirty"
												__magic_name__      =  "Forty"
												__magic_name__      =  "Fifty"
												__magic_name__      =  "Sixty"
												__magic_name__      =  "Seventy"
												__magic_name__      =  "Eighty"
												__magic_name__      =  "Ninety"

												__magic_name__      =  {}
												__magic_name__      =  "Ten"
												__magic_name__      =  "Eleven"
												__magic_name__      =  "Twelve"
												__magic_name__      =  "Thirteen"
												__magic_name__      =  "Fourteen"
												__magic_name__      =  "Fifteen"
												__magic_name__      =  "Sixteen"
												__magic_name__      =  "Seventeen"
												__magic_name__      =  "Eighteen"
												__magic_name__      =  "Nineteen"

												__magic_name__      =  {}
												__magic_name__      =  "Hundred,"
												__magic_name__      =  "Thousand,"
												__magic_name__      =  "Lakh,"
												__magic_name__      =  "Crore,"

												__magic_name__      =  number
												__magic_name__      =  ""
												__magic_name__      =  0
												__magic_name__      =  int(__lowerCamelCase )
												while counter < digits:
																		__magic_name__      =  temp_num % 1_0
																		if counter % 2 == 0:
																								__magic_name__      =  ""
																								if counter in placevalue and current != 0:
																														__magic_name__      =  placevalue[counter]
																								if counter == 2:
																														__magic_name__      =  singles[current] + addition + words
																								elif counter == 0:
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				__magic_name__      =  teens[current] + addition + words
																																				__magic_name__      =  temp_num // 1_0
																																				counter += 1
																														else:
																																				__magic_name__      =  singles[current] + addition + words

																								else:
																														__magic_name__      =  doubles[current] + addition + words

																		else:
																								if counter == 1:
																														if current == 1:
																																				__magic_name__      =  teens[number % 1_0] + words
																														else:
																																				__magic_name__      =  ""
																																				if counter in placevalue:
																																										__magic_name__      =  placevalue[counter]
																																				__magic_name__      =  doubles[current] + addition + words
																								else:
																														__magic_name__      =  ""
																														if counter in placevalue:
																																				if current == 0 and ((temp_num % 1_0_0) // 1_0) == 0:
																																										__magic_name__      =  ""
																																				else:
																																										__magic_name__      =  placevalue[counter]
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				__magic_name__      =  teens[current] + addition + words
																																				__magic_name__      =  temp_num // 1_0
																																				counter += 1
																														else:
																																				__magic_name__      =  singles[current] + addition + words
																		counter += 1
																		__magic_name__      =  temp_num // 1_0
						return words


if __name__ == "__main__":
			import doctest

			doctest.testmod()



