






"""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  snake_case		:


   '''simple docstring'''

   def __init__(     self			:					Dict,	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					List[Any]=13,	_lowerCamelCase			:					Optional[Any]=7,	_lowerCamelCase			:					Optional[Any]=True,	_lowerCamelCase			:					List[Any]=True,	_lowerCamelCase			:					Union[str, Any]=False,	_lowerCamelCase			:					Optional[int]=True,	_lowerCamelCase			:					Tuple=99,	_lowerCamelCase			:					int=64,	_lowerCamelCase			:					int=2,	_lowerCamelCase			:					Tuple=4,	_lowerCamelCase			:					List[str]=64,	_lowerCamelCase			:					int="gelu",	_lowerCamelCase			:					Dict=0.1,	_lowerCamelCase			:					Tuple=0.1,	_lowerCamelCase			:					Optional[Any]=5_12,	_lowerCamelCase			:					Optional[int]=16,	_lowerCamelCase			:					Any=2,	_lowerCamelCase			:					Optional[int]=0.02,	_lowerCamelCase			:					List[Any]=3,	_lowerCamelCase			:					Dict=4,	_lowerCamelCase			:					Tuple=None,	):

     '''simple docstring'''
     __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						      =			scope


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     __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
     __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						      =			ids_tensor([self.batch_size],	self.num_choices  )

     __A						      =			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     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					Union[str, Any],	_lowerCamelCase			:					Union[str, Any],	_lowerCamelCase			:					List[Any],	_lowerCamelCase			:					List[str],	_lowerCamelCase			:					Any,	_lowerCamelCase			:					Dict  ):

     '''simple docstring'''
     __A						      =			TFMPNetModel(config=_lowerCamelCase  )
     __A						      =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
     __A						      =			model(_lowerCamelCase  )
     __A						      =			[input_ids, input_mask]
     __A						      =			model(_lowerCamelCase  )
     self.parent.assertEqual(result.last_hidden_state.shape,	(self.batch_size, self.seq_length, self.hidden_size)  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict,	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Any,	_lowerCamelCase			:					int,	_lowerCamelCase			:					Union[str, Any]  ):

     '''simple docstring'''
     __A						      =			TFMPNetForMaskedLM(config=_lowerCamelCase  )
     __A						      =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
     __A						      =			model(_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.seq_length, self.vocab_size)  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Union[str, Any],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					int,	_lowerCamelCase			:					Any,	_lowerCamelCase			:					List[str],	_lowerCamelCase			:					int,	_lowerCamelCase			:					Any  ):

     '''simple docstring'''
     __A						      =			TFMPNetForQuestionAnswering(config=_lowerCamelCase  )
     __A						      =			{
         '''input_ids''': input_ids,
         '''attention_mask''': input_mask,
     }
     __A						      =			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     _SCREAMING_SNAKE_CASE    (     self			:					Union[str, Any],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Any,	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     __A						      =			self.num_labels
     __A						      =			TFMPNetForSequenceClassification(_lowerCamelCase  )
     __A						      =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
     __A						      =			model(_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.num_labels)  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					int,	_lowerCamelCase			:					Union[str, Any],	_lowerCamelCase			:					Optional[Any],	_lowerCamelCase			:					Union[str, Any],	_lowerCamelCase			:					List[str],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					List[Any]  ):

     '''simple docstring'''
     __A						      =			self.num_choices
     __A						      =			TFMPNetForMultipleChoice(_lowerCamelCase  )
     __A						      =			tf.tile(tf.expand_dims(_lowerCamelCase,	1  ),	(1, self.num_choices, 1)  )
     __A						      =			tf.tile(tf.expand_dims(_lowerCamelCase,	1  ),	(1, self.num_choices, 1)  )
     __A						      =			{
         '''input_ids''': multiple_choice_inputs_ids,
         '''attention_mask''': multiple_choice_input_mask,
     }
     __A						      =			model(_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.num_choices)  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					Dict,	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Tuple  ):

     '''simple docstring'''
     __A						      =			self.num_labels
     __A						      =			TFMPNetForTokenClassification(_lowerCamelCase  )
     __A						      =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
     __A						      =			model(_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.seq_length, self.num_labels)  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     __A						      =			self.prepare_config_and_inputs()
     ((__A)      ,   (__A)      ,   (__A)      ,   (__A)      ,   (__A)      ,   (__A))						      =			config_and_inputs
     __A						      =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
     return config, inputs_dict





@require_tf
class  snake_case		(							_lowerCAmelCase       ,     _lowerCAmelCase       ,     unittest.TestCase						):


   '''simple docstring'''

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


   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     __A						      =			TFMPNetModelTester(self  )
     __A						      =			ConfigTester(self,	config_class=_lowerCamelCase,	hidden_size=37  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     self.config_tester.run_common_tests()


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_model(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_masked_lm(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_question_answering(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_sequence_classification(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_multiple_choice(*_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     __A						      =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_token_classification(*_lowerCamelCase  )






   @slow
   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

     '''simple docstring'''
     for model_name in ["microsoft/mpnet-base"]:
       __A						      =			TFMPNetModel.from_pretrained(_lowerCamelCase  )
       self.assertIsNotNone(_lowerCamelCase  )





@require_tf
class  snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   @slow
   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     __A						      =			TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''  )
     __A						      =			tf.constant([[0, 1, 2, 3, 4, 5]]  )
     __A						      =			model(_lowerCamelCase  )[0]

     __A						      =			[1, 6, 7_68]
     self.assertEqual(output.shape,	_lowerCamelCase  )

     __A						      =			tf.constant(
         [
             [
                 [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
                 [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
                 [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
             ]
         ]  )
     tf.debugging.assert_near(output[:, :3, :3],	_lowerCamelCase,	atol=1e-4  )












"""simple docstring"""


def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  if len(__UpperCamelCase			) == 1:
    return True
  try:
    __A						      =			series[1] / series[0]
    for index in range(len(__UpperCamelCase			) - 1			):
      if series[index + 1] / series[index] != common_ratio:
        return False
  except ZeroDivisionError:
    return False
  return True



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  __A						      =			1
  for value in series:
    answer *= value
  return pow(__UpperCamelCase		,	1 / len(__UpperCamelCase			)			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()




