




'''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   SCREAMING_SNAKE_CASE_		:



 def __init__(  self			,		A			,		A=13			,		A=7			,		A=True			,		A=True			,		A=False			,		A=True			,		A=99			,		A=64			,		A=2			,		A=4			,		A=64			,		A="gelu"			,		A=0.1			,		A=0.1			,		A=512			,		A=16			,		A=2			,		A=0.0_2			,		A=3			,		A=4			,		A=None			,		):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		str						=      None
        if self.use_input_mask:
               lowercase  :		Dict						=      random_attention_mask([self.batch_size, self.seq_length] )

        lowercase  :		Optional[int]						=      None
        lowercase  :		Union[str, Any]						=      None
        lowercase  :		Optional[Any]						=      None
        if self.use_labels:
               lowercase  :		Optional[int]						=      ids_tensor([self.batch_size]			,		self.type_sequence_label_size )
               lowercase  :		Any						=      ids_tensor([self.batch_size, self.seq_length]			,		self.num_labels )
               lowercase  :		str						=      ids_tensor([self.batch_size]			,		self.num_choices )

        lowercase  :		int						=      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			,		A			,		A			,		A			,		A			,		A			,		A ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      TFMPNetModel(config=A )
        lowercase  :		int						=      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        lowercase  :		Any						=      model(A )
        lowercase  :		Union[str, Any]						=      [input_ids, input_mask]
        lowercase  :		str						=      model(A )
        self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size) )



 def    SCREAMING_SNAKE_CASE(  self			,		A			,		A			,		A			,		A			,		A			,		A ):






        '''simple docstring'''




        lowercase  :		str						=      TFMPNetForMaskedLM(config=A )
        lowercase  :		str						=      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        lowercase  :		str						=      model(A )
        self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.vocab_size) )



 def    SCREAMING_SNAKE_CASE(  self			,		A			,		A			,		A			,		A			,		A			,		A ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      TFMPNetForQuestionAnswering(config=A )
        lowercase  :		Dict						=      {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
        }
        lowercase  :		List[str]						=      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    SCREAMING_SNAKE_CASE(  self			,		A			,		A			,		A			,		A			,		A			,		A ):






        '''simple docstring'''




        lowercase  :		List[str]						=      self.num_labels
        lowercase  :		List[Any]						=      TFMPNetForSequenceClassification(A )
        lowercase  :		int						=      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        lowercase  :		List[Any]						=      model(A )
        self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels) )



 def    SCREAMING_SNAKE_CASE(  self			,		A			,		A			,		A			,		A			,		A			,		A ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      self.num_choices
        lowercase  :		Optional[Any]						=      TFMPNetForMultipleChoice(A )
        lowercase  :		str						=      tf.tile(tf.expand_dims(A			,		1 )			,		(1, self.num_choices, 1) )
        lowercase  :		Tuple						=      tf.tile(tf.expand_dims(A			,		1 )			,		(1, self.num_choices, 1) )
        lowercase  :		Optional[int]						=      {
            '''input_ids''': multiple_choice_inputs_ids,
            '''attention_mask''': multiple_choice_input_mask,
        }
        lowercase  :		Optional[int]						=      model(A )
        self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_choices) )



 def    SCREAMING_SNAKE_CASE(  self			,		A			,		A			,		A			,		A			,		A			,		A ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      self.num_labels
        lowercase  :		Dict						=      TFMPNetForTokenClassification(A )
        lowercase  :		List[str]						=      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        lowercase  :		str						=      model(A )
        self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.num_labels) )





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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






@require_tf
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_		,     lowerCamelCase_		,     unittest.TestCase				):
 __snake_case	:     Union[str, Any]			   =      (
     (
         TFMPNetForMaskedLM,
         TFMPNetForMultipleChoice,
         TFMPNetForQuestionAnswering,
         TFMPNetForSequenceClassification,
         TFMPNetForTokenClassification,
         TFMPNetModel,
     )
     if is_tf_available()
     else ()
 )
 __snake_case	:     str			   =      (
     {
         "feature-extraction": TFMPNetModel,
         "fill-mask": TFMPNetForMaskedLM,
         "question-answering": TFMPNetForQuestionAnswering,
         "text-classification": TFMPNetForSequenceClassification,
         "token-classification": TFMPNetForTokenClassification,
         "zero-shot": TFMPNetForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __snake_case	:     str			   =      False
 __snake_case	:     Optional[Any]			   =      False



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      TFMPNetModelTester(self )
        lowercase  :		List[Any]						=      ConfigTester(self			,		config_class=A			,		hidden_size=37 )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self.config_tester.run_common_tests()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_model(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Any						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_masked_lm(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_question_answering(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Any						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_sequence_classification(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_multiple_choice(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_token_classification(*A )





 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        for model_name in ["microsoft/mpnet-base"]:
               lowercase  :		Union[str, Any]						=      TFMPNetModel.from_pretrained(A )
               self.assertIsNotNone(A )






@require_tf
class   SCREAMING_SNAKE_CASE_		(       unittest.TestCase				):



 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      TFMPNetModel.from_pretrained('''microsoft/mpnet-base''' )
        lowercase  :		Optional[Any]						=      tf.constant([[0, 1, 2, 3, 4, 5]] )
        lowercase  :		Optional[Any]						=      model(A )[0]

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

        lowercase  :		Any						=      tf.constant(
            [
                [
                    [-0.1_0_6_7_1_7_2, 0.0_8_2_1_6_4_7_3, 0.0_0_2_4_5_4_3],
                    [-0.0_3_4_6_5_8_7_9, 0.8_3_5_4_1_1_8, -0.0_3_2_5_2_2_8_8],
                    [-0.0_6_5_6_9_4_7_6, -0.1_2_4_2_4_1_1_1, -0.0_4_9_4_4_3_6],
                ]
            ] )
        tf.debugging.assert_near(output[:, :3, :3]			,		A			,		atol=1E-4 )










'''simple docstring'''






class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      [0, 1]





 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        if (difference := index - (len(self.sequence ) - 2)) >= 1:
               for _ in range(A ):
                      self.sequence.append(self.sequence[-1] + self.sequence[-2] )
        return self.sequence[:index]






def 		a_( )				->None:






       '''simple docstring'''




       print(
           '''Fibonacci Series Using Dynamic Programming\n'''   ,				'''Enter the index of the Fibonacci number you want to calculate '''   ,				'''in the prompt below. (To exit enter exit or Ctrl-C)\n'''   ,				sep=''''''   ,				)
       lowercase  :		Optional[int]						=      Fibonacci()

       while True:
              lowercase  :		str						=      input('''>> '''      )
              if prompt in {"exit", "quit"}:
                     break

              try:
                     lowercase  :		int						=      int(lowerCamelCase      )
              except ValueError:
                     print('''Enter a number or \'exit\''''      )
                     continue

              print(fibonacci.get(lowerCamelCase      )      )


if __name__ == "__main__":
      main()




