





"""simple docstring"""
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCamelCase			:		Any		    =   logging.get_logger(__name__)

__UpperCamelCase			:		Optional[int]		    =   {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}







class 	a				(					a__   ):
 snake_case__    	=    '''longt5'''
 snake_case__    	=    ['''past_key_values''']
 snake_case__    	=    {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



 def __init__(			self							,    _snake_case=3_21_28							,    _snake_case=5_12							,    _snake_case=64							,    _snake_case=20_48							,    _snake_case=6							,    _snake_case=None							,    _snake_case=8							,    _snake_case=1_27							,    _snake_case=16							,    _snake_case=32							,    _snake_case=1_28							,    _snake_case=0.1							,    _snake_case=1E-6							,    _snake_case=1.0							,    _snake_case="relu"							,    _snake_case=True							,    _snake_case="local"							,    _snake_case=True							,    _snake_case=0							,    _snake_case=1							,    **_snake_case							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      vocab_size
  lowerCAmelCase  							=      d_model
  lowerCAmelCase  							=      d_kv
  lowerCAmelCase  							=      d_ff
  lowerCAmelCase  							=      num_layers
  # default = symmetry
  lowerCAmelCase  							=      num_decoder_layers if num_decoder_layers is not None else self.num_layers
  lowerCAmelCase  							=      num_heads
  lowerCAmelCase  							=      local_radius
  lowerCAmelCase  							=      global_block_size
  lowerCAmelCase  							=      relative_attention_num_buckets
  lowerCAmelCase  							=      relative_attention_max_distance
  lowerCAmelCase  							=      dropout_rate
  lowerCAmelCase  							=      layer_norm_epsilon
  lowerCAmelCase  							=      initializer_factor
  lowerCAmelCase  							=      feed_forward_proj
  lowerCAmelCase  							=      encoder_attention_type
  lowerCAmelCase  							=      use_cache

  lowerCAmelCase  							=      self.feed_forward_proj.split('-'			)
  lowerCAmelCase  							=      act_info[-1]
  lowerCAmelCase  							=      act_info[0] == 'gated'

  if len(_snake_case			) > 1 and act_info[0] != "gated" or len(_snake_case			) > 2:
   raise ValueError(
       F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
       'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
       '\'gated-gelu\' or \'relu\''			)

  # for backwards compatibility
  if feed_forward_proj == "gated-gelu":
   lowerCAmelCase  							=      'gelu_new'

  super().__init__(
      pad_token_id=_snake_case							,    eos_token_id=_snake_case							,    is_encoder_decoder=_snake_case							,    **_snake_case							,    )







class 	a				(					a__   ):



 @property
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      {
      'input_ids': {0: 'batch', 1: 'encoder_sequence'},
      'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
  }
  if self.use_past:
   lowerCAmelCase  							=      'past_encoder_sequence + sequence'
   lowerCAmelCase  							=      {0: 'batch'}
   lowerCAmelCase  							=      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
  else:
   lowerCAmelCase  							=      {0: 'batch', 1: 'decoder_sequence'}
   lowerCAmelCase  							=      {0: 'batch', 1: 'decoder_sequence'}

  if self.use_past:
   self.fill_with_past_key_values_(_snake_case							,    direction='inputs'			)

  return common_inputs



 @property
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  return 13












"""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							,    _snake_case							,    _snake_case=13							,    _snake_case=7							,    _snake_case=True							,    _snake_case=True							,    _snake_case=False							,    _snake_case=True							,    _snake_case=99							,    _snake_case=64							,    _snake_case=2							,    _snake_case=4							,    _snake_case=64							,    _snake_case="gelu"							,    _snake_case=0.1							,    _snake_case=0.1							,    _snake_case=5_12							,    _snake_case=16							,    _snake_case=2							,    _snake_case=0.02							,    _snake_case=3							,    _snake_case=4							,    _snake_case=None							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      parent
  lowerCAmelCase  							=      batch_size
  lowerCAmelCase  							=      seq_length
  lowerCAmelCase  							=      is_training
  lowerCAmelCase  							=      use_input_mask
  lowerCAmelCase  							=      use_token_type_ids
  lowerCAmelCase  							=      use_labels
  lowerCAmelCase  							=      vocab_size
  lowerCAmelCase  							=      hidden_size
  lowerCAmelCase  							=      num_hidden_layers
  lowerCAmelCase  							=      num_attention_heads
  lowerCAmelCase  							=      intermediate_size
  lowerCAmelCase  							=      hidden_act
  lowerCAmelCase  							=      hidden_dropout_prob
  lowerCAmelCase  							=      attention_probs_dropout_prob
  lowerCAmelCase  							=      max_position_embeddings
  lowerCAmelCase  							=      type_vocab_size
  lowerCAmelCase  							=      type_sequence_label_size
  lowerCAmelCase  							=      initializer_range
  lowerCAmelCase  							=      num_labels
  lowerCAmelCase  							=      num_choices
  lowerCAmelCase  							=      scope



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size			)

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

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

  lowerCAmelCase  							=      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 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetForQuestionAnswering(config=_snake_case			)
  lowerCAmelCase  							=      {
      'input_ids': input_ids,
      'attention_mask': input_mask,
  }
  lowerCAmelCase  							=      model(_snake_case			)
  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 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.num_labels
  lowerCAmelCase  							=      TFMPNetForSequenceClassification(_snake_case			)
  lowerCAmelCase  							=      {'input_ids': input_ids, 'attention_mask': input_mask}
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)			)



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      self.num_labels
  lowerCAmelCase  							=      TFMPNetForTokenClassification(_snake_case			)
  lowerCAmelCase  							=      {'input_ids': input_ids, 'attention_mask': input_mask}
  lowerCAmelCase  							=      model(_snake_case			)
  self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels)			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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







@require_tf
class 	a				(					a__					,      a__					,      unittest.TestCase   ):
 snake_case__    	=    (
     (
         TFMPNetForMaskedLM,
         TFMPNetForMultipleChoice,
         TFMPNetForQuestionAnswering,
         TFMPNetForSequenceClassification,
         TFMPNetForTokenClassification,
         TFMPNetModel,
     )
     if is_tf_available()
     else ()
 )
 snake_case__    	=    (
     {
         '''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__    	=    False
 snake_case__    	=    False



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      TFMPNetModelTester(self			)
  lowerCAmelCase  							=      ConfigTester(self							,    config_class=_snake_case							,    hidden_size=37			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_model(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_masked_lm(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_question_answering(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_sequence_classification(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_multiple_choice(*_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_mpnet_for_token_classification(*_snake_case			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for model_name in ["microsoft/mpnet-base"]:
   lowerCAmelCase  							=      TFMPNetModel.from_pretrained(_snake_case			)
   self.assertIsNotNone(_snake_case			)







@require_tf
class 	a				(					unittest.TestCase   ):



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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

  lowerCAmelCase  							=      [1, 6, 7_68]
  self.assertEqual(output.shape							,    _snake_case			)

  lowerCAmelCase  							=      tf.constant(
      [
          [
              [-0.1_067_172, 0.08_216_473, 0.0_024_543],
              [-0.03_465_879, 0.8_354_118, -0.03_252_288],
              [-0.06_569_476, -0.12_424_111, -0.0_494_436],
          ]
      ]			)
  tf.debugging.assert_near(output[:, :3, :3]							,    _snake_case							,    atol=1E-4			)





