


"""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_:






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






     def   lowerCamelCase__  (						self	:       str					):
           lowerCAmelCase       :						Any							= ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size					)

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

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

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






     def   lowerCamelCase__  (						self	:       int       ,					UpperCamelCase_	:       List[str]       ,					UpperCamelCase_	:       List[str]       ,					UpperCamelCase_	:       int       ,					UpperCamelCase_	:       Union[str, Any]       ,					UpperCamelCase_	:       Union[str, Any]       ,					UpperCamelCase_	:       str					):
           lowerCAmelCase       :						Optional[int]							= TFMPNetForMaskedLM(config=UpperCamelCase_					)
           lowerCAmelCase       :						Tuple							= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
           lowerCAmelCase       :						List[str]							= model(UpperCamelCase_					)
           self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size)					)






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






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






     def   lowerCamelCase__  (						self	:       Dict       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       Union[str, Any]       ,					UpperCamelCase_	:       Union[str, Any]					):
           lowerCAmelCase       :						List[Any]							= self.num_labels
           lowerCAmelCase       :						Union[str, Any]							= TFMPNetForTokenClassification(UpperCamelCase_					)
           lowerCAmelCase       :						List[Any]							= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
           lowerCAmelCase       :						str							= model(UpperCamelCase_					)
           self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.num_labels)					)






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



@require_tf
class 		snake_case_(							a__				,       a__				,       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   lowerCamelCase__  (						self	:       int					):
           lowerCAmelCase       :						List[str]							= TFMPNetModelTester(self					)
           lowerCAmelCase       :						int							= ConfigTester(self       ,					config_class=UpperCamelCase_       ,					hidden_size=3_7					)






     def   lowerCamelCase__  (						self	:       List[Any]					):
           self.config_tester.run_common_tests()






     def   lowerCamelCase__  (						self	:       Optional[Any]					):
           lowerCAmelCase       :						int							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_model(*UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Dict					):
           lowerCAmelCase       :						Optional[Any]							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_masked_lm(*UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       int					):
           lowerCAmelCase       :						int							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_question_answering(*UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Tuple					):
           lowerCAmelCase       :						Union[str, Any]							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_sequence_classification(*UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       int					):
           lowerCAmelCase       :						Optional[Any]							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_multiple_choice(*UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       int					):
           lowerCAmelCase       :						str							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_token_classification(*UpperCamelCase_					)






     @slow
     def   lowerCamelCase__  (						self	:       Any					):
           for model_name in ["microsoft/mpnet-base"]:
                 lowerCAmelCase       :						Tuple							= TFMPNetModel.from_pretrained(UpperCamelCase_					)
                 self.assertIsNotNone(UpperCamelCase_					)



@require_tf
class 		snake_case_(							unittest.TestCase      ):






     @slow
     def   lowerCamelCase__  (						self	:       Any					):
           lowerCAmelCase       :						int							= TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''					)
           lowerCAmelCase       :						Dict							= tf.constant([[0, 1, 2, 3, 4, 5]]					)
           lowerCAmelCase       :						Tuple							= model(UpperCamelCase_					)[0]

           lowerCAmelCase       :						int							= [1, 6, 7_6_8]
           self.assertEqual(output.shape       ,					UpperCamelCase_					)

           lowerCAmelCase       :						List[Any]							= 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]       ,					UpperCamelCase_       ,					atol=1E-4					)






"""simple docstring"""





from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__		:  Optional[Any]     					=					logging.get_logger(__name__)

snake_case__		:  str     					=					{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''prophetnet'''
     __UpperCamelCase	=       ['''past_key_values''']
     __UpperCamelCase	=       {
         '''num_attention_heads''': '''num_encoder_attention_heads''',
     }






     def __init__(						self	:       Union[str, Any]       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[Union[str, Callable]] = "gelu"       ,					UpperCamelCase_	:       Optional[int] = 3_0_5_2_2       ,					UpperCamelCase_	:       Optional[int] = 1_0_2_4       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[int] = 5_1_2       ,					UpperCamelCase_	:       Optional[float] = 0.02       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 2       ,					UpperCamelCase_	:       Optional[int] = 3_2       ,					UpperCamelCase_	:       Optional[int] = 1_2_8       ,					UpperCamelCase_	:       Optional[bool] = False       ,					UpperCamelCase_	:       Optional[float] = 0.0       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 1       ,					UpperCamelCase_	:       Optional[int] = 2       ,					**UpperCamelCase_	:       List[Any]       ,					):
           lowerCAmelCase       :						Union[str, Any]							= vocab_size
           lowerCAmelCase       :						Any							= hidden_size
           lowerCAmelCase       :						Tuple							= encoder_ffn_dim
           lowerCAmelCase       :						str							= num_encoder_layers
           lowerCAmelCase       :						int							= num_encoder_attention_heads
           lowerCAmelCase       :						Optional[Any]							= decoder_ffn_dim
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_layers
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_attention_heads
           lowerCAmelCase       :						List[Any]							= max_position_embeddings
           lowerCAmelCase       :						Dict							= init_std  # Normal(0, this parameter)
           lowerCAmelCase       :						Optional[int]							= activation_function

           # parameters for prophetnet
           lowerCAmelCase       :						Tuple							= ngram
           lowerCAmelCase       :						List[Any]							= num_buckets
           lowerCAmelCase       :						Optional[Any]							= relative_max_distance
           lowerCAmelCase       :						Dict							= disable_ngram_loss
           lowerCAmelCase       :						Optional[int]							= eps

           # 3 Types of Dropout
           lowerCAmelCase       :						Any							= attention_dropout
           lowerCAmelCase       :						List[Any]							= activation_dropout
           lowerCAmelCase       :						Tuple							= dropout

           lowerCAmelCase       :						Optional[int]							= use_cache

           super().__init__(
               pad_token_id=UpperCamelCase_       ,					bos_token_id=UpperCamelCase_       ,					eos_token_id=UpperCamelCase_       ,					is_encoder_decoder=UpperCamelCase_       ,					add_cross_attention=UpperCamelCase_       ,					decoder_start_token_id=UpperCamelCase_       ,					**UpperCamelCase_       ,					)






     @property
     def   lowerCamelCase__  (						self	:       str					):
           return self.num_encoder_layers + self.num_decoder_layers






     @num_hidden_layers.setter
     def   lowerCamelCase__  (						self	:       Optional[Any]       ,					UpperCamelCase_	:       int					):
           raise NotImplementedError(
               '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
               ''' `num_decoder_layers`.'''					)


