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   :   Dict			,	A__   :   int			,	A__   :   Optional[int]=13			,	A__   :   str=7			,	A__   :   List[Any]=True			,	A__   :   str=True			,	A__   :   Optional[Any]=False			,	A__   :   str=True			,	A__   :   Tuple=99			,	A__   :   Dict=64			,	A__   :   Union[str, Any]=2			,	A__   :   Union[str, Any]=4			,	A__   :   List[str]=64			,	A__   :   str="gelu"			,	A__   :   Optional[int]=0.1			,	A__   :   Union[str, Any]=0.1			,	A__   :   Any=5_12			,	A__   :   Optional[Any]=16			,	A__   :   str=2			,	A__   :   str=0.02			,	A__   :   List[str]=3			,	A__   :   List[Any]=4			,	A__   :   Tuple=None			,	)							->     int:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   Optional[int]   )							->     List[str]:




         '''simple docstring'''




         snake_case_     :			List[str]       =       ids_tensor([self.batch_size, self.seq_length]			,	self.vocab_size   )

         snake_case_     :			Tuple       =       None
         if self.use_input_mask:
               snake_case_     :			Optional[Any]       =       random_attention_mask([self.batch_size, self.seq_length]   )

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

         snake_case_     :			Optional[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 					UpperCAmelCase__       (				self   :   List[str]			,	A__   :   Tuple			,	A__   :   Optional[Any]			,	A__   :   Union[str, Any]			,	A__   :   str			,	A__   :   Any			,	A__   :   str   )							->     Tuple:




         '''simple docstring'''




         snake_case_     :			List[str]       =       TFMPNetModel(config=A__   )
         snake_case_     :			Dict       =       {"input_ids": input_ids, "attention_mask": input_mask}
         snake_case_     :			Union[str, Any]       =       model(A__   )
         snake_case_     :			Any       =       [input_ids, input_mask]
         snake_case_     :			List[str]       =       model(A__   )
         self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size)   )



   def 					UpperCAmelCase__       (				self   :   Dict			,	A__   :   Optional[Any]			,	A__   :   List[Any]			,	A__   :   Union[str, Any]			,	A__   :   Any			,	A__   :   Tuple			,	A__   :   Union[str, Any]   )							->     str:




         '''simple docstring'''




         snake_case_     :			Any       =       TFMPNetForMaskedLM(config=A__   )
         snake_case_     :			Optional[Any]       =       {"input_ids": input_ids, "attention_mask": input_mask}
         snake_case_     :			int       =       model(A__   )
         self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.vocab_size)   )



   def 					UpperCAmelCase__       (				self   :   Any			,	A__   :   Optional[Any]			,	A__   :   List[str]			,	A__   :   Union[str, Any]			,	A__   :   str			,	A__   :   List[str]			,	A__   :   List[str]   )							->     str:




         '''simple docstring'''




         snake_case_     :			List[Any]       =       TFMPNetForQuestionAnswering(config=A__   )
         snake_case_     :			Any       =       {
             "input_ids": input_ids,
             "attention_mask": input_mask,
         }
         snake_case_     :			List[Any]       =       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 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   Optional[Any]			,	A__   :   Union[str, Any]			,	A__   :   str			,	A__   :   Union[str, Any]			,	A__   :   List[str]			,	A__   :   Tuple   )							->     str:




         '''simple docstring'''




         snake_case_     :			str       =       self.num_labels
         snake_case_     :			Tuple       =       TFMPNetForSequenceClassification(A__   )
         snake_case_     :			List[str]       =       {"input_ids": input_ids, "attention_mask": input_mask}
         snake_case_     :			List[str]       =       model(A__   )
         self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels)   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   Tuple			,	A__   :   str			,	A__   :   Tuple			,	A__   :   Union[str, Any]			,	A__   :   List[Any]			,	A__   :   Union[str, Any]   )							->     Dict:




         '''simple docstring'''




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



   def 					UpperCAmelCase__       (				self   :   Tuple			,	A__   :   List[str]			,	A__   :   List[str]			,	A__   :   Any			,	A__   :   Dict			,	A__   :   Dict			,	A__   :   List[Any]   )							->     List[str]:




         '''simple docstring'''




         snake_case_     :			List[str]       =       self.num_labels
         snake_case_     :			Any       =       TFMPNetForTokenClassification(A__   )
         snake_case_     :			Dict       =       {"input_ids": input_ids, "attention_mask": input_mask}
         snake_case_     :			Any       =       model(A__   )
         self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.num_labels)   )



   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Dict:




         '''simple docstring'''




         snake_case_     :			Optional[int]       =       self.prepare_config_and_inputs()
         ((snake_case_)  ,(snake_case_)  ,(snake_case_)  ,(snake_case_)  ,(snake_case_)  ,(snake_case_))     :			List[Any]       =       config_and_inputs
         snake_case_     :			str       =       {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict



@require_tf
class 					snake_case__       (				_UpperCamelCase					,						_UpperCamelCase					,						unittest.TestCase							):
   _SCREAMING_SNAKE_CASE	: Union[str, Any]						    =      (
       (
           TFMPNetForMaskedLM,
           TFMPNetForMultipleChoice,
           TFMPNetForQuestionAnswering,
           TFMPNetForSequenceClassification,
           TFMPNetForTokenClassification,
           TFMPNetModel,
       )
       if is_tf_available()
       else ()
   )
   _SCREAMING_SNAKE_CASE	: 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 {}
   )
   _SCREAMING_SNAKE_CASE	: int						    =      False
   _SCREAMING_SNAKE_CASE	: Any						    =      False



   def 					UpperCAmelCase__       (				self   :   Any   )							->     int:




         '''simple docstring'''




         snake_case_     :			List[Any]       =       TFMPNetModelTester(self   )
         snake_case_     :			Optional[Any]       =       ConfigTester(self			,	config_class=A__			,	hidden_size=37   )



   def 					UpperCAmelCase__       (				self   :   List[Any]   )							->     Any:




         '''simple docstring'''




         self.config_tester.run_common_tests()



   def 					UpperCAmelCase__       (				self   :   List[str]   )							->     Optional[int]:




         '''simple docstring'''




         snake_case_     :			str       =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_model(*A__   )



   def 					UpperCAmelCase__       (				self   :   int   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			str       =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_masked_lm(*A__   )



   def 					UpperCAmelCase__       (				self   :   int   )							->     List[str]:




         '''simple docstring'''




         snake_case_     :			List[str]       =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_question_answering(*A__   )



   def 					UpperCAmelCase__       (				self   :   Optional[int]   )							->     Optional[int]:




         '''simple docstring'''




         snake_case_     :			Optional[Any]       =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_sequence_classification(*A__   )



   def 					UpperCAmelCase__       (				self   :   str   )							->     int:




         '''simple docstring'''




         snake_case_     :			str       =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_multiple_choice(*A__   )



   def 					UpperCAmelCase__       (				self   :   str   )							->     Union[str, Any]:




         '''simple docstring'''




         snake_case_     :			Dict       =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_token_classification(*A__   )



   @slow
   def 					UpperCAmelCase__       (				self   :   Optional[int]   )							->     Dict:




         '''simple docstring'''




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



@require_tf
class 					snake_case__       (				unittest.TestCase							):



   @slow
   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[int]:




         '''simple docstring'''




         snake_case_     :			Optional[Any]       =       TFMPNetModel.from_pretrained("microsoft/mpnet-base"   )
         snake_case_     :			List[str]       =       tf.constant([[0, 1, 2, 3, 4, 5]]   )
         snake_case_     :			Optional[int]       =       model(A__   )[0]

         snake_case_     :			Tuple       =       [1, 6, 7_68]
         self.assertEqual(output.shape			,	A__   )

         snake_case_     :			Optional[int]       =       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]			,	A__			,	atol=1E-4   )




import math



class 					snake_case__       :



   def __init__(				self   :   int			,	A__   :   str   )							->     int:




         '''simple docstring'''




         snake_case_     :			Tuple       =       len(A__   )
         snake_case_     :			Union[str, Any]       =       [0] * (
             4 * self.N
         )  # approximate the overall size of segment tree with array N
         self.build(1			,	0			,	self.N - 1   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   Tuple   )							->     Optional[int]:




         '''simple docstring'''




         return idx * 2



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   List[str]   )							->     Dict:




         '''simple docstring'''




         return idx * 2 + 1



   def 					UpperCAmelCase__       (				self   :   str			,	A__   :   Any			,	A__   :   Optional[int]			,	A__   :   Tuple   )							->     List[str]:  # noqa: E741




         '''simple docstring'''




         if l == r:
               snake_case_     :			str       =       A[l]
         else:
               snake_case_     :			List[str]       =       (l + r) // 2
               self.build(self.left(A__   )			,	A__			,	A__   )
               self.build(self.right(A__   )			,	mid + 1			,	A__   )
               snake_case_     :			Union[str, Any]       =       max(self.st[self.left(A__   )]			,	self.st[self.right(A__   )]   )



   def 					UpperCAmelCase__       (				self   :   Optional[Any]			,	A__   :   List[str]			,	A__   :   Union[str, Any]			,	A__   :   str   )							->     int:




         '''simple docstring'''




         return self.update_recursive(1			,	0			,	self.N - 1			,	a - 1			,	b - 1			,	A__   )



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   Optional[Any]			,	A__   :   List[str]			,	A__   :   Optional[Any]			,	A__   :   Union[str, Any]			,	A__   :   List[Any]			,	A__   :   str   )							->     Any:  # noqa: E741




         '''simple docstring'''




         if r < a or l > b:
               return True
         if l == r:
               snake_case_     :			List[str]       =       val
               return True
         snake_case_     :			Dict       =       (l + r) // 2
         self.update_recursive(self.left(A__   )			,	A__			,	A__			,	A__			,	A__			,	A__   )
         self.update_recursive(self.right(A__   )			,	mid + 1			,	A__			,	A__			,	A__			,	A__   )
         snake_case_     :			Tuple       =       max(self.st[self.left(A__   )]			,	self.st[self.right(A__   )]   )
         return True



   def 					UpperCAmelCase__       (				self   :   Optional[int]			,	A__   :   List[Any]			,	A__   :   Optional[Any]   )							->     str:




         '''simple docstring'''




         return self.query_recursive(1			,	0			,	self.N - 1			,	a - 1			,	b - 1   )



   def 					UpperCAmelCase__       (				self   :   Union[str, Any]			,	A__   :   Any			,	A__   :   int			,	A__   :   Tuple			,	A__   :   Tuple			,	A__   :   Dict   )							->     str:  # noqa: E741




         '''simple docstring'''




         if r < a or l > b:
               return -math.inf
         if l >= a and r <= b:
               return self.st[idx]
         snake_case_     :			Dict       =       (l + r) // 2
         snake_case_     :			Optional[Any]       =       self.query_recursive(self.left(A__   )			,	A__			,	A__			,	A__			,	A__   )
         snake_case_     :			Any       =       self.query_recursive(self.right(A__   )			,	mid + 1			,	A__			,	A__			,	A__   )
         return max(A__			,	A__   )



   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			int       =       []
         for i in range(1			,	N + 1   ):
               show_list += [self.query(A__			,	A__   )]
         print(A__   )


if __name__ == "__main__":
     UpperCAmelCase							=       [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
     UpperCAmelCase							=       1_5
     UpperCAmelCase							=       SegmentTree(A)
     print(segt.query(4, 6))
     print(segt.query(7, 1_1))
     print(segt.query(7, 1_2))
     segt.update(1, 3, 1_1_1)
     print(segt.query(1, 1_5))
     segt.update(7, 8, 2_3_5)
     segt.show_data()



