



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 lowerCAmelCase_       :







	'''simple docstring'''




	def __init__(	self   ,       lowerCamelCase   ,       lowerCamelCase=13   ,       lowerCamelCase=7   ,       lowerCamelCase=True   ,       lowerCamelCase=True   ,       lowerCamelCase=False   ,       lowerCamelCase=True   ,       lowerCamelCase=99   ,       lowerCamelCase=64   ,       lowerCamelCase=2   ,       lowerCamelCase=4   ,       lowerCamelCase=64   ,       lowerCamelCase="gelu"   ,       lowerCamelCase=0.1   ,       lowerCamelCase=0.1   ,       lowerCamelCase=512   ,       lowerCamelCase=16   ,       lowerCamelCase=2   ,       lowerCamelCase=0.0_2   ,       lowerCamelCase=3   ,       lowerCamelCase=4   ,       lowerCamelCase=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 				_A			(	self       ):



				'''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 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''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 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''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 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''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 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''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 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''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 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''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 				_A			(	self       ):



				'''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 lowerCAmelCase_       (   A_ ,A_ ,unittest.TestCase			):







	'''simple docstring'''




	A_       :     str									=		(
	    (
	        TFMPNetForMaskedLM,
	        TFMPNetForMultipleChoice,
	        TFMPNetForQuestionAnswering,
	        TFMPNetForSequenceClassification,
	        TFMPNetForTokenClassification,
	        TFMPNetModel,
	    )
	    if is_tf_available()
	    else ()
	)
	A_       :     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_       :     List[str]									=		False
	A_       :     str									=		False

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  TFMPNetModelTester(self       )
				a__					=  ConfigTester(self   ,       config_class=lowerCamelCase   ,       hidden_size=37       )

	def 				_A			(	self       ):



				'''simple docstring'''


				self.config_tester.run_common_tests()

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_mpnet_model(*lowerCamelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_mpnet_for_masked_lm(*lowerCamelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCamelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCamelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCamelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCamelCase       )







	@slow
	def 				_A			(	self       ):



				'''simple docstring'''


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


@require_tf
class lowerCAmelCase_       (   unittest.TestCase			):







	'''simple docstring'''




	@slow
	def 				_A			(	self       ):



				'''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, 768]
				self.assertEqual(output.shape   ,       lowerCamelCase       )

				a__					=  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]   ,       lowerCamelCase   ,       atol=1e-4       )






class lowerCAmelCase_       :







	'''simple docstring'''




	def __init__(	self   ,       lowerCamelCase=None       ):



				'''simple docstring'''


				a__					=  data
				a__					=  None







	def __repr__(	self       ):



				'''simple docstring'''


				a__					=  """"""
				a__					=  self
				while temp:
							string_rep += f'<{temp.data}> ---> '
							a__					=  temp.next
				string_rep += "<END>"
				return string_rep




def    UpperCAmelCase					(  lowercase__						:					Optional[int]	):



			'''simple docstring'''





			if not elements_list:
						raise Exception("""The Elements List is empty"""	)

			# Set first element as Head
			a__					=  Node(elements_list[0]	)
			a__					=  head
			# Loop through elements from position 1
			for data in elements_list[1:]:
						a__					=  Node(lowercase__	)
						a__					=  current.next
			return head


_lowercase				:					List[Any]     					=[1, 3, 5, 32, 44, 12, 43]
print(f'''List: {list_data}''')
print("""Creating Linked List from List.""")
_lowercase				:					Dict     					=make_linked_list(list_data)
print("""Linked List:""")





print(linked_list)

