





"""simple docstring"""


import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MPNetForMaskedLM,
		    MPNetForMultipleChoice,
		    MPNetForQuestionAnswering,
		    MPNetForSequenceClassification,
		    MPNetForTokenClassification,
		    MPNetModel,
		)






class 							lowercase__			:

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A=1_3						,   _A=7						,   _A=True						,   _A=True						,   _A=False						,   _A=True						,   _A=9_9						,   _A=6_4						,   _A=5						,   _A=4						,   _A=6_4						,   _A="gelu"						,   _A=0.1						,   _A=0.1						,   _A=5_1_2						,   _A=1_6						,   _A=2						,   _A=0.02						,   _A=3						,   _A=4						,   _A=None						,   ):






									'''simple docstring'''







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







			def   _a      (      self    ):






									'''simple docstring'''







									return MPNetConfig.from_pretrained("""microsoft/mpnet-base"""    )







			def   _a      (      self    ):






									'''simple docstring'''







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

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

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

									UpperCamelCase				:		Optional[Any]				   =			self.get_config()
									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







			def   _a      (      self    ):






									'''simple docstring'''







									return 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						,   )







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			MPNetModel(config=_A    )
									model.to(_A    )
									model.eval()
									UpperCamelCase				:		Union[str, Any]				   =			model(_A						,   _A    )
									UpperCamelCase				:		List[Any]				   =			model(_A    )
									self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)    )
									self.parent.assertEqual(result.pooler_output.shape						,   (self.batch_size, self.hidden_size)    )







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			MPNetForQuestionAnswering(config=_A    )
									model.to(_A    )
									model.eval()
									UpperCamelCase				:		Tuple				   =			model(
									    _A						,   attention_mask=_A						,   start_positions=_A						,   end_positions=_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   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			self.num_labels
									UpperCamelCase				:		int				   =			MPNetForSequenceClassification(_A    )
									model.to(_A    )
									model.eval()
									UpperCamelCase				:		Optional[Any]				   =			model(_A						,   attention_mask=_A						,   labels=_A    )
									self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)    )







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			self.num_choices
									UpperCamelCase				:		Optional[int]				   =			MPNetForMultipleChoice(config=_A    )
									model.to(_A    )
									model.eval()
									UpperCamelCase				:		str				   =			input_ids.unsqueeze(1    ).expand(-1						,   self.num_choices						,   -1    ).contiguous()
									UpperCamelCase				:		List[Any]				   =			input_mask.unsqueeze(1    ).expand(-1						,   self.num_choices						,   -1    ).contiguous()
									UpperCamelCase				:		Union[str, Any]				   =			model(
									    _A						,   attention_mask=_A						,   labels=_A						,   )
									self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_choices)    )







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.num_labels
									UpperCamelCase				:		List[Any]				   =			MPNetForTokenClassification(config=_A    )
									model.to(_A    )
									model.eval()
									UpperCamelCase				:		Any				   =			model(_A						,   attention_mask=_A						,   labels=_A    )
									self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.num_labels)    )







			def   _a      (      self    ):






									'''simple docstring'''







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






@require_torch
class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     unittest.TestCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				Tuple										=  (
			    (
			        MPNetForMaskedLM,
			        MPNetForMultipleChoice,
			        MPNetForQuestionAnswering,
			        MPNetForSequenceClassification,
			        MPNetForTokenClassification,
			        MPNetModel,
			    )
			    if is_torch_available()
			    else ()
			)
			__lowerCAmelCase     :				Any										=  (
			    {
			        """feature-extraction""": MPNetModel,
			        """fill-mask""": MPNetForMaskedLM,
			        """question-answering""": MPNetForQuestionAnswering,
			        """text-classification""": MPNetForSequenceClassification,
			        """token-classification""": MPNetForTokenClassification,
			        """zero-shot""": MPNetForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			__lowerCAmelCase     :				Union[str, Any]										=  False
			__lowerCAmelCase     :				int										=  True







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			MPNetModelTester(self    )
									UpperCamelCase				:		Optional[int]				   =			ConfigTester(self						,   config_class=_A						,   hidden_size=3_7    )







			def   _a      (      self    ):






									'''simple docstring'''







									self.config_tester.run_common_tests()







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_model(*_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_sequence_classification(*_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_multiple_choice(*_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_token_classification(*_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_question_answering(*_A    )






@require_torch
class 							lowercase__			(   unittest.TestCase	):

			"""simple docstring"""







			@slow
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			MPNetModel.from_pretrained("""microsoft/mpnet-base"""    )
									UpperCamelCase				:		Tuple				   =			torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]    )
									UpperCamelCase				:		Any				   =			model(_A    )[0]
									UpperCamelCase				:		Tuple				   =			torch.Size((1, 1_1, 7_6_8)    )
									self.assertEqual(output.shape						,   _A    )
									UpperCamelCase				:		Optional[Any]				   =			torch.tensor(
									    [[[-0.05_50, 0.19_43, -0.07_40], [-0.05_62, 0.22_11, -0.05_79], [-0.04_37, 0.33_37, -0.06_41]]]    )
									# compare the actual values for a slice.
									self.assertTrue(torch.allclose(output[:, :3, :3]						,   _A						,   atol=1e-4    )    )







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

