

"""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 					_lowerCAmelCase  :







 """simple docstring"""







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


 def 					_lowercase (			self							:			List[str]	):
    return MPNetConfig.from_pretrained("microsoft/mpnet-base"	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	)

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

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

    __lowercase								=						self.get_config()
    return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


 def 					_lowercase (			self							:			Dict	):
    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 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			Optional[Any]	):
    __lowercase								=						MPNetModel(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    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 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			str	):
    __lowercase								=						MPNetForQuestionAnswering(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(
        UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					start_positions=UpperCAmelCase__,					end_positions=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 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						self.num_labels
    __lowercase								=						MPNetForSequenceClassification(UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)	)


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


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						self.num_labels
    __lowercase								=						MPNetForTokenClassification(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels)	)



 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.prepare_config_and_inputs()
    ((__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase))								=						config_and_inputs
    __lowercase								=						{"input_ids": input_ids, "attention_mask": input_mask}
    return config, inputs_dict







@require_torch
class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







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


 def 					_lowercase (			self							:			int	):
    __lowercase								=						MPNetModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					hidden_size=3_7	)


 def 					_lowercase (			self							:			Dict	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_sequence_classification(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_multiple_choice(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_token_classification(*UpperCAmelCase__	)



 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_question_answering(*UpperCAmelCase__	)







@require_torch
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @slow
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						MPNetModel.from_pretrained("microsoft/mpnet-base"	)
    __lowercase								=						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]]	)
    __lowercase								=						model(UpperCAmelCase__	)[0]
    __lowercase								=						torch.Size((1, 1_1, 7_6_8)	)
    self.assertEqual(output.shape,					UpperCAmelCase__	)
    __lowercase								=						torch.tensor(
        [[[-0.0_550, 0.1_943, -0.0_740], [-0.0_562, 0.2_211, -0.0_579], [-0.0_437, 0.3_337, -0.0_641]]]	)
    # compare the actual values for a slice.
    self.assertTrue(torch.allclose(output[:, :3, :3],					UpperCAmelCase__,					atol=1E-4	)	)







"""simple docstring"""





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
   from sagemaker import Session, TrainingJobAnalytics
   from sagemaker.huggingface import HuggingFace




@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"     ,"False"   )   ) is not True     ,reason="Skipping test because should only be run when releasing minor transformers version"     ,)
@pytest.mark.usefixtures("sm_env"   )
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]   )
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Optional[int]	):
    if self.framework == "pytorch":
       subprocess.run(
           F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split(),					encoding="utf-8",					check=UpperCAmelCase__,					)
    assert hasattr(self,					"env"	)


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			List[Any]	):
    # configuration for running training on smdistributed Model Parallel
    __lowercase								=						{
        "enabled": True,
        "processes_per_host": 8,
    }
    __lowercase								=						{
        "enabled": True,
        "parameters": {
            "microbatches": 4,
            "placement_strategy": "spread",
            "pipeline": "interleaved",
            "optimize": "speed",
            "partitions": 4,
            "ddp": True,
        },
    }

    __lowercase								=						{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

    __lowercase								=						"trainer" if self.script == "run_glue.py" else "smtrainer"
    # creates estimator
    return HuggingFace(
        entry_point=self.script,					source_dir=self.env.test_path,					role=self.env.role,					image_uri=self.env.image_uri,					base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""",					instance_count=UpperCAmelCase__,					instance_type=self.instance_type,					debugger_hook_config=UpperCAmelCase__,					hyperparameters={
            **self.env.hyperparameters,
            "model_name_or_path": self.model_name_or_path,
            "max_steps": 5_0_0,
        },					metric_definitions=self.env.metric_definitions,					distribution=UpperCAmelCase__,					py_version="py36",					)


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			int	):
    TrainingJobAnalytics(UpperCAmelCase__	).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""	)



 @parameterized.expand([(1,)]	)
 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Union[str, Any]	):
    # create estimator
    __lowercase								=						self.create_estimator(UpperCAmelCase__	)

    # run training
    estimator.fit()

    # result dataframe
    __lowercase								=						TrainingJobAnalytics(estimator.latest_training_job.name	).dataframe()

    # extract kpis
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]	)
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]	)
    # get train time from SageMaker job, this includes starting, preprocessing, stopping
    __lowercase								=						(
        Session().describe_training_job(estimator.latest_training_job.name	).get("TrainingTimeInSeconds",					9_9_9_9_9_9	)
    )

    # assert kpis
    assert train_runtime <= self.results["train_runtime"]
    assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy	)
    assert all(t <= self.results["eval_loss"] for t in eval_loss	)

    # dump tests result into json file to share in PR
    with open(F"""{estimator.latest_training_job.name}.json""",					"w"	) as outfile:
       json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss},					UpperCAmelCase__	)




