"""simple docstring"""




import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.bert.modeling_flax_bert import (
			    FlaxBertForMaskedLM,
			    FlaxBertForMultipleChoice,
			    FlaxBertForNextSentencePrediction,
			    FlaxBertForPreTraining,
			    FlaxBertForQuestionAnswering,
			    FlaxBertForSequenceClassification,
			    FlaxBertForTokenClassification,
			    FlaxBertModel,
			)

class       a_      (     unittest.TestCase			):




					def __init__(	self						:     Tuple  ,						snake_case__						:     List[Any]  ,						snake_case__						:     List[Any]=13  ,						snake_case__						:     List[Any]=7  ,						snake_case__						:     int=True  ,						snake_case__						:     List[Any]=True  ,						snake_case__						:     str=True  ,						snake_case__						:     Any=True  ,						snake_case__						:     List[Any]=99  ,						snake_case__						:     Any=32  ,						snake_case__						:     Dict=5  ,						snake_case__						:     List[str]=4  ,						snake_case__						:     Tuple=37  ,						snake_case__						:     Dict="gelu"  ,						snake_case__						:     Optional[int]=0.1  ,						snake_case__						:     Any=0.1  ,						snake_case__						:     Optional[Any]=512  ,						snake_case__						:     Union[str, Any]=16  ,						snake_case__						:     Optional[Any]=2  ,						snake_case__						:     int=0.02  ,						snake_case__						:     str=4  ,						):
							lowerCAmelCase__        =   parent
							lowerCAmelCase__        =   batch_size
							lowerCAmelCase__        =   seq_length
							lowerCAmelCase__        =   is_training
							lowerCAmelCase__        =   use_attention_mask
							lowerCAmelCase__        =   use_token_type_ids
							lowerCAmelCase__        =   use_labels
							lowerCAmelCase__        =   vocab_size
							lowerCAmelCase__        =   hidden_size
							lowerCAmelCase__        =   num_hidden_layers
							lowerCAmelCase__        =   num_attention_heads
							lowerCAmelCase__        =   intermediate_size
							lowerCAmelCase__        =   hidden_act
							lowerCAmelCase__        =   hidden_dropout_prob
							lowerCAmelCase__        =   attention_probs_dropout_prob
							lowerCAmelCase__        =   max_position_embeddings
							lowerCAmelCase__        =   type_vocab_size
							lowerCAmelCase__        =   type_sequence_label_size
							lowerCAmelCase__        =   initializer_range
							lowerCAmelCase__        =   num_choices




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

							lowerCAmelCase__        =   None
							if self.use_attention_mask:
									lowerCAmelCase__        =   random_attention_mask([self.batch_size, self.seq_length]     )

							lowerCAmelCase__        =   None
							if self.use_token_type_ids:
									lowerCAmelCase__        =   ids_tensor([self.batch_size, self.seq_length]  ,						self.type_vocab_size     )

							lowerCAmelCase__        =   BertConfig(
							    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  ,						type_vocab_size=self.type_vocab_size  ,						is_decoder=snake_case__  ,						initializer_range=self.initializer_range  ,						)

							return config, input_ids, token_type_ids, attention_mask




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   config_and_inputs
							lowerCAmelCase__        =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
							return config, inputs_dict




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   config_and_inputs

							lowerCAmelCase__        =   True
							lowerCAmelCase__        =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
							lowerCAmelCase__        =   ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2     )

							return (
							    config,
							    input_ids,
							    attention_mask,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)

@require_flax
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Tuple			=    True

					UpperCamelCase_ :	int			=    (
					    (
					        FlaxBertModel,
					        FlaxBertForPreTraining,
					        FlaxBertForMaskedLM,
					        FlaxBertForMultipleChoice,
					        FlaxBertForQuestionAnswering,
					        FlaxBertForNextSentencePrediction,
					        FlaxBertForSequenceClassification,
					        FlaxBertForTokenClassification,
					        FlaxBertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   FlaxBertModelTester(self     )




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							# Only check this for base model, not necessary for all model classes.
							# This will also help speed-up tests.
							lowerCAmelCase__        =   FlaxBertModel.from_pretrained("""bert-base-cased"""     )
							lowerCAmelCase__        =   model(np.ones((1, 1)     )     )
							self.assertIsNotNone(snake_case__     )





"""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": 1600, "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": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]			)
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     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=snake_case__  ,						)
							assert hasattr(self  ,						"""env"""     )




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

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

							lowerCAmelCase__        =   """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=snake_case__  ,						instance_type=self.instance_type  ,						debugger_hook_config=snake_case__  ,						hyperparameters={
							        **self.env.hyperparameters,
							        """model_name_or_path""": self.model_name_or_path,
							        """max_steps""": 500,
							    }  ,						metric_definitions=self.env.metric_definitions  ,						distribution=snake_case__  ,						py_version="""py36"""  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     str     ):
							TrainingJobAnalytics(snake_case__     ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""     )




					@parameterized.expand([(1,)]     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     List[str]     ):
							# create estimator
							lowerCAmelCase__        =   self.create_estimator(snake_case__     )

							# run training
							estimator.fit()

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

							# extract kpis
							lowerCAmelCase__        =   list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]     )
							lowerCAmelCase__        =   list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]     )
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							lowerCAmelCase__        =   (
							    Session().describe_training_job(estimator.latest_training_job.name     ).get("""TrainingTimeInSeconds"""  ,						999999     )
							)

							# 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}  ,						snake_case__     )




