


"""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       lowerCamelCase__    ( unittest.TestCase      ):







						def __a    (				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=__lowerCAmelCase      ,      )
								assert hasattr(self      ,      'env'		)







						def __a    (				self :     List[Any]      ,      _lowercase :     int		):
								A       							=			{
								    'enabled': True,
								    'processes_per_host': 8,
								}
								A       							=			{
								    'enabled': True,
								    'parameters': {
								        'microbatches': 4,
								        'placement_strategy': 'spread',
								        'pipeline': 'interleaved',
								        'optimize': 'speed',
								        'partitions': 4,
								        'ddp': True,
								    },
								}

								A       							=			{'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

								A       							=			'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=__lowerCAmelCase      ,      instance_type=self.instance_type      ,      debugger_hook_config=__lowerCAmelCase      ,      hyperparameters={
								        **self.env.hyperparameters,
								        'model_name_or_path': self.model_name_or_path,
								        'max_steps': 500,
								    }      ,      metric_definitions=self.env.metric_definitions      ,      distribution=__lowerCAmelCase      ,      py_version='py36'      ,      )







						def __a    (				self :     Optional[int]      ,      _lowercase :     int		):
								TrainingJobAnalytics(__lowerCAmelCase		).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'		)







						@parameterized.expand([(1,)]		)
						def __a    (				self :     Tuple      ,      _lowercase :     Optional[int]		):
								A       							=			self.create_estimator(__lowerCAmelCase		)

								# run training
								estimator.fit()

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

								# extract kpis
								A       							=			list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']		)
								A       							=			list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']		)
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								A       							=			(
								    Session().describe_training_job(estimator.latest_training_job.name		).get('TrainingTimeInSeconds'      ,      999_999		)
								)

								# 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}      ,      __lowerCAmelCase		)






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


__lowercase        =    {
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			'''albert'''



			def __init__(  self			,       __lowerCAmelCase=30000			,       __lowerCAmelCase=128			,       __lowerCAmelCase=4096			,       __lowerCAmelCase=12			,       __lowerCAmelCase=1			,       __lowerCAmelCase=64			,       __lowerCAmelCase=16384			,       __lowerCAmelCase=1			,       __lowerCAmelCase="gelu_new"			,       __lowerCAmelCase=0			,       __lowerCAmelCase=0			,       __lowerCAmelCase=512			,       __lowerCAmelCase=2			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=1E-1_2			,       __lowerCAmelCase=0.1			,       __lowerCAmelCase="absolute"			,       __lowerCAmelCase=0			,       __lowerCAmelCase=2			,       __lowerCAmelCase=3			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(pad_token_id=__lowerCAmelCase			,       bos_token_id=__lowerCAmelCase			,       eos_token_id=__lowerCAmelCase			,       **__lowerCAmelCase)

					lowerCAmelCase     =    vocab_size
					lowerCAmelCase     =    embedding_size
					lowerCAmelCase     =    hidden_size
					lowerCAmelCase     =    num_hidden_layers
					lowerCAmelCase     =    num_hidden_groups
					lowerCAmelCase     =    num_attention_heads
					lowerCAmelCase     =    inner_group_num
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    intermediate_size
					lowerCAmelCase     =    hidden_dropout_prob
					lowerCAmelCase     =    attention_probs_dropout_prob
					lowerCAmelCase     =    max_position_embeddings
					lowerCAmelCase     =    type_vocab_size
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    layer_norm_eps
					lowerCAmelCase     =    classifier_dropout_prob
					lowerCAmelCase     =    position_embedding_type







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			@property
			def 				a_			(  self):




					"""simple docstring"""


					if self.task == "multiple-choice":
							lowerCAmelCase     =    {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							lowerCAmelCase     =    {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					    ])




