


"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase						:				int             =      {
    "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 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """albert"""







						def __init__(							self   :    int			,			_lowercase   :    Any=30_000			,			_lowercase   :    int=128			,			_lowercase   :    Optional[Any]=4_096			,			_lowercase   :    List[Any]=12			,			_lowercase   :    List[str]=1			,			_lowercase   :    List[Any]=64			,			_lowercase   :    List[str]=16_384			,			_lowercase   :    Optional[int]=1			,			_lowercase   :    Tuple="gelu_new"			,			_lowercase   :    Any=0			,			_lowercase   :    Any=0			,			_lowercase   :    Optional[Any]=512			,			_lowercase   :    Union[str, Any]=2			,			_lowercase   :    Dict=0.0_2			,			_lowercase   :    str=1e-12			,			_lowercase   :    Tuple=0.1			,			_lowercase   :    int="absolute"			,			_lowercase   :    Optional[Any]=0			,			_lowercase   :    int=2			,			_lowercase   :    Optional[int]=3			,			**_lowercase   :    List[str]			,			):
								super().__init__(pad_token_id=_lowercase			,			bos_token_id=_lowercase			,			eos_token_id=_lowercase			,			**_lowercase		)

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



class 			lowerCamelCase__						(   UpperCAmelCase_			):







						@property
						def __a						(							self   :    Union[str, Any]		):
								if self.task == "multiple-choice":
										A					       = {0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
										A					       = {0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								        ('token_type_ids', dynamic_axis),
								    ]		)









"""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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]			)
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=_lowercase			,			)
								assert hasattr(self			,			'env'		)







						def __a						(							self   :    Optional[int]			,			_lowercase   :    int		):
								A					       = f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
								# distributed data settings
								A					       = {'smdistributed': {'dataparallel': {'enabled': True}}} if self.script != 'run_ddp.py' else None

								# 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=_lowercase			,			instance_count=_lowercase			,			instance_type=self.instance_type			,			debugger_hook_config=_lowercase			,			hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}			,			metric_definitions=self.env.metric_definitions			,			distribution=_lowercase			,			py_version='py36'			,			)







						def __a						(							self   :    Tuple			,			_lowercase   :    List[str]		):
								TrainingJobAnalytics(_lowercase		).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'		)







						@parameterized.expand([(2,)]		)
						def __a						(							self   :    List[Any]			,			_lowercase   :    Union[str, Any]		):
								# create estimator
								A					       = self.create_estimator(_lowercase		)

								# 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}			,			_lowercase		)





