



"""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': 6_5_0, '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': 6_0_0, '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': 6_0_0, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]       )
class 					a	(  unittest.TestCase       ):



  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					str:


     '''simple docstring'''




     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 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					int		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=f'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
     # distributed data settings
     SCREAMING_SNAKE_CASE_: str     			={'''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=UpperCAmelCase__       ,		instance_count=UpperCAmelCase__       ,		instance_type=self.instance_type       ,		debugger_hook_config=UpperCAmelCase__       ,		hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}       ,		metric_definitions=self.env.metric_definitions       ,		distribution=UpperCAmelCase__       ,		py_version="""py36"""       ,		)



  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					Dict		)     ->					List[Any]:


     '''simple docstring'''




     TrainingJobAnalytics(UpperCAmelCase__		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)





  @parameterized.expand([(2,)]		)
  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					int		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=self.create_estimator(UpperCAmelCase__		)

     # run training
     estimator.fit()

     # result dataframe
     SCREAMING_SNAKE_CASE_: Any     			=TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

     # extract kpis
     SCREAMING_SNAKE_CASE_: Tuple     			=list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]		)
     # get train time from SageMaker job, this includes starting, preprocessing, stopping
     SCREAMING_SNAKE_CASE_: Dict     			=(
         Session().describe_training_job(estimator.latest_training_job.name		).get("""TrainingTimeInSeconds"""       ,		99_9999		)
     )

     # 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__		)








"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase     =			{
    """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	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Any     						=					'albert'



  def __init__(						self :					Dict       ,		lowerCAmelCase :					List[str]=3_0000       ,		lowerCAmelCase :					List[Any]=128       ,		lowerCAmelCase :					List[str]=4096       ,		lowerCAmelCase :					str=12       ,		lowerCAmelCase :					str=1       ,		lowerCAmelCase :					Tuple=64       ,		lowerCAmelCase :					Dict=1_6384       ,		lowerCAmelCase :					int=1       ,		lowerCAmelCase :					str="gelu_new"       ,		lowerCAmelCase :					Dict=0       ,		lowerCAmelCase :					Optional[Any]=0       ,		lowerCAmelCase :					str=512       ,		lowerCAmelCase :					Optional[int]=2       ,		lowerCAmelCase :					List[Any]=0.0_2       ,		lowerCAmelCase :					Union[str, Any]=1E-12       ,		lowerCAmelCase :					Tuple=0.1       ,		lowerCAmelCase :					List[Any]="absolute"       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					int=2       ,		lowerCAmelCase :					Optional[int]=3       ,		**lowerCAmelCase :					int       ,		)     ->					Tuple:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: List[str]     			=vocab_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=embedding_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_size
     SCREAMING_SNAKE_CASE_: Tuple     			=num_hidden_layers
     SCREAMING_SNAKE_CASE_: Any     			=num_hidden_groups
     SCREAMING_SNAKE_CASE_: List[Any]     			=num_attention_heads
     SCREAMING_SNAKE_CASE_: List[Any]     			=inner_group_num
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_act
     SCREAMING_SNAKE_CASE_: int     			=intermediate_size
     SCREAMING_SNAKE_CASE_: Any     			=hidden_dropout_prob
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=max_position_embeddings
     SCREAMING_SNAKE_CASE_: Any     			=type_vocab_size
     SCREAMING_SNAKE_CASE_: int     			=initializer_range
     SCREAMING_SNAKE_CASE_: List[Any]     			=layer_norm_eps
     SCREAMING_SNAKE_CASE_: Dict     			=classifier_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=position_embedding_type







class 					a	(  UpperCAmelCase__       ):



  @property
  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




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



