

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_50, "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_00, "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_00, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]    )
class        A_ (   unittest.TestCase    ):


    '''simple docstring'''






    def   lowerCAmelCase_    (self	)  ->   Tuple:
        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=A_  ,   )
        assert hasattr(self  ,   '''env'''	)


    def   lowerCAmelCase_    (self  ,   lowercase__	)  ->   str:
        __UpperCAmelCase					       =      F'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
        # distributed data settings
        __UpperCAmelCase					       =      {'''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=A_  ,   instance_count=A_  ,   instance_type=self.instance_type  ,   debugger_hook_config=A_  ,   hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}  ,   metric_definitions=self.env.metric_definitions  ,   distribution=A_  ,   py_version='''py36'''  ,   )


    def   lowerCAmelCase_    (self  ,   lowercase__	)  ->   Optional[Any]:
        TrainingJobAnalytics(A_	).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''	)






    @parameterized.expand([(2,)]	)
    def   lowerCAmelCase_    (self  ,   lowercase__	)  ->   Optional[Any]:
        # create estimator
        __UpperCAmelCase					       =      self.create_estimator(A_	)

        # run training
        estimator.fit()

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

        # extract kpis
        __UpperCAmelCase					       =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']	)
        __UpperCAmelCase					       =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']	)
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        __UpperCAmelCase					       =      (
            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}  ,   A_	)








"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__						: str    =				logging.get_logger(__name__)

__magic_name__						: List[Any]    =				{
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''visual_bert'''




							def __init__(     self:    Any     ,							A_:    List[str]=3_05_22     ,							A_:    Tuple=7_68     ,							A_:    Dict=5_12     ,							A_:    List[Any]=12     ,							A_:    str=12     ,							A_:    Dict=30_72     ,							A_:    str="gelu"     ,							A_:    Union[str, Any]=0.1     ,							A_:    Any=0.1     ,							A_:    List[Any]=5_12     ,							A_:    int=2     ,							A_:    int=0.02     ,							A_:    Any=1E-12     ,							A_:    List[str]=False     ,							A_:    str=True     ,							A_:    Union[str, Any]=1     ,							A_:    Optional[int]=0     ,							A_:    Dict=2     ,							**A_:    Optional[int]     ,							):
										super().__init__(pad_token_id=A_     ,							bos_token_id=A_     ,							eos_token_id=A_     ,							**A_   )

										__lowerCamelCase          =      vocab_size
										__lowerCamelCase          =      max_position_embeddings
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      visual_embedding_dim
										__lowerCamelCase          =      num_hidden_layers
										__lowerCamelCase          =      num_attention_heads
										__lowerCamelCase          =      intermediate_size
										__lowerCamelCase          =      hidden_act
										__lowerCamelCase          =      hidden_dropout_prob
										__lowerCamelCase          =      attention_probs_dropout_prob
										__lowerCamelCase          =      initializer_range
										__lowerCamelCase          =      type_vocab_size
										__lowerCamelCase          =      layer_norm_eps
										__lowerCamelCase          =      bypass_transformer
										__lowerCamelCase          =      special_visual_initialize




