






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ] )
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       def       _a   ( self							):
         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  ,      _lowerCamelCase=1							):
         # 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}-single'''  ,      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}  ,      metric_definitions=self.env.metric_definitions  ,      py_version='py36'  ,      )





       def       _a   ( self  ,      _lowerCamelCase							):
         TrainingJobAnalytics(_lowerCamelCase							).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''							)





       def       _a   ( self							):
         # create estimator
         UpperCamelCase_: Dict   				=    self.create_estimator()

         # run training
         estimator.fit()

         # result dataframe
         UpperCamelCase_: Dict   				=    TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

         # extract kpis
         UpperCamelCase_: str   				=    list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']							)
         UpperCamelCase_: Any   				=    list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']							)
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         UpperCamelCase_: Any   				=    (
             Session().describe_training_job(estimator.latest_training_job.name							).get('TrainingTimeInSeconds'  ,      9_9_9_9_9_9							)
         )

         # 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							)







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


A_		:		Tuple		     =			logging.get_logger(__name__)

A_		:		Dict		     =			{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									='''xglm'''
       a    :		List[Any]									=['''past_key_values''']

       a    :		Union[str, Any]									={
           '''num_attention_heads''': '''attention_heads''',
           '''hidden_size''': '''d_model''',
           '''num_hidden_layers''': '''num_layers''',
       }





       def __init__( self  ,      _lowerCamelCase=2_5_6_0_0_8  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=2_4  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    vocab_size
         UpperCamelCase_: Optional[int]   				=    max_position_embeddings
         UpperCamelCase_: List[str]   				=    d_model
         UpperCamelCase_: List[Any]   				=    ffn_dim
         UpperCamelCase_: List[Any]   				=    num_layers
         UpperCamelCase_: List[Any]   				=    attention_heads
         UpperCamelCase_: Tuple   				=    activation_function
         UpperCamelCase_: Tuple   				=    dropout
         UpperCamelCase_: Tuple   				=    attention_dropout
         UpperCamelCase_: Optional[Any]   				=    activation_dropout
         UpperCamelCase_: List[str]   				=    layerdrop
         UpperCamelCase_: Any   				=    init_std
         UpperCamelCase_: Any   				=    scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCamelCase_: Union[str, Any]   				=    use_cache

         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      decoder_start_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )