from typing import List

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


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Tuple      =			'efficientformer'


   def __init__(self     :						Any       ,  __UpperCAmelCase     :						List[int] = [3, 2, 6, 4]       ,  __UpperCAmelCase     :						List[int] = [4_8, 9_6, 2_2_4, 4_4_8]       ,  __UpperCAmelCase     :						List[bool] = [True, True, True, True]       ,  __UpperCAmelCase     :						int = 4_4_8       ,  __UpperCAmelCase     :						int = 3_2       ,  __UpperCAmelCase     :						int = 4       ,  __UpperCAmelCase     :						int = 7       ,  __UpperCAmelCase     :						int = 5       ,  __UpperCAmelCase     :						int = 8       ,  __UpperCAmelCase     :						int = 4       ,  __UpperCAmelCase     :						float = 0.0       ,  __UpperCAmelCase     :						int = 1_6       ,  __UpperCAmelCase     :						int = 3       ,  __UpperCAmelCase     :						int = 3       ,  __UpperCAmelCase     :						int = 3       ,  __UpperCAmelCase     :						int = 2       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						float = 0.0       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						bool = True       ,  __UpperCAmelCase     :						float = 1E-5       ,  __UpperCAmelCase     :						str = "gelu"       ,  __UpperCAmelCase     :						float = 0.02       ,  __UpperCAmelCase     :						float = 1E-12       ,  __UpperCAmelCase     :						int = 2_2_4       ,  __UpperCAmelCase     :						float = 1E-05       ,  **__UpperCAmelCase     :						Dict       ,  )						->				None:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase		)

         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  hidden_dropout_prob
         UpperCAmelCase__								=  hidden_sizes
         UpperCAmelCase__								=  num_hidden_layers
         UpperCAmelCase__								=  num_attention_heads
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  layer_norm_eps
         UpperCAmelCase__								=  patch_size
         UpperCAmelCase__								=  num_channels
         UpperCAmelCase__								=  depths
         UpperCAmelCase__								=  mlp_expansion_ratio
         UpperCAmelCase__								=  downsamples
         UpperCAmelCase__								=  dim
         UpperCAmelCase__								=  key_dim
         UpperCAmelCase__								=  attention_ratio
         UpperCAmelCase__								=  resolution
         UpperCAmelCase__								=  pool_size
         UpperCAmelCase__								=  downsample_patch_size
         UpperCAmelCase__								=  downsample_stride
         UpperCAmelCase__								=  downsample_pad
         UpperCAmelCase__								=  drop_path_rate
         UpperCAmelCase__								=  num_metaad_blocks
         UpperCAmelCase__								=  distillation
         UpperCAmelCase__								=  use_layer_scale
         UpperCAmelCase__								=  layer_scale_init_value
         UpperCAmelCase__								=  image_size
         UpperCAmelCase__								=  batch_norm_eps




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': 6_50, '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': 6_00, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]     )
class      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						int		)						->				Optional[Any]:
         """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 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Optional[int]=1		)						->				Dict:
         """simple docstring"""



         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=__UpperCAmelCase       ,  instance_type=self.instance_type       ,  debugger_hook_config=__UpperCAmelCase       ,  hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,  metric_definitions=self.env.metric_definitions       ,  py_version="py36"       ,  )


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Tuple		)						->				Optional[int]:
         """simple docstring"""



         TrainingJobAnalytics(__UpperCAmelCase		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)





   def 			lowercase_      (self     :						Any		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.create_estimator()

         # 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"       ,  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}       ,  __UpperCAmelCase		)



