
"""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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]    )
class __lowerCAmelCase	(   unittest.TestCase    ):





     def 		UpperCAmelCase      (			self   ):

      '''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=_lowerCAmelCase			,  )
      assert hasattr(self			,  'env'   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= {
          'enabled': True,
          'processes_per_host': 8,
      }
      __UpperCamelCase       						= {
          'enabled': True,
          'parameters': {
              'microbatches': 4,
              'placement_strategy': 'spread',
              'pipeline': 'interleaved',
              'optimize': 'speed',
              'partitions': 4,
              'ddp': True,
          },
      }

      __UpperCamelCase       						= {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

      __UpperCamelCase       						= 'trainer' if self.script == 'run_glue.py' else 'smtrainer'
      # 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}-{instance_count}-smp-{name_extension}'			,  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,
              'max_steps': 500,
          }			,  metric_definitions=self.env.metric_definitions			,  distribution=_lowerCAmelCase			,  py_version='py36'			,  )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      TrainingJobAnalytics(_lowerCAmelCase   ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'   )







     @parameterized.expand([(1,)]   )
     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.create_estimator(_lowerCAmelCase   )

      # 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'			,  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}			,  _lowerCAmelCase   )





"""simple docstring"""

from typing import List

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


UpperCamelCase			:       Tuple									=				logging.get_logger(__name__)

UpperCamelCase			:       List[str]									=				{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"efficientformer"





     def __init__(			self			,  __UpperCAmelCase = [3, 2, 6, 4]			,  __UpperCAmelCase = [48, 96, 224, 448]			,  __UpperCAmelCase = [True, True, True, True]			,  __UpperCAmelCase = 448			,  __UpperCAmelCase = 32			,  __UpperCAmelCase = 4			,  __UpperCAmelCase = 7			,  __UpperCAmelCase = 5			,  __UpperCAmelCase = 8			,  __UpperCAmelCase = 4			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = 16			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 2			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = True			,  __UpperCAmelCase = True			,  __UpperCAmelCase = 1E-5			,  __UpperCAmelCase = "gelu"			,  __UpperCAmelCase = 0.0_2			,  __UpperCAmelCase = 1E-12			,  __UpperCAmelCase = 224			,  __UpperCAmelCase = 1E-05			,  **__UpperCAmelCase			,  ):

      '''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



