
"""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_6_0_0, '''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_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ] )
class       lowerCAmelCase_		(				unittest.TestCase ):



							'''simple docstring'''





							def    A__						( 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=snake_case_    ,				)
													assert hasattr(self    ,				"""env"""				)




							def    A__						( self    ,				snake_case_				)       ->  Optional[int]:
													# configuration for running training on smdistributed Model Parallel
													__lowerCAmelCase     =  {
													    """enabled""": True,
													    """processes_per_host""": 8,
													}
													__lowerCAmelCase     =  {
													    """enabled""": True,
													    """parameters""": {
													        """microbatches""": 4,
													        """placement_strategy""": """spread""",
													        """pipeline""": """interleaved""",
													        """optimize""": """speed""",
													        """partitions""": 4,
													        """ddp""": True,
													    },
													}

													__lowerCAmelCase     =  {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

													__lowerCAmelCase     =  """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=snake_case_    ,				instance_type=self.instance_type    ,				debugger_hook_config=snake_case_    ,				hyperparameters={
													        **self.env.hyperparameters,
													        """model_name_or_path""": self.model_name_or_path,
													        """max_steps""": 500,
													    }    ,				metric_definitions=self.env.metric_definitions    ,				distribution=snake_case_    ,				py_version="""py36"""    ,				)




							def    A__						( self    ,				snake_case_				)       ->  Optional[Any]:
													TrainingJobAnalytics(snake_case_				).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""				)




							@parameterized.expand([(1,)]				)
							def    A__						( self    ,				snake_case_				)       ->  int:
													# create estimator
													__lowerCAmelCase     =  self.create_estimator(snake_case_				)

													# run training
													estimator.fit()

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

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


"""simple docstring"""
from typing import List

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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

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



							'''simple docstring'''





							_snake_case						      =		'''efficientformer'''




							def __init__( self    ,				snake_case_ = [3, 2, 6, 4]    ,				snake_case_ = [48, 96, 224, 448]    ,				snake_case_ = [True, True, True, True]    ,				snake_case_ = 448    ,				snake_case_ = 32    ,				snake_case_ = 4    ,				snake_case_ = 7    ,				snake_case_ = 5    ,				snake_case_ = 8    ,				snake_case_ = 4    ,				snake_case_ = 0.0    ,				snake_case_ = 16    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 2    ,				snake_case_ = 1    ,				snake_case_ = 0.0    ,				snake_case_ = 1    ,				snake_case_ = True    ,				snake_case_ = True    ,				snake_case_ = 1e-5    ,				snake_case_ = "gelu"    ,				snake_case_ = 0.02    ,				snake_case_ = 1e-1_2    ,				snake_case_ = 224    ,				snake_case_ = 1e-0_5    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)

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