





'''simple docstring'''


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		):




 def 				_snake_case (						self		)     ->				List[str]:
     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 				_snake_case (						self      ,  _lowerCAmelCase=1		)     ->				Tuple:
     # 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 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[int]:
     TrainingJobAnalytics(_lowerCAmelCase		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)





 def 				_snake_case (						self		)     ->				Optional[int]:
     # create estimator
     _lowerCAmelCase						      =   self.create_estimator()

     # 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"      ,  999999		)
     )

     # 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}


class 	lowerCAmelCase  (   A ,      A ):
       lowerCAmelCase_												=			"dinat"

       lowerCAmelCase_												=			{
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self      :			int    ,   __lowercase      :			str=4    ,   __lowercase      :			str=3    ,   __lowercase      :			int=64    ,   __lowercase      :			int=[3, 4, 6, 5]    ,   __lowercase      :			Union[str, Any]=[2, 4, 8, 16]    ,   __lowercase      :			Optional[int]=7    ,   __lowercase      :			str=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]    ,   __lowercase      :			Any=3.0    ,   __lowercase      :			Optional[Any]=True    ,   __lowercase      :			List[Any]=0.0    ,   __lowercase      :			Optional[Any]=0.0    ,   __lowercase      :			int=0.1    ,   __lowercase      :			Union[str, Any]="gelu"    ,   __lowercase      :			Any=0.0_2    ,   __lowercase      :			int=1E-5    ,   __lowercase      :			Optional[int]=0.0    ,   __lowercase      :			Optional[int]=None    ,   __lowercase      :			str=None    ,   **__lowercase      :			Any    ,   ):







            """simple docstring"""







            super().__init__(**__lowercase   )

            __lowercase      		=patch_size
            __lowercase      		=num_channels
            __lowercase      		=embed_dim
            __lowercase      		=depths
            __lowercase      		=len(__lowercase   )
            __lowercase      		=num_heads
            __lowercase      		=kernel_size
            __lowercase      		=dilations
            __lowercase      		=mlp_ratio
            __lowercase      		=qkv_bias
            __lowercase      		=hidden_dropout_prob
            __lowercase      		=attention_probs_dropout_prob
            __lowercase      		=drop_path_rate
            __lowercase      		=hidden_act
            __lowercase      		=layer_norm_eps
            __lowercase      		=initializer_range
            # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
            # this indicates the channel dimension after the last stage of the model
            __lowercase      		=int(embed_dim * 2 ** (len(__lowercase   ) - 1)   )
            __lowercase      		=layer_scale_init_value
            __lowercase      		=['stem'] + [f'''stage{idx}''' for idx in range(1    ,   len(__lowercase   ) + 1   )]
            __lowercase		,  __lowercase      		=get_aligned_output_features_output_indices(
                out_features=__lowercase    ,   out_indices=__lowercase    ,   stage_names=self.stage_names   )


