


'''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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]		)
class 		lowerCAmelCase__	(	unittest.TestCase		):




			def 							lowerCAmelCase__			(						self     :   Any       )      ->int:



					'''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 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   Optional[int]       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
					# distributed data settings
					_UpperCAmelCase						:				Union[str, Any]	    =					{"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

					# 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=lowerCamelCase__		,     instance_count=lowerCamelCase__		,     instance_type=self.instance_type		,     debugger_hook_config=lowerCamelCase__		,     hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}		,     metric_definitions=self.env.metric_definitions		,     distribution=lowerCamelCase__		,     py_version="py36"		,     )




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Optional[int]       )      ->Any:



					'''simple docstring'''







					TrainingJobAnalytics(lowerCamelCase__       ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""       )






			@parameterized.expand([(2,)]       )
			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   List[Any]       )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					self.create_estimator(lowerCamelCase__       )

					# run training
					estimator.fit()

					# result dataframe
					_UpperCAmelCase						:				Optional[Any]	    =					TrainingJobAnalytics(estimator.latest_training_job.name       ).dataframe()

					# extract kpis
					_UpperCAmelCase						:				Tuple	    =					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]       )
					_UpperCAmelCase						:				Tuple	    =					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]       )
					# get train time from SageMaker job, this includes starting, preprocessing, stopping
					_UpperCAmelCase						:				Optional[int]	    =					(
					    Session().describe_training_job(estimator.latest_training_job.name       ).get("TrainingTimeInSeconds"		,     99_99_99       )
					)

					# 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'''


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__					=   logging.get_logger(__name__)

lowerCamelCase__					=   {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Any				      =   "deta"
			lowerCAmelCase       :		Union[str, Any]				      =   {
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}




			def __init__(						self     :   Optional[Any]		,     lowerCamelCase__     :   str=None		,     lowerCamelCase__     :   str=9_00		,     lowerCamelCase__     :   Any=20_48		,     lowerCamelCase__     :   Optional[int]=6		,     lowerCamelCase__     :   str=20_48		,     lowerCamelCase__     :   Dict=8		,     lowerCamelCase__     :   Any=6		,     lowerCamelCase__     :   Union[str, Any]=10_24		,     lowerCamelCase__     :   Optional[int]=8		,     lowerCamelCase__     :   List[Any]=0.0		,     lowerCamelCase__     :   Tuple=True		,     lowerCamelCase__     :   int="relu"		,     lowerCamelCase__     :   str=2_56		,     lowerCamelCase__     :   Dict=0.1		,     lowerCamelCase__     :   Dict=0.0		,     lowerCamelCase__     :   Dict=0.0		,     lowerCamelCase__     :   Optional[int]=0.0_2		,     lowerCamelCase__     :   List[Any]=1.0		,     lowerCamelCase__     :   Optional[int]=True		,     lowerCamelCase__     :   Any=False		,     lowerCamelCase__     :   Any="sine"		,     lowerCamelCase__     :   str=5		,     lowerCamelCase__     :   Optional[int]=4		,     lowerCamelCase__     :   int=4		,     lowerCamelCase__     :   Optional[Any]=True		,     lowerCamelCase__     :   Dict=3_00		,     lowerCamelCase__     :   int=True		,     lowerCamelCase__     :   Dict=True		,     lowerCamelCase__     :   Union[str, Any]=1		,     lowerCamelCase__     :   Any=5		,     lowerCamelCase__     :   List[Any]=2		,     lowerCamelCase__     :   Union[str, Any]=1		,     lowerCamelCase__     :   str=1		,     lowerCamelCase__     :   Union[str, Any]=5		,     lowerCamelCase__     :   str=2		,     lowerCamelCase__     :   Optional[Any]=0.1		,     lowerCamelCase__     :   Union[str, Any]=0.2_5		,     **lowerCamelCase__     :   Optional[Any]		,     )      ->List[str]:



					'''simple docstring'''







					if backbone_config is None:
							logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."       )
							_UpperCAmelCase						:				int	    =					CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]       )
					else:
							if isinstance(lowerCamelCase__		,     lowerCamelCase__       ):
									_UpperCAmelCase						:				Any	    =					backbone_config.pop("model_type"       )
									_UpperCAmelCase						:				Optional[int]	    =					CONFIG_MAPPING[backbone_model_type]
									_UpperCAmelCase						:				List[str]	    =					config_class.from_dict(lowerCamelCase__       )

					_UpperCAmelCase						:				Optional[Any]	    =					backbone_config
					_UpperCAmelCase						:				Optional[int]	    =					num_queries
					_UpperCAmelCase						:				Union[str, Any]	    =					max_position_embeddings
					_UpperCAmelCase						:				Union[str, Any]	    =					d_model
					_UpperCAmelCase						:				str	    =					encoder_ffn_dim
					_UpperCAmelCase						:				Optional[int]	    =					encoder_layers
					_UpperCAmelCase						:				int	    =					encoder_attention_heads
					_UpperCAmelCase						:				Union[str, Any]	    =					decoder_ffn_dim
					_UpperCAmelCase						:				Tuple	    =					decoder_layers
					_UpperCAmelCase						:				Union[str, Any]	    =					decoder_attention_heads
					_UpperCAmelCase						:				Any	    =					dropout
					_UpperCAmelCase						:				List[str]	    =					attention_dropout
					_UpperCAmelCase						:				Union[str, Any]	    =					activation_dropout
					_UpperCAmelCase						:				Optional[int]	    =					activation_function
					_UpperCAmelCase						:				str	    =					init_std
					_UpperCAmelCase						:				Tuple	    =					init_xavier_std
					_UpperCAmelCase						:				Optional[Any]	    =					encoder_layerdrop
					_UpperCAmelCase						:				int	    =					auxiliary_loss
					_UpperCAmelCase						:				Union[str, Any]	    =					position_embedding_type
					# deformable attributes
					_UpperCAmelCase						:				List[Any]	    =					num_feature_levels
					_UpperCAmelCase						:				List[Any]	    =					encoder_n_points
					_UpperCAmelCase						:				Tuple	    =					decoder_n_points
					_UpperCAmelCase						:				Optional[int]	    =					two_stage
					_UpperCAmelCase						:				Dict	    =					two_stage_num_proposals
					_UpperCAmelCase						:				int	    =					with_box_refine
					_UpperCAmelCase						:				str	    =					assign_first_stage
					if two_stage is True and with_box_refine is False:
							raise ValueError("If two_stage is True, with_box_refine must be True."       )
					# Hungarian matcher
					_UpperCAmelCase						:				Optional[int]	    =					class_cost
					_UpperCAmelCase						:				Dict	    =					bbox_cost
					_UpperCAmelCase						:				int	    =					giou_cost
					# Loss coefficients
					_UpperCAmelCase						:				int	    =					mask_loss_coefficient
					_UpperCAmelCase						:				List[Any]	    =					dice_loss_coefficient
					_UpperCAmelCase						:				Dict	    =					bbox_loss_coefficient
					_UpperCAmelCase						:				int	    =					giou_loss_coefficient
					_UpperCAmelCase						:				Optional[Any]	    =					eos_coefficient
					_UpperCAmelCase						:				int	    =					focal_alpha
					super().__init__(is_encoder_decoder=lowerCamelCase__		,     **lowerCamelCase__       )




			@property
			def 							lowerCAmelCase__			(						self     :   int       )      ->int:



					'''simple docstring'''







					return self.encoder_attention_heads




			@property
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->int:



					'''simple docstring'''







					return self.d_model






			def 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					copy.deepcopy(self.__dict__       )
					_UpperCAmelCase						:				Tuple	    =					self.backbone_config.to_dict()
					_UpperCAmelCase						:				List[Any]	    =					self.__class__.model_type
					return output



