



'''simple docstring'''





import os

import pytest
from attr import dataclass


__a          =		"us-east-1"  # defaults region







@dataclass
class     UpperCAmelCase_       :
    """simple docstring"""







    lowercase					=		42
    lowercase					=		"arn:aws:iam::558105141721:role/sagemaker_execution_role"
    lowercase					=		{
        "task_name": "mnli",
        "per_device_train_batch_size": 16,
        "per_device_eval_batch_size": 16,
        "do_train": True,
        "do_eval": True,
        "do_predict": True,
        "output_dir": "/opt/ml/model",
        "overwrite_output_dir": True,
        "max_steps": 5_00,
        "save_steps": 55_00,
    }
    lowercase					=		{**hyperparameters, "max_steps": 10_00}




    @property
    def lowerCamelCase       (    self					:     List[str]						):
          if self.framework == "pytorch":
                return [
                    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
                ]
          else:
                return [
                    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
                ]




    @property
    def lowerCamelCase       (    self					:     Union[str, Any]						):
          return f"{self.framework}-transfromers-test"




    @property
    def lowerCamelCase       (    self					:     List[str]						):
          return f"./tests/sagemaker/scripts/{self.framework}"




    @property
    def lowerCamelCase       (    self					:     List[str]						):
          if self.framework == "pytorch":
                return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
          else:
                return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"





@pytest.fixture(scope="""class"""		)
def 						__snake_case(				_lowerCAmelCase		)		-> Union[str, Any]:
      snake_case__  :    Dict    						=      SageMakerTestEnvironment(framework=request.cls.framework		)





'''simple docstring'''





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__a          =		logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		field(default=_a		,		metadata={"help": "Whether to use SortishSampler or not."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}	)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `max_length` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `num_beams` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        }		,		)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    int    						=      super().to_dict()
          for k, v in d.items():
                if isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    Optional[int]    						=      v.to_dict()
          return d
