



import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

A_		:  Optional[Any]										=  logging.getLogger()


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		argparse.ArgumentParser()
 parser.add_argument("-f"       )
 a							=		parser.parse_args()
 return args.f




class 		_lowercase		(							UpperCAmelCase__						):

   def 		A				(   self      :							str				)							->					Tuple:


    """simple docstring"""






    a							=		logging.StreamHandler(sys.stdout				)
    logger.addHandler(__lowerCAmelCase				)

    a							=		self.get_auto_remove_tmp_dir()
    a							=		f"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

    with patch.object(__lowerCAmelCase				, "argv"				, __lowerCAmelCase				):
     a							=		run_glue_with_pabee.main()
     for value in result.values():
      self.assertGreaterEqual(__lowerCAmelCase				, 0.7_5				)




from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

UpperCAmelCase__			:  int					     =     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def        __lowercase   (  cls      )  ->    Any:
        return cls






def        __lowercase   (  _A    ,     _A = None    ,     _A = None    ,     _A = None    ,     _A = None    ,     )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = device
      SCREAMING_SNAKE_CASE			:   Tuple	  = shape[0]

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = layout or torch.strided
      SCREAMING_SNAKE_CASE			:   Dict	  = device or torch.device("""cpu"""      )

      if generator is not None:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = generator.device.type if not isinstance(_A    ,     _A      ) else generator[0].device.type
            if gen_device_type != device.type and gen_device_type == "cpu":
                  SCREAMING_SNAKE_CASE			:   Tuple	  = """cpu"""
                  if device != "mps":
                        logger.info(
                            F"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
                            F" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
                            F" slighly speed up this function by passing a generator that was created on the {device} device."      )
            elif gen_device_type != device.type and gen_device_type == "cuda":
                  raise ValueError(F"Cannot generate a {device} tensor from a generator of type {gen_device_type}."      )

      if isinstance(_A    ,     _A      ):
            SCREAMING_SNAKE_CASE			:   int	  = (1,) + shape[1:]
            SCREAMING_SNAKE_CASE			:   Any	  = [
                torch.randn(_A    ,     generator=generator[i]    ,     device=_A    ,     dtype=_A    ,     layout=_A      )
                for i in range(_A      )
            ]
            SCREAMING_SNAKE_CASE			:   Any	  = torch.cat(_A    ,     dim=0      ).to(_A      )
      else:
            SCREAMING_SNAKE_CASE			:   int	  = torch.randn(_A    ,     generator=_A    ,     device=_A    ,     dtype=_A    ,     layout=_A      ).to(_A      )

      return latents







def        __lowercase   (  _A      )  ->    Dict:
      if is_torch_version("""<"""    ,     """2.0.0"""      ) or not hasattr(_A    ,     """_dynamo"""      ):
            return False
      return isinstance(_A    ,     torch._dynamo.eval_frame.OptimizedModule      )




