





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

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

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






def       _A  (      ):
   a__	:						int      		=  argparse.ArgumentParser()
   parser.add_argument("-f"   )
   a__	:						Dict      		=  parser.parse_args()
   return args.f







class   __lowerCAmelCase			(					_UpperCamelCase  ):





   def 	_snake_case ( self      )				->  None:
      """simple docstring"""





      a__	:						Optional[int]      		=  logging.StreamHandler(sys.stdout      )
      logger.addHandler(snake_case      )





   def 	_snake_case ( self					,			snake_case      )				->  Any:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  get_gpu_count()

      if n_gpu > 1:
         pass
         # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
         # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
         # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
         # cmd = [sys.executable] + distributed_args + args
         # execute_subprocess_async(cmd, env=self.get_env())
         # XXX: test the results - need to save them first into .json file
      else:
         args.insert(0					,			"run_glue_deebert.py"      )
         with patch.object(snake_case					,			"argv"					,			snake_case      ):
            a__	:						List[Any]      		=  run_glue_deebert.main()
            for value in result.values():
               self.assertGreaterEqual(snake_case					,			0.666      )





   @slow
   @require_torch_non_multi_gpu
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						Any      		=  "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
      self.run_and_check(snake_case      )

      a__	:						Dict      		=  "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
      self.run_and_check(snake_case      )

      a__	:						Dict      		=  "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
      self.run_and_check(snake_case      )








import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  SCREAMING_SNAKE_CASE__       :			int			      =						{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  SCREAMING_SNAKE_CASE__       :			Dict			      =						{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }






def       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  (images / 2 + 0.5).clamp(0							, 1   )
   a__	:						List[Any]      		=  images.cpu().permute(0							, 2							, 3							, 1   ).float().numpy()
   a__	:						int      		=  numpy_to_pil(lowerCamelCase   )
   return images






def       _A  (      lowerCamelCase   ):
   if images.ndim == 3:
      a__	:						Tuple      		=  images[None, ...]
   a__	:						Dict      		=  (images * 255).round().astype("uint8"   )
   if images.shape[-1] == 1:
      # special case for grayscale (single channel) images
      a__	:						str      		=  [Image.fromarray(image.squeeze()							, mode="L"   ) for image in images]
   else:
      a__	:						List[Any]      		=  [Image.fromarray(lowerCamelCase   ) for image in images]

   return pil_images

