


"""simple docstring"""







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)

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




def   UpperCAmelCase__			():




					'''simple docstring'''


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




class 					UpperCamelCase_      (					_a):

	"""simple docstring"""





	def     UpperCAmelCase_ (					self     :							List[str]  )			->      List[str]:
						__SCREAMING_SNAKE_CASE		  =							logging.StreamHandler(sys.stdout  )
						logger.addHandler(A_  )




	def     UpperCAmelCase_ (					self     :							Tuple     ,     UpperCAmelCase__     :							int  )			->      Tuple:
						__SCREAMING_SNAKE_CASE		  =							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(A_     ,     "argv"     ,     A_  ):
																__SCREAMING_SNAKE_CASE		  =							run_glue_deebert.main()
																for value in result.values():
																					self.assertGreaterEqual(A_     ,     0.666  )




	@slow
	@require_torch_non_multi_gpu
	def     UpperCAmelCase_ (					self     :							str  )			->      Optional[Any]:
						__SCREAMING_SNAKE_CASE		  =							"\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(A_  )

						__SCREAMING_SNAKE_CASE		  =							"\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(A_  )

						__SCREAMING_SNAKE_CASE		  =							"\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(A_  )




"""simple docstring"""

from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			str					, UpperCamelCase__						:			Optional[str] = None			):






 """simple docstring"""







 if version.parse(hfh.__version__			).release < version.parse("""0.11.0"""			).release:
  # old versions of hfh don't url-encode the file path
  __lowercase						=						quote(UpperCamelCase__			)
 return hfh.hf_hub_url(UpperCamelCase__					, UpperCamelCase__					, repo_type="""dataset"""					, revision=UpperCamelCase__			)
