





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)

__lowerCamelCase			:	List[Any]					    =	logging.getLogger()



def 						A_							(	)		->					int:
 UpperCamelCase      :   List[Any]			      =  argparse.ArgumentParser()
 parser.add_argument("-f"	)
 UpperCamelCase      :   Tuple			      =  parser.parse_args()
 return args.f



class    A__  (			__snake_case     ):





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  logging.StreamHandler(sys.stdout  )
       logger.addHandler(A_  )





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  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_  ):
         UpperCamelCase      :   str			      =  run_glue_deebert.main()
         for value in result.values():
          self.assertGreaterEqual(A_			,				0.6_66  )





      @slow
      @require_torch_non_multi_gpu
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   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(A_  )

       UpperCamelCase      :   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(A_  )

       UpperCamelCase      :   Tuple			      =  "\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_  )







import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class    A__  :
      _UpperCAmelCase      :Union[str, Any]		       =  None





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  self.feature_extraction_class(**self.feat_extract_dict  )
       UpperCamelCase      :   Union[str, Any]			      =  json.loads(feat_extract.to_json_string()  )
       for key, value in self.feat_extract_dict.items():
        self.assertEqual(obj[key]			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   List[Any]			      =  os.path.join(A_			,				"feat_extract.json"  )
        feat_extract_first.to_json_file(A_  )
        UpperCamelCase      :   str			      =  self.feature_extraction_class.from_json_file(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   int			      =  feat_extract_first.save_pretrained(A_  )[0]
        check_json_file_has_correct_format(A_  )
        UpperCamelCase      :   Optional[Any]			      =  self.feature_extraction_class.from_pretrained(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  self.feature_extraction_class()
       self.assertIsNotNone(A_  )
