'''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)

UpperCamelCase__     :	List[str]				=			logging.getLogger()







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				str   				=  argparse.ArgumentParser()
   parser.add_argument('''-f'''    )
   UpperCAmelCase__							:				Dict   				=  parser.parse_args()
   return args.f


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  logging.StreamHandler(sys.stdout							)
         logger.addHandler(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				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(lowerCamelCase_					,'''argv'''					,lowerCamelCase_							):
               UpperCAmelCase__							:				str   				=  run_glue_deebert.main()
               for value in result.values():
                  self.assertGreaterEqual(lowerCamelCase_					,0.666							)




      @slow
      @require_torch_non_multi_gpu
      def 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



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

         UpperCAmelCase__							:				str   				=  '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(lowerCamelCase_							)

         UpperCAmelCase__							:				int   				=  '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(lowerCamelCase_							)





'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__     :	Union[str, Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	str				=			{'vocab_file': 'sentencepiece.model'}

UpperCamelCase__     :	int				=			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCamelCase__     :	Any				=			{
    'google/rembert': 256,
}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      List[Any]      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      List[Any]      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      Tuple      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_=False					,lowerCamelCase_=True					,lowerCamelCase_=True					,lowerCamelCase_="[CLS]"					,lowerCamelCase_="[SEP]"					,lowerCamelCase_="[UNK]"					,lowerCamelCase_="[SEP]"					,lowerCamelCase_="[PAD]"					,lowerCamelCase_="[CLS]"					,lowerCamelCase_="[MASK]"					,**lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



         super().__init__(
             do_lower_case=lowerCamelCase_					,remove_space=lowerCamelCase_					,keep_accents=lowerCamelCase_					,bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,unk_token=lowerCamelCase_					,sep_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,cls_token=lowerCamelCase_					,mask_token=lowerCamelCase_					,**lowerCamelCase_					,)

         UpperCAmelCase__							:				Union[str, Any]   				=  do_lower_case
         UpperCAmelCase__							:				str   				=  remove_space
         UpperCAmelCase__							:				Optional[Any]   				=  keep_accents
         UpperCAmelCase__							:				List[Any]   				=  vocab_file

         UpperCAmelCase__							:				Dict   				=  spm.SentencePieceProcessor()
         self.sp_model.Load(lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         return len(self.sp_model							)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  {self.convert_ids_to_tokens(lowerCamelCase_							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab




      def __getstate__(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  self.__dict__.copy()
         UpperCAmelCase__							:				Any   				=  None
         return state




      def __setstate__(				self					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  d
         UpperCAmelCase__							:				Any   				=  spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=False							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.sp_model.EncodeAsPieces(lowerCamelCase_							)
         return pieces




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.sp_model.PieceToId(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         return self.sp_model.IdToPiece(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.sp_model.decode_pieces(lowerCamelCase_							)
         return out_string




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  [self.sep_token_id]
         UpperCAmelCase__							:				List[Any]   				=  [self.cls_token_id]
         if token_ids_a is None:
            return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = False							)			->			List[int]:



         '''simple docstring'''



         if already_has_special_tokens:
            if token_ids_a is not None:
               raise ValueError(
                   '''You should not supply a second sequence if the provided sequence of '''
                   '''ids is already formatted with special tokens for the model.'''							)
            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
            return [1] + ([0] * len(lowerCamelCase_							)) + [1] + ([0] * len(lowerCamelCase_							)) + [1]
         return [1] + ([0] * len(lowerCamelCase_							)) + [1]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  [self.sep_token_id]
         UpperCAmelCase__							:				List[Any]   				=  [self.cls_token_id]

         if token_ids_a is None:
            return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(lowerCamelCase_							):
            logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_							)							)
            return
         UpperCAmelCase__							:				Union[str, Any]   				=  os.path.join(
             lowerCamelCase_					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCamelCase_							):
            copyfile(self.vocab_file					,lowerCamelCase_							)

         return (out_vocab_file,)




