



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


a__   :					Optional[Any] 						=      logging.get_logger(__name__)

a__   :					List[Any] 						=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a__   :					str 						=      {
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

a__   :					Optional[int] 						=      {
    "junnyu/roformer_chinese_small": 15_36,
    "junnyu/roformer_chinese_base": 15_36,
    "junnyu/roformer_chinese_char_small": 5_12,
    "junnyu/roformer_chinese_char_base": 5_12,
    "junnyu/roformer_small_discriminator": 1_28,
    "junnyu/roformer_small_generator": 1_28,
}


a__   :					Dict 						=      {
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	List[str]												=     VOCAB_FILES_NAMES
   A     :	str												=     PRETRAINED_VOCAB_FILES_MAP
   A     :	Any												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A     :	Optional[Any]												=     PRETRAINED_INIT_CONFIGURATION
   A     :	Dict												=     RoFormerTokenizer


   def __init__(    self	:      Any      ,					lowerCAmelCase	:      Optional[int]=None      ,					lowerCAmelCase	:      List[str]=None      ,					lowerCAmelCase	:      List[Any]=True      ,					lowerCAmelCase	:      Optional[Any]="[UNK]"      ,					lowerCAmelCase	:      Tuple="[SEP]"      ,					lowerCAmelCase	:      str="[PAD]"      ,					lowerCAmelCase	:      Union[str, Any]="[CLS]"      ,					lowerCAmelCase	:      Any="[MASK]"      ,					lowerCAmelCase	:      Dict=True      ,					lowerCAmelCase	:      Dict=None      ,					**lowerCAmelCase	:      Any      ,					)						->					Union[str, Any]:




         """simple docstring"""

         super().__init__(
             lowerCAmelCase      ,					tokenizer_file=lowerCAmelCase      ,					do_lower_case=lowerCAmelCase      ,					unk_token=lowerCAmelCase      ,					sep_token=lowerCAmelCase      ,					pad_token=lowerCAmelCase      ,					cls_token=lowerCAmelCase      ,					mask_token=lowerCAmelCase      ,					tokenize_chinese_chars=lowerCAmelCase      ,					strip_accents=lowerCAmelCase      ,					**lowerCAmelCase      ,					)

         lowercase__  =	json.loads(self.backend_tokenizer.normalizer.__getstate__())
         if (
             pre_tok_state.get('lowercase'      ,					lowerCAmelCase) != do_lower_case
             or pre_tok_state.get('strip_accents'      ,					lowerCAmelCase) != strip_accents
         ):
               lowercase__  =	getattr(lowerCAmelCase      ,					pre_tok_state.pop('type'))
               lowercase__  =	do_lower_case
               lowercase__  =	strip_accents
               lowercase__  =	pre_tok_class(**lowerCAmelCase)

         lowercase__  =	do_lower_case


   def __getstate__(    self	:      Tuple)						->					List[Any]:




         """simple docstring"""

         lowercase__  =	self.__dict__.copy()
         lowercase__  =	BertPreTokenizer()
         return state


   def __setstate__(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Dict)						->					int:




         """simple docstring"""

         lowercase__  =	d
         lowercase__  =	self.__dict__['_tokenizer'].get_vocab()
         lowercase__  =	PreTokenizer.custom(JiebaPreTokenizer(lowerCAmelCase))


   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Optional[Any]=None)						->					List[str]:




         """simple docstring"""

         lowercase__  =	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

         return output


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      List[int]      ,					lowerCAmelCase	:      Optional[List[int]] = None)						->					List[int]:




         """simple docstring"""

         lowercase__  =	[self.sep_token_id]
         lowercase__  =	[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       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Optional[str] = None)						->					Tuple[str]:




         """simple docstring"""

         lowercase__  =	self._tokenizer.model.save(lowerCAmelCase      ,					name=lowerCAmelCase)
         return tuple(lowerCAmelCase)



   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      Optional[Any]=None      ,					lowerCAmelCase	:      Tuple=None      ,					lowerCAmelCase	:      Union[str, Any]=False      ,					**lowerCAmelCase	:      int      ,					)						->					str:




         """simple docstring"""

         lowercase__  =	BertPreTokenizer()
         return super().save_pretrained(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					**lowerCAmelCase)





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 	_lowerCAmelCase (    ):
      lowercase__  =	argparse.ArgumentParser()
      parser.add_argument('-f'						)
      lowercase__  =	parser.parse_args()
      return args.f



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      List[str])						->					None:




         """simple docstring"""

         lowercase__  =	logging.StreamHandler(sys.stdout)
         logger.addHandler(lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Union[str, Any])						->					List[Any]:




         """simple docstring"""

         lowercase__  =	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):
                     lowercase__  =	run_glue_deebert.main()
                     for value in result.values():
                           self.assertGreaterEqual(lowerCAmelCase      ,					0.6_66)



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




         """simple docstring"""

         lowercase__  =	'\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(lowerCAmelCase)

         lowercase__  =	'\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(lowerCAmelCase)

         lowercase__  =	'\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(lowerCAmelCase)
