





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   __SCREAMING_SNAKE_CASE     (					lowerCAmelCase_   ):
       _UpperCAmelCase :    List[str]					       =       VOCAB_FILES_NAMES
       _UpperCAmelCase :    str					       =       PRETRAINED_VOCAB_FILES_MAP
       _UpperCAmelCase :    Any					       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCAmelCase :    Optional[Any]					       =       PRETRAINED_INIT_CONFIGURATION
       _UpperCAmelCase :    Dict					       =       RoFormerTokenizer







       def __init__( self	:      Any						,   A	:      Optional[int]=None						,   A	:      List[str]=None						,   A	:      List[Any]=True						,   A	:      Optional[Any]="[UNK]"						,   A	:      Tuple="[SEP]"						,   A	:      str="[PAD]"						,   A	:      Union[str, Any]="[CLS]"						,   A	:      Any="[MASK]"						,   A	:      Dict=True						,   A	:      Dict=None						,   **A	:      Any						,   )  ->Union[str, Any]:
          super().__init__(
              A						,   tokenizer_file=A						,   do_lower_case=A						,   unk_token=A						,   sep_token=A						,   pad_token=A						,   cls_token=A						,   mask_token=A						,   tokenize_chinese_chars=A						,   strip_accents=A						,   **A						,   )

          lowerCamelCase__  :		Dict     =				json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
          if (
              pre_tok_state.get('''lowercase'''						,   A     ) != do_lower_case
              or pre_tok_state.get('''strip_accents'''						,   A     ) != strip_accents
          ):
             lowerCamelCase__  :		Any     =				getattr(A						,   pre_tok_state.pop('''type'''     )     )
             lowerCamelCase__  :		List[Any]     =				do_lower_case
             lowerCamelCase__  :		Tuple     =				strip_accents
             lowerCamelCase__  :		Optional[Any]     =				pre_tok_class(**A     )

          lowerCamelCase__  :		List[str]     =				do_lower_case







       def __getstate__( self	:      Tuple     )  ->List[Any]:
          lowerCamelCase__  :		int     =				self.__dict__.copy()
          lowerCamelCase__  :		Dict     =				BertPreTokenizer()
          return state







       def __setstate__( self	:      Optional[Any]						,   A	:      Dict     )  ->int:
          lowerCamelCase__  :		str     =				d
          lowerCamelCase__  :		Union[str, Any]     =				self.__dict__['''_tokenizer'''].get_vocab()
          lowerCamelCase__  :		List[Any]     =				PreTokenizer.custom(JiebaPreTokenizer(A     )     )







       def 		__lowerCamelCase       ( self	:      Any						,   A	:      Any						,   A	:      Optional[Any]=None     )  ->List[str]:
          lowerCamelCase__  :		List[str]     =				[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 		__lowerCamelCase       ( self	:      List[str]						,   A	:      List[int]						,   A	:      Optional[List[int]] = None     )  ->List[int]:
          lowerCamelCase__  :		Dict     =				[self.sep_token_id]
          lowerCamelCase__  :		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	:      str						,   A	:      str						,   A	:      Optional[str] = None     )  ->Tuple[str]:
          lowerCamelCase__  :		Optional[int]     =				self._tokenizer.model.save(A						,   name=A     )
          return tuple(A     )







       def 		__lowerCamelCase       ( self	:      List[str]						,   A	:      Optional[Any]						,   A	:      Optional[Any]=None						,   A	:      Tuple=None						,   A	:      Union[str, Any]=False						,   **A	:      int						,   )  ->str:
          lowerCamelCase__  :		Tuple     =				BertPreTokenizer()
          return super().save_pretrained(A						,   A						,   A						,   A						,   **A     )








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)
