



"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async




def 			snake_case		(		lowerCAmelCase_=None	)						->				int:
  if subparsers is not None:
    _snake_case   	=      subparsers.add_parser('''test'''	)
  else:
    _snake_case   	=      argparse.ArgumentParser('''Accelerate test command'''	)

  parser.add_argument(
      '''--config_file'''  ,						default=lowerCAmelCase_  ,						help=(
          '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
          '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
          '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
          '''with \'huggingface\'.'''
      )  ,						)

  if subparsers is not None:
    parser.set_defaults(func=lowerCAmelCase_	)
  return parser




def 			snake_case		(		lowerCAmelCase_	)						->				int:
  _snake_case   	=      os.path.sep.join(__file__.split(os.path.sep	)[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']	)

  if args.config_file is None:
    _snake_case   	=      script_name
  else:
    _snake_case   	=      f"""--config_file={args.config_file} {script_name}"""

  _snake_case   	=      ['''accelerate-launch'''] + test_args.split()
  _snake_case   	=      execute_subprocess_async(lowerCAmelCase_  ,						env=os.environ.copy()	)
  if result.returncode == 0:
    print('''Test is a success! You are ready for your distributed training!'''	)




def 			snake_case		(		)						->				List[Any]:
  _snake_case   	=      test_command_parser()
  _snake_case   	=      parser.parse_args()
  test_command(lowerCAmelCase_	)


if __name__ == "__main__":
     main()









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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_albert import AlbertTokenizer
else:
				_lowercase							:   List[str]						=						None

_lowercase							:   List[str]						=						logging.get_logger(__name__)
_lowercase							:   List[Any]						=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase							:   str						=						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

_lowercase							:   List[str]						=						{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowercase							:   Dict						=						"▁"






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			VOCAB_FILES_NAMES
				lowerCAmelCase					=			PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase					=			AlbertTokenizer

				def __init__(    self					, a__=None					, a__=None					, a__=True					, a__=True					, a__=False					, a__="[CLS]"					, a__="[SEP]"					, a__="<unk>"					, a__="[SEP]"					, a__="<pad>"					, a__="[CLS]"					, a__="[MASK]"					, **a__					, )						->					Dict:
					# Mask token behave like a normal word, i.e. include the space before it and
					# is included in the raw text, there should be a match in a non-normalized sentence.
					A             =					(
					    AddedToken(a__					, lstrip=a__					, rstrip=a__					, normalized=a__ )
					    if isinstance(a__					, a__ )
					    else mask_token
					)

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

					A             =					do_lower_case
					A             =					remove_space
					A             =					keep_accents
					A             =					vocab_file
					A             =					False if not self.vocab_file else True

				def   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[int]:
					A             =					[self.sep_token_id]
					A             =					[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   _UpperCAmelCase       (    self					, a__					, a__ = None )						->					List[int]:
					A             =					[self.sep_token_id]
					A             =					[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					, a__					, a__ = None )						->					Tuple[str]:
					if not self.can_save_slow_tokenizer:
						raise ValueError(
						    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
						    """tokenizer.""" )

					if not os.path.isdir(a__ ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
						return
					A             =					os.path.join(
					    a__					, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

					return (out_vocab_file,)



