

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:
					lowerCAmelCase_       	=	None

lowerCAmelCase_       	=	logging.get_logger(__name__)
lowerCAmelCase_       	=	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_       	=	{
    '''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''',
    },
}

lowerCAmelCase_       	=	{
    '''albert-base-v1''': 5_12,
    '''albert-large-v1''': 5_12,
    '''albert-xlarge-v1''': 5_12,
    '''albert-xxlarge-v1''': 5_12,
    '''albert-base-v2''': 5_12,
    '''albert-large-v2''': 5_12,
    '''albert-xlarge-v2''': 5_12,
    '''albert-xxlarge-v2''': 5_12,
}

lowerCAmelCase_       	=	'''▁'''





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE     :   Dict									=				PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE     :   List[Any]									=				AlbertTokenizer







			def __init__(     self :				Tuple							,   _UpperCamelCase :				Any=None							,   _UpperCamelCase :				str=None							,   _UpperCamelCase :				Tuple=True							,   _UpperCamelCase :				Optional[Any]=True							,   _UpperCamelCase :				Union[str, Any]=False							,   _UpperCamelCase :				str="[CLS]"							,   _UpperCamelCase :				Dict="[SEP]"							,   _UpperCamelCase :				Tuple="<unk>"							,   _UpperCamelCase :				str="[SEP]"							,   _UpperCamelCase :				Optional[int]="<pad>"							,   _UpperCamelCase :				Tuple="[CLS]"							,   _UpperCamelCase :				int="[MASK]"							,   **_UpperCamelCase :				Tuple							,   )      ->Tuple:
									# 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.
									snake_case_   =	(
									    AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							,   normalized=_UpperCamelCase							)
									    if isinstance(_UpperCamelCase							,   _UpperCamelCase							)
									    else mask_token
									)

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

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







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									snake_case_   =	[self.sep_token_id]
									snake_case_   =	[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 				snake_case__(     self :				str							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									snake_case_   =	[self.sep_token_id]
									snake_case_   =	[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 				snake_case__(     self :				str							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[str] = 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(_UpperCamelCase							):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
															return
									snake_case_   =	os.path.join(
									    _UpperCamelCase							,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

									return (out_vocab_file,)






'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import platform
import sys


a__							:							Tuple ='''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
    import torch

    print('''Torch version:''', torch.__version__)
    print('''Cuda available:''', torch.cuda.is_available())
    print('''Cuda version:''', torch.version.cuda)
    print('''CuDNN version:''', torch.backends.cudnn.version())
    print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
    print('''Torch version:''', None)

try:
    import transformers

    print('''transformers version:''', transformers.__version__)
except ImportError:
    print('''transformers version:''', None)
