


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

import sentencepiece as spm

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


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{'vocab_file': 'spiece.model'}

lowerCAmelCase	=							{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

lowerCAmelCase	=							{'bert_for_seq_generation': 512}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[str]  				=			VOCAB_FILES_NAMES
							_lowercase     :      Dict  				=			PRETRAINED_VOCAB_FILES_MAP
							_lowercase     :      List[str]  				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowercase     :      List[int]  				=			[]
							_lowercase     :      List[Any]  				=			['''input_ids''', '''attention_mask''']

							def __init__(							self:     List[Any]   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     int="<s>"   ,						UpperCamelCase_:     Dict="</s>"   ,						UpperCamelCase_:     Dict="<unk>"   ,						UpperCamelCase_:     Optional[Any]="<pad>"   ,						UpperCamelCase_:     str="<::::>"   ,						UpperCamelCase_:     Optional[Dict[str, Any]] = None   ,						**UpperCamelCase_:     Optional[Any]   ,						)      ->       None:





									"""simple docstring"""







									lowercase__						=				{} if sp_model_kwargs is None else sp_model_kwargs

									# Add extra_ids to the special token list
									super().__init__(
									    bos_token=UpperCamelCase_   ,						eos_token=UpperCamelCase_   ,						unk_token=UpperCamelCase_   ,						pad_token=UpperCamelCase_   ,						sep_token=UpperCamelCase_   ,						sp_model_kwargs=self.sp_model_kwargs   ,						**UpperCamelCase_   ,						)

									lowercase__						=				vocab_file

									lowercase__						=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(UpperCamelCase_							)

							@property
							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Union[str, Any]:





									"""simple docstring"""







									return self.sp_model.get_piece_size()

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





									"""simple docstring"""







									lowercase__						=				{self.convert_ids_to_tokens(UpperCamelCase_							): i for i in range(self.vocab_size							)}
									vocab.update(self.added_tokens_encoder							)
									return vocab

							def __getstate__(							self:     str							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				self.__dict__.copy()
									lowercase__						=				None
									return state

							def __setstate__(							self:     str   ,						UpperCamelCase_:     Union[str, Any]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				d

									# for backward compatibility
									if not hasattr(self   ,						'''sp_model_kwargs'''							):
											lowercase__						=				{}

									lowercase__						=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(self.vocab_file							)

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     str							)      ->       List[str]:





									"""simple docstring"""







									return self.sp_model.encode(UpperCamelCase_   ,						out_type=UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     Tuple							)      ->       List[str]:





									"""simple docstring"""







									return self.sp_model.piece_to_id(UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     Optional[int]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				self.sp_model.IdToPiece(UpperCamelCase_							)
									return token

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Any							)      ->       int:





									"""simple docstring"""







									lowercase__						=				[]
									lowercase__						=				''''''
									for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
													out_string += self.sp_model.decode(UpperCamelCase_							) + token
													lowercase__						=				[]
											else:
													current_sub_tokens.append(UpperCamelCase_							)
									out_string += self.sp_model.decode(UpperCamelCase_							)
									return out_string.strip()


							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Optional[str] = None							)      ->       Tuple[str]:





									"""simple docstring"""







									if not os.path.isdir(UpperCamelCase_							):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'							)
											return
									lowercase__						=				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_							) and os.path.isfile(self.vocab_file							):
											copyfile(self.vocab_file   ,						UpperCamelCase_							)
									elif not os.path.isfile(self.vocab_file							):
											with open(UpperCamelCase_   ,						'''wb'''							) as fi:
													lowercase__						=				self.sp_model.serialized_model_proto()
													fi.write(UpperCamelCase_							)

									return (out_vocab_file,)






# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCAmelCase	=							'Create a default config file for Accelerate with only a few flags set.'
def       _a			(				SCREAMING_SNAKE_CASE="no"						,    SCREAMING_SNAKE_CASE = default_json_config_file						,    SCREAMING_SNAKE_CASE = False					):


		"""simple docstring"""




		lowercase__						=				Path(SCREAMING_SNAKE_CASE					)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE						,    exist_ok=SCREAMING_SNAKE_CASE					)
		if path.exists():
				print(
				    f'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'					)
				return False
		lowercase__						=				mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    f'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'					)
		lowercase__						=				{
		    '''compute_environment''': '''LOCAL_MACHINE''',
		    '''mixed_precision''': mixed_precision,
		}
		if torch.cuda.is_available():
				lowercase__						=				torch.cuda.device_count()
				lowercase__						=				num_gpus
				lowercase__						=				False
				if num_gpus > 1:
						lowercase__						=				'''MULTI_GPU'''
				else:
						lowercase__						=				'''NO'''
		elif is_xpu_available() and use_xpu:
				lowercase__						=				torch.xpu.device_count()
				lowercase__						=				num_xpus
				lowercase__						=				False
				if num_xpus > 1:
						lowercase__						=				'''MULTI_XPU'''
				else:
						lowercase__						=				'''NO'''
		elif is_npu_available():
				lowercase__						=				torch.npu.device_count()
				lowercase__						=				num_npus
				lowercase__						=				False
				if num_npus > 1:
						lowercase__						=				'''MULTI_NPU'''
				else:
						lowercase__						=				'''NO'''
		else:
				lowercase__						=				0
				lowercase__						=				True
				lowercase__						=				1
				lowercase__						=				'''NO'''
		lowercase__						=				ClusterConfig(**SCREAMING_SNAKE_CASE					)
		config.to_json_file(SCREAMING_SNAKE_CASE					)
		return path
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				parser.add_parser('''default'''						,    parents=SCREAMING_SNAKE_CASE						,    help=SCREAMING_SNAKE_CASE						,    formatter_class=SCREAMING_SNAKE_CASE					)
		parser.add_argument(
		    '''--config_file'''						,    default=SCREAMING_SNAKE_CASE						,    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\'.'''
		    )						,    dest='''save_location'''						,    )

		parser.add_argument(
		    '''--mixed_precision'''						,    choices=['''no''', '''fp16''', '''bf16''']						,    type=SCREAMING_SNAKE_CASE						,    help='''Whether or not to use mixed precision training. '''
		    '''Choose between FP16 and BF16 (bfloat16) training. '''
		    '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''						,    default='''no'''						,    )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE					)
		return parser
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				write_basic_config(args.mixed_precision						,    args.save_location					)
		if config_file:
				print(f'accelerate configuration saved at {config_file}'					)


