

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


UpperCamelCase__		 =     logging.get_logger(__name__)

UpperCamelCase__		 =     '''▁'''

UpperCamelCase__		 =     {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

UpperCamelCase__		 =     {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

UpperCamelCase__		 =     {
    '''facebook/s2t-small-librispeech-asr''': 1024,
}

UpperCamelCase__		 =     ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

UpperCamelCase__		 =     {'''mustc''': MUSTC_LANGS}

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE				    =							MAX_MODEL_INPUT_SIZES
       __SCREAMING_SNAKE_CASE				    =							["input_ids", "attention_mask"]

       __SCREAMING_SNAKE_CASE				    =							[]


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase="<s>"      , _UpperCamelCase="</s>"      , _UpperCamelCase="<pad>"      , _UpperCamelCase="<unk>"      , _UpperCamelCase=False      , _UpperCamelCase=False      , _UpperCamelCase=None      , _UpperCamelCase=None      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							None:






              """simple docstring"""


              __snake_case       	=							{} if sp_model_kwargs is None else sp_model_kwargs

              super().__init__(
                  bos_token=_UpperCamelCase      , eos_token=_UpperCamelCase      , unk_token=_UpperCamelCase      , pad_token=_UpperCamelCase      , do_upper_case=_UpperCamelCase      , do_lower_case=_UpperCamelCase      , tgt_lang=_UpperCamelCase      , lang_codes=_UpperCamelCase      , sp_model_kwargs=self.sp_model_kwargs      , **_UpperCamelCase      , )
              __snake_case       	=							do_upper_case
              __snake_case       	=							do_lower_case

              __snake_case       	=							load_json(_UpperCamelCase   )
              __snake_case       	=							{v: k for k, v in self.encoder.items()}
              __snake_case       	=							spm_file
              __snake_case       	=							load_spm(_UpperCamelCase      , self.sp_model_kwargs   )

              if lang_codes is not None:
                     __snake_case       	=							lang_codes
                     __snake_case       	=							LANGUAGES[lang_codes]
                     __snake_case       	=							[F'<lang:{lang}>' for lang in self.langs]
                     __snake_case       	=							{lang: self.sp_model.PieceToId(F'<lang:{lang}>'   ) for lang in self.langs}

                     __snake_case       	=							self.lang_tokens
                     __snake_case       	=							tgt_lang if tgt_lang is not None else self.langs[0]

                     self.set_tgt_lang_special_tokens(self._tgt_lang   )
              else:
                     __snake_case       	=							{}


       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              return len(self.encoder   )


       @property
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              return self._tgt_lang


       @tgt_lang.setter
       def 			a		(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							new_tgt_lang
              self.set_tgt_lang_special_tokens(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							self.lang_code_to_id[tgt_lang]
              __snake_case       	=							[lang_code_id]


       def 			a		(		self      , _UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              return self.encoder.get(_UpperCamelCase      , self.encoder[self.unk_token]   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              return self.decoder.get(_UpperCamelCase      , self.unk_token   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase=None   )      ->							List[int]:






              """simple docstring"""


              if token_ids_a is None:
                     return self.prefix_tokens + token_ids_a + [self.eos_token_id]
              # We don't expect to process pairs, but leave the pair logic for API consistency
              return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = False   )      ->							List[int]:






              """simple docstring"""


              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=_UpperCamelCase      , token_ids_a=_UpperCamelCase      , already_has_special_tokens=_UpperCamelCase   )

              __snake_case       	=							[1] * len(self.prefix_tokens   )
              __snake_case       	=							[1]
              if token_ids_a is None:
                     return prefix_ones + ([0] * len(_UpperCamelCase   )) + suffix_ones
              return prefix_ones + ([0] * len(_UpperCamelCase   )) + ([0] * len(_UpperCamelCase   )) + suffix_ones


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.encoder.copy()
              vocab.update(self.added_tokens_encoder   )
              return vocab


       def __getstate__(		self   )      ->							Dict:






              """simple docstring"""


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


       def __setstate__(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							d

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

              __snake_case       	=							load_spm(self.spm_file      , self.sp_model_kwargs   )






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							Tuple[str]:






              """simple docstring"""


              __snake_case       	=							Path(_UpperCamelCase   )
              assert save_dir.is_dir(), F'{save_directory} should be a directory'
              __snake_case       	=							save_dir / (
                  (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
              )
              __snake_case       	=							save_dir / (
                  (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
              )

              save_json(self.encoder      , _UpperCamelCase   )

              if os.path.abspath(self.spm_file   ) != os.path.abspath(_UpperCamelCase   ) and os.path.isfile(self.spm_file   ):
                     copyfile(self.spm_file      , _UpperCamelCase   )
              elif not os.path.isfile(self.spm_file   ):
                     with open(_UpperCamelCase      , """wb"""   ) as fi:
                            __snake_case       	=							self.sp_model.serialized_model_proto()
                            fi.write(_UpperCamelCase   )

              return (str(_UpperCamelCase   ), str(_UpperCamelCase   ))







def 						lowerCamelCase__      ( __A    :str	,__A    :Dict[str, Any]		):


       """simple docstring"""




       __snake_case       	=							sentencepiece.SentencePieceProcessor(**__A		)
       spm.Load(str(__A		)		)
       return spm







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       with open(__A	,"""r"""		) as f:
              return json.load(__A		)







def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :str		):


       """simple docstring"""




       with open(__A	,"""w"""		) as f:
              json.dump(__A	,__A	,indent=2		)




# 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


UpperCamelCase__		 =     '''Create a default config file for Accelerate with only a few flags set.'''







def 						lowerCamelCase__      ( __A    :List[Any]="no"	,__A    :str = default_json_config_file	,__A    :bool = False		):


       """simple docstring"""




       __snake_case       	=							Path(__A		)
       path.parent.mkdir(parents=__A	,exist_ok=__A		)
       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
       __snake_case       	=							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}'		)
       __snake_case       	=							{
           """compute_environment""": """LOCAL_MACHINE""",
           """mixed_precision""": mixed_precision,
       }
       if torch.cuda.is_available():
              __snake_case       	=							torch.cuda.device_count()
              __snake_case       	=							num_gpus
              __snake_case       	=							False
              if num_gpus > 1:
                     __snake_case       	=							"""MULTI_GPU"""
              else:
                     __snake_case       	=							"""NO"""
       elif is_xpu_available() and use_xpu:
              __snake_case       	=							torch.xpu.device_count()
              __snake_case       	=							num_xpus
              __snake_case       	=							False
              if num_xpus > 1:
                     __snake_case       	=							"""MULTI_XPU"""
              else:
                     __snake_case       	=							"""NO"""
       elif is_npu_available():
              __snake_case       	=							torch.npu.device_count()
              __snake_case       	=							num_npus
              __snake_case       	=							False
              if num_npus > 1:
                     __snake_case       	=							"""MULTI_NPU"""
              else:
                     __snake_case       	=							"""NO"""
       else:
              __snake_case       	=							0
              __snake_case       	=							True
              __snake_case       	=							1
              __snake_case       	=							"""NO"""
       __snake_case       	=							ClusterConfig(**__A		)
       config.to_json_file(__A		)
       return path







def 						lowerCamelCase__      ( __A    :Dict	,__A    :List[Any]		):


       """simple docstring"""




       __snake_case       	=							parser.add_parser("""default"""	,parents=__A	,help=__A	,formatter_class=__A		)
       parser.add_argument(
           """--config_file"""	,default=__A	,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=__A	,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=__A		)
       return parser







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




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

