

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_lowerCamelCase    :       Union[str, Any]        =							logging.get_logger(__name__)


_lowerCamelCase    :       str        =							{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCamelCase    :       Optional[Any]        =							{
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

_lowerCamelCase    :       Any        =							{
    "facebook/bart-base": 1_0_2_4,
    "facebook/bart-large": 1_0_2_4,
    "facebook/bart-large-mnli": 1_0_2_4,
    "facebook/bart-large-cnn": 1_0_2_4,
    "facebook/bart-large-xsum": 1_0_2_4,
    "yjernite/bart_eli5": 1_0_2_4,
}





class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					VOCAB_FILES_NAMES
   UpperCamelCase       					=					PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase       					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase       					=					["""input_ids""", """attention_mask"""]
   UpperCamelCase       					=					BartTokenizer



   def __init__(							self							:					int,		__A							:					Dict=None,		__A							:					int=None,		__A							:					Optional[Any]=None,		__A							:					List[str]="replace",		__A							:					Any="<s>",		__A							:					Dict="</s>",		__A							:					List[Any]="</s>",		__A							:					int="<s>",		__A							:					Optional[Any]="<unk>",		__A							:					int="<pad>",		__A							:					str="<mask>",		__A							:					List[Any]=False,		__A							:					List[Any]=True,		**__A							:					Any,		):
     super().__init__(
         __A,		__A,		tokenizer_file=__A,		errors=__A,		bos_token=__A,		eos_token=__A,		sep_token=__A,		cls_token=__A,		unk_token=__A,		pad_token=__A,		mask_token=__A,		add_prefix_space=__A,		trim_offsets=__A,		**__A,		)

     UpperCAmelCase	:							Dict						=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
     if pre_tok_state.get('''add_prefix_space''',		__A						) != add_prefix_space:
       UpperCAmelCase	:							Optional[Any]						=			getattr(__A,		pre_tok_state.pop('''type'''						)						)
       UpperCAmelCase	:							List[str]						=			add_prefix_space
       UpperCAmelCase	:							Optional[Any]						=			pre_tok_class(**__A						)

     UpperCAmelCase	:							str						=			add_prefix_space

     # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
     UpperCAmelCase	:							Optional[Any]						=			'''post_processor'''
     UpperCAmelCase	:							str						=			getattr(self.backend_tokenizer,		__A,		__A						)
     if tokenizer_component_instance:
       UpperCAmelCase	:							str						=			json.loads(tokenizer_component_instance.__getstate__()						)

       # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
       if "sep" in state:
         UpperCAmelCase	:							Optional[Any]						=			tuple(state['''sep''']						)
       if "cls" in state:
         UpperCAmelCase	:							str						=			tuple(state['''cls''']						)

       UpperCAmelCase	:							Union[str, Any]						=			False

       if state.get('''add_prefix_space''',		__A						) != add_prefix_space:
         UpperCAmelCase	:							Dict						=			add_prefix_space
         UpperCAmelCase	:							List[str]						=			True

       if state.get('''trim_offsets''',		__A						) != trim_offsets:
         UpperCAmelCase	:							Union[str, Any]						=			trim_offsets
         UpperCAmelCase	:							Dict						=			True

       if changes_to_apply:
         UpperCAmelCase	:							str						=			getattr(__A,		state.pop('''type'''						)						)
         UpperCAmelCase	:							Optional[int]						=			component_class(**__A						)
         setattr(self.backend_tokenizer,		__A,		__A						)



   @property
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     if self._mask_token is None:
       if self.verbose:
         logger.error('''Using mask_token, but it is not set yet.'''						)
       return None
     return str(self._mask_token						)



   @mask_token.setter
   def 	__magic_name__   (							self							:					Optional[int],		__A							:					Dict						):
     UpperCAmelCase	:							Dict						=			AddedToken(__A,		lstrip=__A,		rstrip=__A						) if isinstance(__A,		__A						) else value
     UpperCAmelCase	:							Tuple						=			value



   def 	__magic_name__   (							self							:					Tuple,		*__A							:					int,		**__A							:					Tuple						):
     UpperCAmelCase	:							int						=			kwargs.get('''is_split_into_words''',		__A						)

     if is_split_into_words and not self.add_prefix_space:
       raise ValueError(
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           '''to use it with pretokenized inputs.'''						)

     return super()._batch_encode_plus(*__A,		**__A						)



   def 	__magic_name__   (							self							:					str,		*__A							:					Dict,		**__A							:					List[Any]						):
     UpperCAmelCase	:							int						=			kwargs.get('''is_split_into_words''',		__A						)

     if is_split_into_words and not self.add_prefix_space:
       raise ValueError(
           F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
           '''to use it with pretokenized inputs.'''						)

     return super()._encode_plus(*__A,		**__A						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					str,		__A							:					Optional[str] = None						):
     UpperCAmelCase	:							int						=			self._tokenizer.model.save(__A,		name=__A						)
     return tuple(__A						)



   def 	__magic_name__   (							self							:					List[Any],		__A							:					Optional[int],		__A							:					str=None						):
     UpperCAmelCase	:							Tuple						=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					List[int],		__A							:					Optional[List[int]] = None						):
     UpperCAmelCase	:							int						=			[self.sep_token_id]
     UpperCAmelCase	:							Dict						=			[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 + sep + token_ids_a + sep						) * [0]








import logging
import os

from .state import PartialState





class  __UpperCAmelCase					(	logging.LoggerAdapter						):



   @staticmethod
   def 	__magic_name__   (							__A							:					str						):
     UpperCAmelCase	:							Dict						=			PartialState()
     return not main_process_only or (main_process_only and state.is_main_process)





   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Union[str, Any],		__A							:					Union[str, Any],		*__A							:					Optional[int],		**__A							:					Tuple						):
     if PartialState._shared_state == {}:
       raise RuntimeError(
           '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''						)
     UpperCAmelCase	:							List[str]						=			kwargs.pop('''main_process_only''',		__A						)
     UpperCAmelCase	:							int						=			kwargs.pop('''in_order''',		__A						)

     if self.isEnabledFor(__A						):
       if self._should_log(__A						):
         UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self.process(__A,		__A						)
         self.logger.log(__A,		__A,		*__A,		**__A						)

       elif in_order:
         UpperCAmelCase	:							Dict						=			PartialState()
         for i in range(state.num_processes						):
           if i == state.process_index:
             UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.process(__A,		__A						)
             self.logger.log(__A,		__A,		*__A,		**__A						)
           state.wait_for_everyone()






def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      str = None      )  ->						Dict:
  if log_level is None:
    UpperCAmelCase	:							Union[str, Any]						=			os.environ.get('''ACCELERATE_LOG_LEVEL'''    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Tuple						=			logging.getLogger(UpperCAmelCase      )
  if log_level is not None:
    logger.setLevel(log_level.upper()      )
    logger.root.setLevel(log_level.upper()      )
  return MultiProcessAdapter(UpperCAmelCase    ,			{}      )





