'''simple docstring'''






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation

_UpperCAmelCase    :     Tuple         =       logging.get_logger(__name__)


_UpperCAmelCase    :     Dict         =       {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_UpperCAmelCase    :     str         =       {
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

_UpperCAmelCase    :     Tuple         =       {'''facebook/blenderbot-3B''': 1_28}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  (
         list(range(ord('!'				)   ,		ord('~'				) + 1				)				) + list(range(ord('¡'				)   ,		ord('¬'				) + 1				)				) + list(range(ord('®'				)   ,		ord('ÿ'				) + 1				)				)
     )
     _A    		=  bs[:]
     _A    		=  0
     for b in range(2**8				):
          if b not in bs:
               bs.append(__snake_case				)
               cs.append(2**8 + n				)
               n += 1
     _A    		=  [chr(__snake_case				) for n in cs]
     return dict(zip(__snake_case   ,		__snake_case				)				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any				):
     _A    		=  set()
     _A    		=  word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)				)
          _A    		=  char
     return pairs







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   VOCAB_FILES_NAMES
   __lowerCAmelCase         =   PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase         =   ["input_ids", "attention_mask"]
   def __init__(      self      :			Optional[Any], UpperCamelCase__      :			Any, UpperCamelCase__      :			Dict, UpperCamelCase__      :			Optional[int]="replace", UpperCamelCase__      :			List[str]="<s>", UpperCamelCase__      :			Any="</s>", UpperCamelCase__      :			List[str]="</s>", UpperCamelCase__      :			List[Any]="<s>", UpperCamelCase__      :			Optional[Any]="<unk>", UpperCamelCase__      :			Tuple="<pad>", UpperCamelCase__      :			Optional[int]="<mask>", UpperCamelCase__      :			List[str]=False, **UpperCamelCase__      :			Optional[Any], )  ->			Dict:
        _A    		=  AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__, UpperCamelCase__     ) else bos_token
        _A    		=  AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__, UpperCamelCase__     ) else eos_token
        _A    		=  AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__, UpperCamelCase__     ) else sep_token
        _A    		=  AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__, UpperCamelCase__     ) else cls_token
        _A    		=  AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__, UpperCamelCase__     ) else unk_token
        _A    		=  AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__, UpperCamelCase__     ) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        _A    		=  AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__, UpperCamelCase__     ) else mask_token

        super().__init__(
            errors=UpperCamelCase__, bos_token=UpperCamelCase__, eos_token=UpperCamelCase__, unk_token=UpperCamelCase__, sep_token=UpperCamelCase__, cls_token=UpperCamelCase__, pad_token=UpperCamelCase__, mask_token=UpperCamelCase__, add_prefix_space=UpperCamelCase__, **UpperCamelCase__, )

        with open(UpperCamelCase__, encoding='utf-8'     ) as vocab_handle:
             _A    		=  json.load(UpperCamelCase__     )
        _A    		=  {v: k for k, v in self.encoder.items()}
        _A    		=  errors  # how to handle errors in decoding
        _A    		=  bytes_to_unicode()
        _A    		=  {v: k for k, v in self.byte_encoder.items()}
        with open(UpperCamelCase__, encoding='utf-8'     ) as merges_handle:
             _A    		=  merges_handle.read().split('\n'     )[1:-1]
        _A    		=  [tuple(merge.split()     ) for merge in bpe_merges]
        _A    		=  dict(zip(UpperCamelCase__, range(len(UpperCamelCase__     )     )     )     )
        _A    		=  {}
        _A    		=  add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        _A    		=  re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'     )
   @property
   # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Dict:
        return len(self.encoder     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Tuple:
        return dict(self.encoder, **self.added_tokens_encoder     )
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			Optional[int]     )  ->			Any:
        if token in self.cache:
             return self.cache[token]
        _A    		=  tuple(UpperCamelCase__     )
        _A    		=  get_pairs(UpperCamelCase__     )

        if not pairs:
             return token

        while True:
             _A    		=  min(UpperCamelCase__, key=lambda UpperCamelCase__     : self.bpe_ranks.get(UpperCamelCase__, float('inf'     )     )     )
             if bigram not in self.bpe_ranks:
                  break
             _A		,       _A    		=  bigram
             _A    		=  []
             _A    		=  0
             while i < len(UpperCamelCase__     ):
                  try:
                       _A    		=  word.index(UpperCamelCase__, UpperCamelCase__     )
                  except ValueError:
                       new_word.extend(word[i:]     )
                       break
                  else:
                       new_word.extend(word[i:j]     )
                       _A    		=  j

                  if word[i] == first and i < len(UpperCamelCase__     ) - 1 and word[i + 1] == second:
                       new_word.append(first + second     )
                       i += 2
                  else:
                       new_word.append(word[i]     )
                       i += 1
             _A    		=  tuple(UpperCamelCase__     )
             _A    		=  new_word
             if len(UpperCamelCase__     ) == 1:
                  break
             else:
                  _A    		=  get_pairs(UpperCamelCase__     )
        _A    		=  ' '.join(UpperCamelCase__     )
        _A    		=  word
        return word
   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			int     )  ->			Optional[Any]:
        _A    		=  []
        for token in re.findall(self.pat, UpperCamelCase__     ):
             _A    		=  ''.join(
                 self.byte_encoder[b] for b in token.encode('utf-8'     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
             bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__     ).split(' '     )     )
        return bpe_tokens
   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			Any     )  ->			Dict:
        return self.encoder.get(UpperCamelCase__, self.encoder.get(self.unk_token     )     )
   def 	__UpperCAmelCase   (      self      :			List[Any], UpperCamelCase__      :			Any     )  ->			Optional[int]:
        return self.decoder.get(UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			int     )  ->			Any:
        _A    		=  ''.join(UpperCamelCase__     )
        _A    		=  bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8', errors=self.errors     )
        return text
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[str] = None     )  ->			Tuple[str]:
        if not os.path.isdir(UpperCamelCase__     ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
             return
        _A    		=  os.path.join(
            UpperCamelCase__, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
        _A    		=  os.path.join(
            UpperCamelCase__, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']     )

        with open(UpperCamelCase__, 'w', encoding='utf-8'     ) as f:
             f.write(json.dumps(self.encoder, indent=2, sort_keys=UpperCamelCase__, ensure_ascii=UpperCamelCase__     ) + '\n'     )

        _A    		=  0
        with open(UpperCamelCase__, 'w', encoding='utf-8'     ) as writer:
             writer.write('#version: 0.2\n'     )
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda UpperCamelCase__     : kv[1]     ):
                  if index != token_index:
                       logger.warning(
                           f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                           ' Please check that the tokenizer is not corrupted!'     )
                       _A    		=  token_index
                  writer.write(' '.join(UpperCamelCase__     ) + '\n'     )
                  index += 1

        return vocab_file, merge_file
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			List[int], UpperCamelCase__      :			Optional[List[int]] = None, UpperCamelCase__      :			bool = False     )  ->			List[int]:
        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=UpperCamelCase__, token_ids_a=UpperCamelCase__, already_has_special_tokens=UpperCamelCase__     )

        if token_ids_a is None:
             return [1] + ([0] * len(UpperCamelCase__     )) + [1]
        return [1] + ([0] * len(UpperCamelCase__     )) + [1, 1] + ([0] * len(UpperCamelCase__     )) + [1]
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			List[int], UpperCamelCase__      :			Optional[List[int]] = 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 + sep + token_ids_a + sep     ) * [0]
   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			List[Any]=False, **UpperCamelCase__      :			int     )  ->			Tuple:
        _A    		=  kwargs.pop('add_prefix_space', self.add_prefix_space     )
        if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__     ) > 0 and not text[0].isspace()):
             _A    		=  ' ' + text
        return (text, kwargs)
   def 	__UpperCAmelCase   (      self      :			List[str], UpperCamelCase__      :			List[int], UpperCamelCase__      :			Optional[List[int]] = None     )  ->			Any:
        return token_ids_a + [self.eos_token_id]

   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			"Conversation"     )  ->			List[int]:
        _A    		=  []
        for is_user, text in conversation.iter_texts():
             if is_user:
                  # We need to space prefix as it's being done within blenderbot
                  inputs.append(' ' + text     )
             else:
                  # Generated responses should contain them already.
                  inputs.append(UpperCamelCase__     )

        _A    		=  '  '.join(UpperCamelCase__     )
        _A    		=  self.encode(UpperCamelCase__     )
        if len(UpperCamelCase__     ) > self.model_max_length:
             _A    		=  input_ids[-self.model_max_length :]
             logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'     )
        return input_ids




'''simple docstring'''






_UpperCAmelCase    :     Any         =       '''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
   from .utils import rich



