import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE			:						List[str]				  =		{
    """gpt-neox-20b""": 2048,
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE     					=      ["""input_ids""", """attention_mask"""]

  def __init__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				__SCREAMING_SNAKE_CASE			:   Dict="<|endoftext|>"  ,				__SCREAMING_SNAKE_CASE			:   int="<|endoftext|>"  ,				__SCREAMING_SNAKE_CASE			:   Any="<|endoftext|>"  ,				__SCREAMING_SNAKE_CASE			:   str=False  ,				**__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				):
   super().__init__(
       __SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				tokenizer_file=__SCREAMING_SNAKE_CASE  ,				unk_token=__SCREAMING_SNAKE_CASE  ,				bos_token=__SCREAMING_SNAKE_CASE  ,				eos_token=__SCREAMING_SNAKE_CASE  ,				add_prefix_space=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE  ,				)

   __a							=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
   if pre_tok_state.get("add_prefix_space"  ,				__SCREAMING_SNAKE_CASE							) != add_prefix_space:
    __a							=	getattr(__SCREAMING_SNAKE_CASE  ,				pre_tok_state.pop("type"							)							)
    __a							=	add_prefix_space
    __a							=	pre_tok_class(**__SCREAMING_SNAKE_CASE							)

   __a							=	add_prefix_space

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Optional[str] = None							):
   __a							=	self._tokenizer.model.save(__SCREAMING_SNAKE_CASE  ,				name=__SCREAMING_SNAKE_CASE							)
   return tuple(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   "Conversation"							):
   __a							=	[]
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							) + [self.eos_token_id]							)

   if len(__SCREAMING_SNAKE_CASE							) > self.model_max_length:
    __a							=	input_ids[-self.model_max_length :]
   return input_ids



import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE			:						List[Any]				  =		get_logger(__name__)
class  A_			(							enum.Enum			):
  _SCREAMING_SNAKE_CASE     					=      """all_checks"""
  _SCREAMING_SNAKE_CASE     					=      """basic_checks"""
  _SCREAMING_SNAKE_CASE     					=      """no_checks"""
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass



def 	__A		(      _A				,					_A				,					_A=None       ):



 """simple docstring"""


 if expected_checksums is None:
  logger.info("Unable to verify checksums."       )
  return
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise ExpectedMoreDownloadedFiles(str(set(_A       ) - set(_A       )       )       )
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise UnexpectedDownloadedFile(str(set(_A       ) - set(_A       )       )       )
 __a							=	[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
 __a							=	" for " + verification_name if verification_name is not None else ""
 if len(_A       ) > 0:
  raise NonMatchingChecksumError(
      f"""Checksums didn't match{for_verification_name}:\n"""
      f"""{bad_urls}\n"""
      "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"       )
 logger.info("All the checksums matched successfully" + for_verification_name       )
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 if expected_splits is None:
  logger.info("Unable to verify splits sizes."       )
  return
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise ExpectedMoreSplits(str(set(_A       ) - set(_A       )       )       )
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise UnexpectedSplits(str(set(_A       ) - set(_A       )       )       )
 __a							=	[
     {"expected": expected_splits[name], "recorded": recorded_splits[name]}
     for name in expected_splits
     if expected_splits[name].num_examples != recorded_splits[name].num_examples
 ]
 if len(_A       ) > 0:
  raise NonMatchingSplitsSizesError(str(_A       )       )
 logger.info("All the splits matched successfully."       )



def 	__A		(      _A				,					_A = True       ):



 """simple docstring"""


 if record_checksum:
  __a							=	shaaaa()
  with open(_A				,					"rb"       ) as f:
   for chunk in iter(lambda: f.read(1 << 20       )				,					B""       ):
    m.update(_A       )
   __a							=	m.hexdigest()
 else:
  __a							=	None
 return {"num_bytes": os.path.getsize(_A       ), "checksum": checksum}






def 	__A		(      _A       ):



 """simple docstring"""


 if dataset_size and config.IN_MEMORY_MAX_SIZE:
  return dataset_size < config.IN_MEMORY_MAX_SIZE
 else:
  return False


