


'''simple docstring'''

import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


a_						:       List[str]      			=				namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def __snake_case      (   UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		List[str]	):
 return (abs(source - target	) / target) < 0.01







@pytest.mark.integration
def __snake_case      (   UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						_TestCommandArgs(dataset=UpperCAmelCase_      ,       all_configs=UpperCAmelCase_      ,       save_infos=UpperCAmelCase_	)
 lowerCamelCase_  				=						TestCommand(*UpperCAmelCase_	)
 test_command.run()
 lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "README.md"	)
 assert os.path.exists(UpperCAmelCase_	)
 lowerCamelCase_  				=						DatasetInfosDict.from_directory(UpperCAmelCase_	)
 lowerCamelCase_  				=						DatasetInfosDict(
     {
         "default": DatasetInfo(
             features=Features(
                 {
                     "tokens": Sequence(Value("string"	)	),
                     "ner_tags": Sequence(
                         ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]	)	),
                     "langs": Sequence(Value("string"	)	),
                     "spans": Sequence(Value("string"	)	),
                 }	)      ,       splits=[
                 {
                     "name": "train",
                     "num_bytes": 2351563,
                     "num_examples": 10000,
                 },
                 {
                     "name": "validation",
                     "num_bytes": 238418,
                     "num_examples": 1000,
                 },
             ]      ,       download_size=3940680      ,       dataset_size=2589981      ,       )
     }	)
 assert dataset_infos.keys() == expected_dataset_infos.keys()
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  lowerCamelCase_				,lowerCamelCase_  				=						getattr(dataset_infos["default"]      ,       UpperCAmelCase_	), getattr(expected_dataset_infos["default"]      ,       UpperCAmelCase_	)
  if key == "num_bytes":
   assert is_apercent_close(UpperCAmelCase_      ,       UpperCAmelCase_	)
  elif key == "splits":
   assert list(UpperCAmelCase_	) == list(UpperCAmelCase_	)
   for split in result:
    assert result[split].name == expected[split].name
    assert result[split].num_examples == expected[split].num_examples
    assert is_apercent_close(result[split].num_bytes      ,       expected[split].num_bytes	)
  else:
   result == expected







'''simple docstring'''

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


a_						:       Dict      			=				logging.get_logger(__name__)

a_						:       List[Any]      			=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

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

a_						:       str      			=				{
    """gpt-neox-20b""": 2048,
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						VOCAB_FILES_NAMES
  _lowerCamelCase						=						PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase						=						["input_ids", "attention_mask"]






  def __init__(					self    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase=False    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(
       UpperCamelCase    ,						UpperCamelCase    ,						tokenizer_file=UpperCamelCase    ,						unk_token=UpperCamelCase    ,						bos_token=UpperCamelCase    ,						eos_token=UpperCamelCase    ,						add_prefix_space=UpperCamelCase    ,						**UpperCamelCase    ,						)

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

   lowerCamelCase_  				=						add_prefix_space






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   lowerCamelCase_  				=						self._tokenizer.model.save(UpperCamelCase    ,						name=UpperCamelCase  )
   return tuple(UpperCamelCase  )







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  ) + [self.eos_token_id]  )

   if len(UpperCamelCase  ) > self.model_max_length:
    lowerCamelCase_  				=						input_ids[-self.model_max_length :]
   return input_ids



