





'''simple docstring'''

from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
   import torch

if is_torch_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm


UpperCAmelCase					:		Tuple  =							logging.get_logger(__name__)





@dataclass
class lowerCamelCase						(a__					):
  _lowercase  :	Any       					=				[
      """no_inference""",
      """no_cuda""",
      """no_tpu""",
      """no_speed""",
      """no_memory""",
      """no_env_print""",
      """no_multi_process""",
  ]







  def __init__(		self     ,     **lowercase__		)		->						List[Any]:






        """simple docstring"""





        for deprecated_arg in self.deprecated_args:
              if deprecated_arg in kwargs:
                    _snake_case		:	Optional[int]					     =     deprecated_arg[3:]
                    setattr(self     ,     lowercase__     ,     not kwargs.pop(lowercase__		)		)
                    logger.warning(
                        F'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
                        F''' {positive_arg}={kwargs[positive_arg]}'''		)

        _snake_case		:	Optional[Any]					     =     kwargs.pop('''torchscript'''     ,     self.torchscript		)
        _snake_case		:	Dict					     =     kwargs.pop('''torch_xla_tpu_print_metrics'''     ,     self.torch_xla_tpu_print_metrics		)
        _snake_case		:	Tuple					     =     kwargs.pop('''fp16_opt_level'''     ,     self.fpaa_opt_level		)
        super().__init__(**lowercase__		)

  _lowercase  :	bool       					=				field(default=a__						,      metadata={"""help""": """Trace the models using torchscript"""}					)
  _lowercase  :	bool       					=				field(default=a__						,      metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}					)
  _lowercase  :	str       					=				field(
      default="""O1"""						,      metadata={
          """help""": (
              """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
              """See details at https://nvidia.github.io/apex/amp.html"""
          )
      }						,      )







  @cached_property
  def 				UpperCAmelCase_				(		self		)		->						Tuple["torch.device", int]:






        """simple docstring"""





        requires_backends(self     ,     ['''torch''']		)
        logger.info('''PyTorch: setting up devices'''		)
        if not self.cuda:
              _snake_case		:	List[str]					     =     torch.device('''cpu'''		)
              _snake_case		:	int					     =     0
        elif is_torch_tpu_available():
              _snake_case		:	Tuple					     =     xm.xla_device()
              _snake_case		:	int					     =     0
        else:
              _snake_case		:	Union[str, Any]					     =     torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''		)
              _snake_case		:	Optional[Any]					     =     torch.cuda.device_count()
        return device, n_gpu







  @property
  def 				UpperCAmelCase_				(		self		)		->						List[Any]:






        """simple docstring"""





        return is_torch_tpu_available() and self.tpu







  @property
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        requires_backends(self     ,     ['''torch''']		)
        # TODO(PVP): currently only single GPU is supported
        return torch.cuda.current_device()







  @property
  def 				UpperCAmelCase_				(		self		)		->						"torch.device":






        """simple docstring"""





        requires_backends(self     ,     ['''torch''']		)
        return self._setup_devices[0]







  @property
  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        requires_backends(self     ,     ['''torch''']		)
        return self._setup_devices[1]







  @property
  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        return self.n_gpu > 0









'''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


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

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

UpperCAmelCase					:		Any		=							{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase					:		Optional[Any]		=							{
    'gpt-neox-20b': 2_0_4_8,
}





class lowerCamelCase						(a__					):
  _lowercase  :	Optional[int]       					=				VOCAB_FILES_NAMES
  _lowercase  :	str       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Dict       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	Optional[int]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__=False     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





        super().__init__(
            lowercase__     ,     lowercase__     ,     tokenizer_file=lowercase__     ,     unk_token=lowercase__     ,     bos_token=lowercase__     ,     eos_token=lowercase__     ,     add_prefix_space=lowercase__     ,     **lowercase__     ,     )

        _snake_case		:	Optional[int]					     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get('''add_prefix_space'''     ,     lowercase__		) != add_prefix_space:
              _snake_case		:	int					     =     getattr(lowercase__     ,     pre_tok_state.pop('''type'''		)		)
              _snake_case		:	int					     =     add_prefix_space
              _snake_case		:	Optional[Any]					     =     pre_tok_class(**lowercase__		)

        _snake_case		:	List[str]					     =     add_prefix_space







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self._tokenizer.model.save(lowercase__     ,     name=lowercase__		)
        return tuple(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[int]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase__     ,     add_special_tokens=lowercase__		) + [self.eos_token_id]		)

        if len(lowercase__		) > self.model_max_length:
              _snake_case		:	Dict					     =     input_ids[-self.model_max_length :]
        return input_ids


