'''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


__a:			Dict	       =       logging.get_logger(__name__)



@dataclass
class       UpperCAmelCase  (						A__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   [
           "no_inference",
           "no_cuda",
           "no_tpu",
           "no_speed",
           "no_memory",
           "no_env_print",
           "no_multi_process",
       ]

       def __init__(						self     ,       **__lowerCAmelCase			)  ->  Union[str, Any]:
              for deprecated_arg in self.deprecated_args:
                     if deprecated_arg in kwargs:
                            lowercase__    :   int						= deprecated_arg[3:]
                            setattr(self     ,       __A     ,       not kwargs.pop(__A			)			)
                            logger.warning(
                                F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                                F""" {positive_arg}={kwargs[positive_arg]}"""			)

              lowercase__    :   Optional[int]						= kwargs.pop('''torchscript'''     ,       self.torchscript			)
              lowercase__    :   List[str]						= kwargs.pop('''torch_xla_tpu_print_metrics'''     ,       self.torch_xla_tpu_print_metrics			)
              lowercase__    :   Tuple						= kwargs.pop('''fp16_opt_level'''     ,       self.fpaa_opt_level			)
              super().__init__(**__A			)

       SCREAMING_SNAKE_CASE 		=   field(default=A__   ,						metadata={"help": "Trace the models using torchscript"} )
       SCREAMING_SNAKE_CASE 		=   field(default=A__   ,						metadata={"help": "Print Xla/PyTorch tpu metrics"} )
       SCREAMING_SNAKE_CASE 		=   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 _lowerCAmelCase(						self			)  ->  Tuple["torch.device", int]:
              requires_backends(self     ,       ['''torch''']			)
              logger.info('''PyTorch: setting up devices'''			)
              if not self.cuda:
                     lowercase__    :   str						= torch.device('''cpu'''			)
                     lowercase__    :   Tuple						= 0
              elif is_torch_tpu_available():
                     lowercase__    :   int						= xm.xla_device()
                     lowercase__    :   Optional[Any]						= 0
              else:
                     lowercase__    :   Tuple						= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)
                     lowercase__    :   List[str]						= torch.cuda.device_count()
              return device, n_gpu

       @property
       def _lowerCAmelCase(						self			)  ->  int:
              return is_torch_tpu_available() and self.tpu

       @property
       def _lowerCAmelCase(						self			)  ->  int:
              requires_backends(self     ,       ['''torch''']			)
              # TODO(PVP): currently only single GPU is supported
              return torch.cuda.current_device()

       @property
       def _lowerCAmelCase(						self			)  ->  "torch.device":
              requires_backends(self     ,       ['''torch''']			)
              return self._setup_devices[0]

       @property
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
              requires_backends(self     ,       ['''torch''']			)
              return self._setup_devices[1]


       @property
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
              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_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


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

__a:			Tuple	       =       {"""tokenizer_file""": """tokenizer.json"""}

__a:			Union[str, Any]	       =       {
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE 		=   ["input_ids", "attention_mask"]
       SCREAMING_SNAKE_CASE 		=   None

       def __init__(						self     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase="<unk>"     ,       __lowerCAmelCase="<s>"     ,       __lowerCAmelCase="</s>"     ,       __lowerCAmelCase="<pad>"     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=False     ,       **__lowerCAmelCase     ,       )  ->  Union[str, Any]:
        super().__init__(
            __lowerCAmelCase     ,       __lowerCAmelCase     ,       tokenizer_file=__lowerCAmelCase     ,       unk_token=__lowerCAmelCase     ,       bos_token=__lowerCAmelCase     ,       eos_token=__lowerCAmelCase     ,       pad_token=__lowerCAmelCase     ,       add_prefix_space=__lowerCAmelCase     ,       clean_up_tokenization_spaces=__lowerCAmelCase     ,       **__lowerCAmelCase     ,       )
        lowercase__    :   int						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
        if pre_tok_state.get('''add_prefix_space'''     ,       __lowerCAmelCase			) != add_prefix_space:
         lowercase__    :   int						= getattr(__lowerCAmelCase     ,       pre_tok_state.pop('''type'''			)			)
         lowercase__    :   Tuple						= add_prefix_space
         lowercase__    :   List[str]						= pre_tok_class(**__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= add_prefix_space

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  BatchEncoding:
        lowercase__    :   Dict						= kwargs.get('''is_split_into_words'''     ,       __lowerCAmelCase			)
        if not (self.add_prefix_space or not is_split_into_words):
         raise Exception(
             F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
             ''' pretokenized inputs.'''			)

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

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  BatchEncoding:
        lowercase__    :   str						= kwargs.get('''is_split_into_words'''     ,       __lowerCAmelCase			)

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

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Tuple[str]:
        lowercase__    :   List[Any]						= self._tokenizer.model.save(__lowerCAmelCase     ,       name=__lowerCAmelCase			)
        return tuple(__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[int]:
        lowercase__    :   Dict						= []
        for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			) + [self.eos_token_id]			)

        if len(__lowerCAmelCase			) > self.model_max_length:
         lowercase__    :   Optional[Any]						= input_ids[-self.model_max_length :]
        return input_ids

