




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


lowerCamelCase       :       Any        =    logging.get_logger(__name__)




@dataclass
class 						__lowercase		(SCREAMING_SNAKE_CASE__							):




      """simple docstring"""





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

      def __init__(      self				,      **A	)		->				List[Any]:
         for deprecated_arg in self.deprecated_args:
            if deprecated_arg in kwargs:
               snake_case      :				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]}"""	)

         snake_case      :				Tuple				=       kwargs.pop("""torchscript"""				,      self.torchscript	)
         snake_case      :				Tuple				=       kwargs.pop("""torch_xla_tpu_print_metrics"""				,      self.torch_xla_tpu_print_metrics	)
         snake_case      :				List[str]				=       kwargs.pop("""fp16_opt_level"""				,      self.fpaa_opt_level	)
         super().__init__(**A	)

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

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

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

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

      @property
      def 	UpperCAmelCase      (      self	)		->				Dict:
         requires_backends(self				,      ["""torch"""]	)
         return self._setup_devices[1]

      @property
      def 	UpperCAmelCase      (      self	)		->				List[Any]:
         return self.n_gpu > 0






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  CpmAntTokenizer
  lowerCamelCase__   =  False







  def       __A   (							self       :				List[str]      )     ->		str:
        super().setUp()

        SCREAMING_SNAKE_CASE_      =     [
            "<d>",
            "</d>",
            "<s>",
            "</s>",
            "</_>",
            "<unk>",
            "<pad>",
            "</n>",
            "我",
            "是",
            "C",
            "P",
            "M",
            "A",
            "n",
            "t",
        ]
        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]      )
        with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"      ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )







  @tooslow
  def       __A   (							self       :				int      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     ["今天", "天气", "真", "好", "！"]
        SCREAMING_SNAKE_CASE_      =     tokenizer.tokenize(__magic_name__      )
        self.assertListEqual(__magic_name__     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     [tokenizer.bos_token] + tokens

        SCREAMING_SNAKE_CASE_      =     [6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__      )     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.decode(__magic_name__      )
        self.assertEqual(__magic_name__     ,   __magic_name__      )


