




import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
 import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_			=     logging.get_logger(__name__)

a_			=     {"vocab_file": "vocab.txt"}

a_			=     {
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

a_			=     {
    "openbmb/cpm-ant-10b": 1024,
}




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Any:

    """simple docstring"""


    __UpperCamelCase  		=						collections.OrderedDict()
    with open(SCREAMING_SNAKE_CASE_					,     '''r'''					,     encoding='''utf-8'''      ) as reader:
        __UpperCamelCase  		=						reader.readlines()
    for index, token in enumerate(SCREAMING_SNAKE_CASE_      ):
        __UpperCamelCase  		=						token.rstrip('''\n'''      )
        __UpperCamelCase  		=						index
    return vocab

class  _lowerCamelCase						(			lowercase__   ):

      """simple docstring"""

      def __init__(     self					: Dict ,       snake_case					: List[Any] ,       snake_case					: Optional[int]="<unk>" ,       snake_case					: int=200				):
          __UpperCamelCase  		=						vocab
          __UpperCamelCase  		=						unk_token
          __UpperCamelCase  		=						max_input_chars_per_word







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: str				):
          __UpperCamelCase  		=						list(__lowercase				)
          if len(__lowercase				) > self.max_input_chars_per_word:
              return [self.unk_token]

          __UpperCamelCase  		=						0
          __UpperCamelCase  		=						[]
          while start < len(__lowercase				):
              __UpperCamelCase  		=						len(__lowercase				)
              __UpperCamelCase  		=						None
              while start < end:
                  __UpperCamelCase  		=						''''''.join(chars[start:end]				)
                  if substr in self.vocab:
                      __UpperCamelCase  		=						substr
                      break
                  end -= 1
              if cur_substr is None:
                  sub_tokens.append(self.unk_token				)
                  start += 1
              else:
                  sub_tokens.append(__lowercase				)
                  __UpperCamelCase  		=						end

          return sub_tokens







class  _lowerCamelCase						(			lowercase__   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					VOCAB_FILES_NAMES
      lowerCAmelCase__			: Union[str, Any]    =					PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__			: Any    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__			: int    =					["""input_ids""", """attention_mask"""]
      lowerCAmelCase__			: List[str]    =					False







      def __init__(     self					: Tuple ,       snake_case					: List[Any] ,       snake_case					: Optional[Any]="<d>" ,       snake_case					: List[str]="</d>" ,       snake_case					: Dict="<s>" ,       snake_case					: Any="</s>" ,       snake_case					: Optional[Any]="<pad>" ,       snake_case					: str="<unk>" ,       snake_case					: Any="</n>" ,       snake_case					: List[str]="</_>" ,       snake_case					: Union[str, Any]="left" ,       **snake_case					: Dict ,       ):
          requires_backends(self ,       ['''jieba''']				)
          super().__init__(
              bod_token=__lowercase ,       eod_token=__lowercase ,       bos_token=__lowercase ,       eos_token=__lowercase ,       pad_token=__lowercase ,       unk_token=__lowercase ,       line_token=__lowercase ,       space_token=__lowercase ,       padding_side=__lowercase ,       **__lowercase ,       )
          __UpperCamelCase  		=						bod_token
          __UpperCamelCase  		=						eod_token
          __UpperCamelCase  		=						load_vocab(__lowercase				)
          __UpperCamelCase  		=						self.encoder[space_token]
          __UpperCamelCase  		=						self.encoder[line_token]

          del self.encoder[space_token]
          del self.encoder[line_token]

          __UpperCamelCase  		=						collections.OrderedDict(sorted(self.encoder.items() ,       key=lambda snake_case				: x[1]				)				)
          __UpperCamelCase  		=						{v: k for k, v in self.encoder.items()}

          __UpperCamelCase  		=						WordpieceTokenizer(vocab=self.encoder ,       unk_token=self.unk_token				)







      @property
      def       snake_case	(     self					: List[Any]				):
          return self.encoder[self.bod_token]







      @property
      def       snake_case	(     self					: Optional[Any]				):
          return self.encoder[self.eod_token]







      @property
      def       snake_case	(     self					: int				):
          return self.encoder["\n"]







      @property
      def       snake_case	(     self					: int				):
          return len(self.encoder				)







      def       snake_case	(     self					: Union[str, Any]				):
          return dict(self.encoder ,       **self.added_tokens_encoder				)







      def       snake_case	(     self					: Any ,       snake_case					: Dict				):
          __UpperCamelCase  		=						[]
          for x in jieba.cut(__lowercase ,       cut_all=__lowercase				):
              output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowercase				)				)
          return output_tokens







      def       snake_case	(     self					: Optional[int] ,       snake_case					: List[Any] ,       **snake_case					: List[Any]				):
          __UpperCamelCase  		=						[i for i in token_ids if i >= 0]
          __UpperCamelCase  		=						[
              x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
          ]
          return super()._decode(__lowercase ,       **__lowercase				)







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: List[Any]				):
          return token in self.encoder







      def       snake_case	(     self					: int ,       snake_case					: List[str]				):
          return "".join(__lowercase				)







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: str				):
          return self.encoder.get(__lowercase ,       self.encoder.get(self.unk_token				)				)







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: Any				):
          return self.decoder.get(__lowercase ,       self.unk_token				)







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: str ,       snake_case					: Optional[str] = None				):
          if os.path.isdir(__lowercase				):
              __UpperCamelCase  		=						os.path.join(
                  __lowercase ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
          else:
              __UpperCamelCase  		=						(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
          __UpperCamelCase  		=						0
          if " " in self.encoder:
              __UpperCamelCase  		=						self.encoder[''' ''']
              del self.encoder[" "]
          if "\n" in self.encoder:
              __UpperCamelCase  		=						self.encoder['''\n''']
              del self.encoder["\n"]
          __UpperCamelCase  		=						collections.OrderedDict(sorted(self.encoder.items() ,       key=lambda snake_case				: x[1]				)				)
          with open(__lowercase ,       '''w''' ,       encoding='''utf-8'''				) as writer:
              for token, token_index in self.encoder.items():
                  if index != token_index:
                      logger.warning(
                          F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                          ''' Please check that the vocabulary is not corrupted!'''				)
                      __UpperCamelCase  		=						token_index
                  writer.write(token + '''\n'''				)
                  index += 1
          return (vocab_file,)







      def       snake_case	(     self					: Tuple ,       snake_case					: List[int] ,       snake_case					: List[int] = None				):
          if token_ids_a is None:
              return [self.bos_token_id] + token_ids_a
          return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a







      def       snake_case	(     self					: List[str] ,       snake_case					: List[int] ,       snake_case					: Optional[List[int]] = None ,       snake_case					: bool = False				):

          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__lowercase ,       token_ids_a=__lowercase ,       already_has_special_tokens=__lowercase				)

          if token_ids_a is not None:
              return [1] + ([0] * len(__lowercase				)) + [1] + ([0] * len(__lowercase				))
          return [1] + ([0] * len(__lowercase				))












import os
import time

import numpy as np
import onnxruntime as ort


a_   		=     "1"
a_   		=     "0"
a_   		=     "1"

a_   		=     ort.SessionOptions()
a_   		=     ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
a_   		=     ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
a_   		=     ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
a_   		=     ort.RunOptions()

a_   		=     128
a_   		=     1
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
a_   		=     time.time()
a_   		=     2000
a_   		=     {}
for iter in range(max_iters):
 a_   		=     sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))






