




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


_UpperCAmelCase	:						Dict			=     logging.get_logger(__name__)

_UpperCAmelCase	:						List[str]			=     {"vocab_file": "vocab.txt"}

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

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



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :int      	=						collections.OrderedDict()
  with open(lowerCamelCase,			"r",			encoding="utf-8"   ) as reader:
    lowercase    :Dict      	=						reader.readlines()
  for index, token in enumerate(lowerCamelCase   ):
    lowercase    :Dict      	=						token.rstrip("\n"   )
    lowercase    :List[str]      	=						index
  return vocab




class    __lowerCAmelCase       (							lowerCAmelCase):






       def __init__(   self:					Optional[int]       ,					_lowerCAmelCase:					Optional[Any]       ,					_lowerCAmelCase:					List[str]="<unk>"       ,					_lowerCAmelCase:					Optional[Any]=2_00				):
         lowercase    :Tuple      	=						vocab
         lowercase    :str      	=						unk_token
         lowercase    :List[str]      	=						max_input_chars_per_word






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					Dict				):
         lowercase    :Tuple      	=						list(_lowerCAmelCase				)
         if len(_lowerCAmelCase				) > self.max_input_chars_per_word:
           return [self.unk_token]

         lowercase    :Union[str, Any]      	=						0
         lowercase    :Tuple      	=						[]
         while start < len(_lowerCAmelCase				):
           lowercase    :Union[str, Any]      	=						len(_lowerCAmelCase				)
           lowercase    :List[str]      	=						None
           while start < end:
             lowercase    :List[Any]      	=						"".join(chars[start:end]				)
             if substr in self.vocab:
               lowercase    :Tuple      	=						substr
               break
             end -= 1
           if cur_substr is None:
             sub_tokens.append(self.unk_token				)
             start += 1
           else:
             sub_tokens.append(_lowerCAmelCase				)
             lowercase    :Tuple      	=						end

         return sub_tokens







class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						VOCAB_FILES_NAMES
       _a 					=						PRETRAINED_VOCAB_FILES_MAP
       _a 					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _a 					=						['''input_ids''', '''attention_mask''']
       _a 					=						False






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

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

         lowercase    :Tuple      	=						collections.OrderedDict(sorted(self.encoder.items()       ,					key=lambda _lowerCAmelCase				: x[1]				)				)
         lowercase    :Optional[Any]      	=						{v: k for k, v in self.encoder.items()}

         lowercase    :Any      	=						WordpieceTokenizer(vocab=self.encoder       ,					unk_token=self.unk_token				)






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         return self.encoder[self.bod_token]






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         return self.encoder[self.eod_token]






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         return self.encoder["\n"]






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         return len(self.encoder				)






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






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					Optional[int]				):
         lowercase    :List[Any]      	=						[]
         for x in jieba.cut(_lowerCAmelCase       ,					cut_all=_lowerCAmelCase				):
           output_tokens.extend(self.wordpiece_tokenizer.tokenize(_lowerCAmelCase				)				)
         return output_tokens






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict       ,					_lowerCAmelCase:					Tuple       ,					**_lowerCAmelCase:					List[Any]				):
         lowercase    :List[str]      	=						[i for i in token_ids if i >= 0]
         lowercase    :str      	=						[
             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(_lowerCAmelCase       ,					**_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					Dict				):
         return token in self.encoder






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					_lowerCAmelCase:					List[str]				):
         return "".join(_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					List[Any]				):
         return self.encoder.get(_lowerCAmelCase       ,					self.encoder.get(self.unk_token				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					Tuple				):
         return self.decoder.get(_lowerCAmelCase       ,					self.unk_token				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					Optional[str] = None				):
         if os.path.isdir(_lowerCAmelCase				):
           lowercase    :Optional[Any]      	=						os.path.join(
               _lowerCAmelCase       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)
         else:
           lowercase    :Tuple      	=						(filename_prefix + "-" if filename_prefix else "") + save_directory
         lowercase    :int      	=						0
         if " " in self.encoder:
           lowercase    :Tuple      	=						self.encoder[" "]
           del self.encoder[" "]
         if "\n" in self.encoder:
           lowercase    :List[str]      	=						self.encoder["\n"]
           del self.encoder["\n"]
         lowercase    :List[Any]      	=						collections.OrderedDict(sorted(self.encoder.items()       ,					key=lambda _lowerCAmelCase				: x[1]				)				)
         with open(_lowerCAmelCase       ,					"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!"				)
               lowercase    :Optional[int]      	=						token_index
             writer.write(token + "\n"				)
             index += 1
         return (vocab_file,)






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

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

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









import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
     import onnxruntime as ort


_UpperCAmelCase	:						Dict			=     logging.get_logger(__name__)

_UpperCAmelCase	:						List[Any]			=     {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}




class    __lowerCAmelCase       :






       def __init__(   self:					Dict       ,					_lowerCAmelCase:					Union[str, Any]=None       ,					**_lowerCAmelCase:					Any				):
         logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."				)
         lowercase    :str      	=						model
         lowercase    :Any      	=						kwargs.get("model_save_dir"       ,					_lowerCAmelCase				)
         lowercase    :Dict      	=						kwargs.get("latest_model_name"       ,					_lowerCAmelCase				)






       def __call__(   self:					Optional[Any]       ,					**_lowerCAmelCase:					List[Any]				):
         lowercase    :List[str]      	=						{k: np.array(_lowerCAmelCase				) for k, v in kwargs.items()}
         return self.model.run(_lowerCAmelCase       ,					_lowerCAmelCase				)






       @staticmethod
       def 					SCREAMING_SNAKE_CASE    (   _lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					List[Any]=None       ,					_lowerCAmelCase:					List[Any]=None				):
         if provider is None:
           logger.info("No onnxruntime provider specified, using CPUExecutionProvider"				)
           lowercase    :int      	=						"CPUExecutionProvider"

         return ort.InferenceSession(_lowerCAmelCase       ,					providers=[provider]       ,					sess_options=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					Optional[str] = None       ,					**_lowerCAmelCase:					Any				):
         lowercase    :Optional[int]      	=						file_name if file_name is not None else ONNX_WEIGHTS_NAME

         lowercase    :Tuple      	=						self.model_save_dir.joinpath(self.latest_model_name				)
         lowercase    :Optional[int]      	=						Path(_lowerCAmelCase				).joinpath(_lowerCAmelCase				)
         try:
           shutil.copyfile(_lowerCAmelCase       ,					_lowerCAmelCase				)
         except shutil.SameFileError:
           pass

         # copy external weights (for models >2GB)
         lowercase    :Tuple      	=						self.model_save_dir.joinpath(_lowerCAmelCase				)
         if src_path.exists():
           lowercase    :Dict      	=						Path(_lowerCAmelCase				).joinpath(_lowerCAmelCase				)
           try:
             shutil.copyfile(_lowerCAmelCase       ,					_lowerCAmelCase				)
           except shutil.SameFileError:
             pass






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					Union[str, os.PathLike]       ,					**_lowerCAmelCase:					int       ,					):
         if os.path.isfile(_lowerCAmelCase				):
           logger.error(F"Provided path ({save_directory}) should be a directory, not a file"				)
           return

         os.makedirs(_lowerCAmelCase       ,					exist_ok=_lowerCAmelCase				)

         # saving model weights/files
         self._save_pretrained(_lowerCAmelCase       ,					**_lowerCAmelCase				)






       @classmethod
       def 					SCREAMING_SNAKE_CASE    (   cls:					Dict       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					Optional[Union[bool, str, None]] = None       ,					_lowerCAmelCase:					Optional[Union[str, None]] = None       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional["ort.SessionOptions"] = None       ,					**_lowerCAmelCase:					Optional[int]       ,					):
         lowercase    :List[str]      	=						file_name if file_name is not None else ONNX_WEIGHTS_NAME
         # load model from local directory
         if os.path.isdir(_lowerCAmelCase				):
           lowercase    :Union[str, Any]      	=						OnnxRuntimeModel.load_model(
               os.path.join(_lowerCAmelCase       ,					_lowerCAmelCase				)       ,					provider=_lowerCAmelCase       ,					sess_options=_lowerCAmelCase				)
           lowercase    :Optional[Any]      	=						Path(_lowerCAmelCase				)
         # load model from hub
         else:
           # download model
           lowercase    :str      	=						hf_hub_download(
               repo_id=_lowerCAmelCase       ,					filename=_lowerCAmelCase       ,					use_auth_token=_lowerCAmelCase       ,					revision=_lowerCAmelCase       ,					cache_dir=_lowerCAmelCase       ,					force_download=_lowerCAmelCase       ,					)
           lowercase    :Optional[int]      	=						Path(_lowerCAmelCase				).parent
           lowercase    :Tuple      	=						Path(_lowerCAmelCase				).name
           lowercase    :Tuple      	=						OnnxRuntimeModel.load_model(_lowerCAmelCase       ,					provider=_lowerCAmelCase       ,					sess_options=_lowerCAmelCase				)
         return cls(model=_lowerCAmelCase       ,					**_lowerCAmelCase				)






       @classmethod
       def 					SCREAMING_SNAKE_CASE    (   cls:					str       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					bool = True       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					**_lowerCAmelCase:					Any       ,					):
         lowercase    :List[str]      	=						None
         if len(str(_lowerCAmelCase				).split("@"				)				) == 2:
           lowercase	,    lowercase    :Tuple      	=						model_id.split("@"				)

         return cls._from_pretrained(
             model_id=_lowerCAmelCase       ,					revision=_lowerCAmelCase       ,					cache_dir=_lowerCAmelCase       ,					force_download=_lowerCAmelCase       ,					use_auth_token=_lowerCAmelCase       ,					**_lowerCAmelCase       ,					)



