



import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
      if is_torch_available():
            import torch
      if is_tf_available():
            import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_snake_case	=						{
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case	=						{
    "Salesforce/codegen-350M-mono": 2048,
}



class 						UpperCAmelCase_					( UpperCamelCase_):
       lowerCamelCase__         =	VOCAB_FILES_NAMES
       lowerCamelCase__         =	PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase__         =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase__         =	['input_ids', 'attention_mask']
       lowerCamelCase__         =	CodeGenTokenizer







       def __init__(     self,    __a=None,    __a=None,    __a=None,    __a="<|endoftext|>",    __a="<|endoftext|>",    __a="<|endoftext|>",    __a=False,    **__a,    ):

              '''simple docstring'''

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

              if kwargs.pop("add_bos_token",    __a):
                     _lowerCAmelCase  :							List[Any]				     =					kwargs.pop("name_or_path",    "")
                     raise ValueError(
                         "Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token."
                         "Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n"
                         f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
                         f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
                         "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
                         " so that the fast tokenizer works correctly.")

              _lowerCAmelCase  :							Optional[Any]				     =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
              if pre_tok_state.get("add_prefix_space",    __a) != add_prefix_space:
                     _lowerCAmelCase  :							Dict				     =					getattr(__a,    pre_tok_state.pop("type"))
                     _lowerCAmelCase  :							Dict				     =					add_prefix_space
                     _lowerCAmelCase  :							Any				     =					pre_tok_class(**__a)

              _lowerCAmelCase  :							Any				     =					add_prefix_space







       def      snake_case__   (     self,    *__a,    **__a):

              '''simple docstring'''

              _lowerCAmelCase  :							List[str]				     =					kwargs.get("is_split_into_words",    __a)
              assert self.add_prefix_space or not is_split_into_words, (
                  f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                  "to use it with pretokenized inputs."
              )

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







       def      snake_case__   (     self,    *__a,    **__a):

              '''simple docstring'''

              _lowerCAmelCase  :							Tuple				     =					kwargs.get("is_split_into_words",    __a)

              assert self.add_prefix_space or not is_split_into_words, (
                  f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                  "to use it with pretokenized inputs."
              )

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







       def      snake_case__   (     self,    __a,    __a = None):

              '''simple docstring'''

              _lowerCAmelCase  :							int				     =					self._tokenizer.model.save(__a,    name=__a)
              return tuple(__a)







       def      snake_case__   (     self,    __a,    __a = False,    __a = None,    __a = None,    **__a,    ):

              '''simple docstring'''

              _lowerCAmelCase  :							Dict				     =					super().decode(
                  token_ids=__a,    skip_special_tokens=__a,    clean_up_tokenization_spaces=__a,    **__a,    )

              if truncate_before_pattern is not None and len(__a) > 0:
                     _lowerCAmelCase  :							Tuple				     =					self.truncate(__a,    __a)

              return decoded_text







       def      snake_case__   (     self,    __a,    __a):

              '''simple docstring'''

              def find_re(__a,    __a,    __a):
                     _lowerCAmelCase  :							List[Any]				     =					pattern.search(__a,    __a)
                     return m.start() if m else -1

              _lowerCAmelCase  :							Union[str, Any]				     =					[re.compile(__a,    re.MULTILINE) for pattern in truncate_before_pattern]

              _lowerCAmelCase  :							List[str]				     =					list(re.finditer("^print",    __a,    re.MULTILINE))

              if len(__a) > 1:
                     _lowerCAmelCase  :							Tuple				     =					completion[: prints[1].start()]

              _lowerCAmelCase  :							Dict				     =					list(re.finditer("^def",    __a,    re.MULTILINE))

              if len(__a) > 1:
                     _lowerCAmelCase  :							Union[str, Any]				     =					completion[: defs[1].start()]

              _lowerCAmelCase  :							List[str]				     =					0

              _lowerCAmelCase  :							Optional[int]				     =					[
                  pos for pos in [find_re(__a,    __a,    __a) for terminal in terminals] if pos != -1
              ]

              if len(__a) > 0:
                     return completion[: min(__a)]
              else:
                     return completion





import baseaa






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							return baseaa.aaaencode(string.encode("utf-8"						)						)






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							return baseaa.aaadecode(_lowerCamelCase						).decode("utf-8"						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
