

'''simple docstring'''
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


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

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

lowerCamelCase			:				Dict 			=					{
    "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"
        ),
    },
}

lowerCamelCase			:				Union[str, Any] 			=					{
    "Salesforce/codegen-350M-mono": 2_0_4_8,
}







class 							A__   (	A__  ):
  A__					=    VOCAB_FILES_NAMES
  A__					=    PRETRAINED_VOCAB_FILES_MAP
  A__					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__					=    ['input_ids', 'attention_mask']
  A__					=    CodeGenTokenizer





  def __init__(		self				:     str							, _a				:     List[Any]=None							, _a				:     Tuple=None							, _a				:     List[Any]=None							, _a				:     Tuple="<|endoftext|>"							, _a				:     Any="<|endoftext|>"							, _a				:     int="<|endoftext|>"							, _a				:     str=False							, **_a				:     int							, )     ->   Dict:





       '''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			):
            _SCREAMING_SNAKE_CASE							   =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.'			)

       _SCREAMING_SNAKE_CASE							   =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
       if pre_tok_state.get('add_prefix_space'							, _a			) != add_prefix_space:
            _SCREAMING_SNAKE_CASE							   =getattr(_a							, pre_tok_state.pop('type'			)			)
            _SCREAMING_SNAKE_CASE							   =add_prefix_space
            _SCREAMING_SNAKE_CASE							   =pre_tok_class(**_a			)

       _SCREAMING_SNAKE_CASE							   =add_prefix_space





  def 						A  (		self				:     int							, *_a				:     List[Any]							, **_a				:     Union[str, Any]			)     ->   BatchEncoding:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =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 						A  (		self				:     Optional[Any]							, *_a				:     Tuple							, **_a				:     Optional[Any]			)     ->   BatchEncoding:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =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 						A  (		self				:     int							, _a				:     str							, _a				:     Optional[str] = None			)     ->   Tuple[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self._tokenizer.model.save(_a							, name=_a			)
       return tuple(_a			)





  def 						A  (		self				:     List[str]							, _a				:     Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]							, _a				:     bool = False							, _a				:     bool = None							, _a				:     Optional[List[str]] = None							, **_a				:     Optional[Any]							, )     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =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:
            _SCREAMING_SNAKE_CASE							   =self.truncate(_a							, _a			)

       return decoded_text





  def 						A  (		self				:     Optional[int]							, _a				:     List[str]							, _a				:     str			)     ->   Optional[Any]:





       '''simple docstring'''






       def find_re(_a				:     Union[str, Any]							, _a				:     List[Any]							, _a				:     Dict			):
            _SCREAMING_SNAKE_CASE							   =pattern.search(_a							, _a			)
            return m.start() if m else -1

       _SCREAMING_SNAKE_CASE							   =[re.compile(_a							, re.MULTILINE			) for pattern in truncate_before_pattern]

       _SCREAMING_SNAKE_CASE							   =list(re.finditer('^print'							, _a							, re.MULTILINE			)			)

       if len(_a			) > 1:
            _SCREAMING_SNAKE_CASE							   =completion[: prints[1].start()]

       _SCREAMING_SNAKE_CASE							   =list(re.finditer('^def'							, _a							, re.MULTILINE			)			)

       if len(_a			) > 1:
            _SCREAMING_SNAKE_CASE							   =completion[: defs[1].start()]

       _SCREAMING_SNAKE_CASE							   =0

       _SCREAMING_SNAKE_CASE							   =[
           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



'''simple docstring'''
from ..utils import DummyObject, requires_backends







class 							A__   (	metaclass=A__  ):
  A__					=    ['note_seq']





  def __init__(		self				:     List[str]							, *_a				:     Any							, **_a				:     Dict			)     ->   Optional[Any]:





       '''simple docstring'''






       requires_backends(self							, ['note_seq']			)





  @classmethod
  def 						A  (		cls				:     Any							, *_a				:     str							, **_a				:     List[Any]			)     ->   List[str]:





       '''simple docstring'''






       requires_backends(cls							, ['note_seq']			)





  @classmethod
  def 						A  (		cls				:     int							, *_a				:     Optional[Any]							, **_a				:     Optional[int]			)     ->   Tuple:





       '''simple docstring'''






       requires_backends(cls							, ['note_seq']			)
