





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


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_     							= {
    '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'
        ),
    },
}

UpperCAmelCase_     							= {
    'Salesforce/codegen-350M-mono': 2048,
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		VOCAB_FILES_NAMES
   a						:							Dict   		=		PRETRAINED_VOCAB_FILES_MAP
   a						:							Optional[int]   		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a						:							str   		=		["input_ids", "attention_mask"]
   a						:							Optional[Any]   		=		CodeGenTokenizer




   def __init__(    self,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__="<|endoftext|>",		__magic_name__="<|endoftext|>",		__magic_name__="<|endoftext|>",		__magic_name__=False,		**__magic_name__,		) ->      List[str]:






       """simple docstring"""
       super().__init__(
           __magic_name__,		__magic_name__,		tokenizer_file=__magic_name__,		unk_token=__magic_name__,		bos_token=__magic_name__,		eos_token=__magic_name__,		add_prefix_space=__magic_name__,		**__magic_name__,		)

       if kwargs.pop('''add_bos_token''',		__magic_name__							):
           UpperCamelCase__							:				Dict          =      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.'''							)

       UpperCamelCase__							:				Optional[int]          =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
       if pre_tok_state.get('''add_prefix_space''',		__magic_name__							) != add_prefix_space:
           UpperCamelCase__							:				List[str]          =      getattr(__magic_name__,		pre_tok_state.pop('''type'''							)							)
           UpperCamelCase__							:				Union[str, Any]          =      add_prefix_space
           UpperCamelCase__							:				int          =      pre_tok_class(**__magic_name__							)

       UpperCamelCase__							:				Dict          =      add_prefix_space




   def       UpperCamelCase__  (    self,		*__magic_name__,		**__magic_name__							) ->      BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      kwargs.get('''is_split_into_words''',		__magic_name__							)
       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(*__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		*__magic_name__,		**__magic_name__							) ->      BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      kwargs.get('''is_split_into_words''',		__magic_name__							)

       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(*__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None							) ->      Tuple[str]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self._tokenizer.model.save(__magic_name__,		name=__magic_name__							)
       return tuple(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = False,		__magic_name__ = None,		__magic_name__ = None,		**__magic_name__,		) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      super().decode(
           token_ids=__magic_name__,		skip_special_tokens=__magic_name__,		clean_up_tokenization_spaces=__magic_name__,		**__magic_name__,		)

       if truncate_before_pattern is not None and len(__magic_name__							) > 0:
           UpperCamelCase__							:				Any          =      self.truncate(__magic_name__,		__magic_name__							)

       return decoded_text




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__							) ->      Any:






       """simple docstring"""
       def find_re(__magic_name__,		__magic_name__,		__magic_name__							):
           UpperCamelCase__							:				Optional[Any]          =      pattern.search(__magic_name__,		__magic_name__							)
           return m.start() if m else -1

       UpperCamelCase__							:				Any          =      [re.compile(__magic_name__,		re.MULTILINE							) for pattern in truncate_before_pattern]

       UpperCamelCase__							:				Dict          =      list(re.finditer('''^print''',		__magic_name__,		re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:				List[str]          =      completion[: prints[1].start()]

       UpperCamelCase__							:				Optional[Any]          =      list(re.finditer('''^def''',		__magic_name__,		re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:				Tuple          =      completion[: defs[1].start()]

       UpperCamelCase__							:				List[Any]          =      0

       UpperCamelCase__							:				int          =      [
           pos for pos in [find_re(__magic_name__,		__magic_name__,		__magic_name__							) for terminal in terminals] if pos != -1
       ]

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








def 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	int:
    if not isinstance(__UpperCAmelCase		,   __UpperCAmelCase      ) or number < 0:
        raise ValueError('''Input must be a non-negative integer'''      )

    UpperCamelCase__							:				Optional[Any]          =      0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()

