'''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


__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": 2_0_4_8,
}




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Union[str, Any]							=VOCAB_FILES_NAMES
     lowerCamelCase  :					Union[str, Any]							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase  :					Tuple							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase  :					str							=["input_ids", "attention_mask"]
     lowerCamelCase  :					Any							=CodeGenTokenizer






     def __init__(			self		:		Optional[int]  ,    a		:		str=None  ,    a		:		int=None  ,    a		:		Optional[Any]=None  ,    a		:		Optional[Any]="<|endoftext|>"  ,    a		:		Optional[Any]="<|endoftext|>"  ,    a		:		int="<|endoftext|>"  ,    a		:		Optional[int]=False  ,    **a		:		Optional[int]  ,    ):





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

         __lowerCamelCase          =  add_prefix_space






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    *a		:		Dict  ,    **a		:		Any   ):





         """simple docstring"""



         __lowerCamelCase          =  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        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    *a		:		str  ,    **a		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  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        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		str  ,    a		:		Optional[str] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  self._tokenizer.model.save(a  ,    name=a   )
         return tuple(a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]  ,    a		:		Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]  ,    a		:		bool = False  ,    a		:		bool = None  ,    a		:		Optional[List[str]] = None  ,    **a		:		Tuple  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  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          =  self.truncate(a  ,    a   )

         return decoded_text






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		List[str]  ,    a		:		int   ):





         """simple docstring"""



         def find_re(a		:		int  ,    a		:		List[Any]  ,    a		:		Dict   ):
             __lowerCamelCase          =  pattern.search(a  ,    a   )
             return m.start() if m else -1

         __lowerCamelCase          =  [re.compile(a  ,    re.MULTILINE   ) for pattern in truncate_before_pattern]

         __lowerCamelCase          =  list(re.finditer('''^print'''  ,    a  ,    re.MULTILINE   )   )

         if len(a   ) > 1:
             __lowerCamelCase          =  completion[: prints[1].start()]

         __lowerCamelCase          =  list(re.finditer('''^def'''  ,    a  ,    re.MULTILINE   )   )

         if len(a   ) > 1:
             __lowerCamelCase          =  completion[: defs[1].start()]

         __lowerCamelCase          =  0

         __lowerCamelCase          =  [
             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 bisect import bisect
from itertools import accumulate






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  sorted(zip(UpperCamelCase__    ,  UpperCamelCase__    )    ,  key=lambda UpperCamelCase__    : x[0] / x[1]    ,  reverse=UpperCamelCase__    )
    __lowerCamelCase     ,  __lowerCamelCase          =  [i[0] for i in r], [i[1] for i in r]
    __lowerCamelCase          =  list(accumulate(UpperCamelCase__    )    )
    __lowerCamelCase          =  bisect(UpperCamelCase__    ,  UpperCamelCase__    )
    return (
        0
        if k == 0
        else sum(vl[:k]    ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
        if k != n
        else sum(vl[:k]    )
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
