






'''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': 2048,
}


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   Union[str, Any]		       =    VOCAB_FILES_NAMES
    __lowerCamelCase:   Any		       =    PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase:   List[str]		       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase:   Tuple		       =    ['input_ids', 'attention_mask']
    __lowerCamelCase:   Tuple		       =    CodeGenTokenizer




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






       '''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			):
          lowercase_	:    int           =     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."			)

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

       lowercase_	:    int           =     add_prefix_space




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     *a  :       List[str]  ,     **a  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     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 				lowerCAmelCase__				(			self  :       Dict  ,     *a  :       Optional[Any]  ,     **a  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    Dict           =     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 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       str  ,     a  :       Optional[str] = None			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     self._tokenizer.model.save(a  ,     name=a			)
       return tuple(a			)




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     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]  ,     ):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     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:
          lowercase_	:    Optional[Any]           =     self.truncate(a  ,     a			)

       return decoded_text





    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       str  ,     a  :       List[Any]			):






       '''simple docstring'''






       def find_re(a  :       Tuple  ,     a  :       List[str]  ,     a  :       Optional[int]			):
          lowercase_	:    List[Any]           =     pattern.search(a  ,     a			)
          return m.start() if m else -1

       lowercase_	:    Tuple           =     [re.compile(a  ,     re.MULTILINE			) for pattern in truncate_before_pattern]

       lowercase_	:    int           =     list(re.finditer("^print"  ,     a  ,     re.MULTILINE			)			)

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

       lowercase_	:    Union[str, Any]           =     list(re.finditer("^def"  ,     a  ,     re.MULTILINE			)			)

       if len(a			) > 1:
          lowercase_	:    Optional[Any]           =     completion[: defs[1].start()]

       lowercase_	:    Optional[int]           =     0

       lowercase_	:    Union[str, Any]           =     [
           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'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase = 200							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     [1, 2, 5, 10, 20, 50, 100, 200]
   lowercase_	:    str           =     [0] * (pence + 1)
   lowercase_	:    Dict           =     1  # base case: 1 way to make 0 pence

   for coin in coins:
      for i in range(_UpperCamelCase						,		pence + 1						,		1							):
         number_of_ways[i] += number_of_ways[i - coin]
   return number_of_ways[pence]


if __name__ == "__main__":
 assert solution(200) == 73682




