






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


a_		:						str             =    logging.get_logger(__name__)

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

a_		:						int             =    {
    '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'
        ),
    },
}

a_		:						Any             =    {
    'Salesforce/codegen-350M-mono': 20_48,
}

class 		_snake_case  (  A__					):
			_lowercase		:    Optional[Any]			     =							VOCAB_FILES_NAMES
			_lowercase		:    str			     =							PRETRAINED_VOCAB_FILES_MAP
			_lowercase		:    Dict			     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_lowercase		:    int			     =							['''input_ids''', '''attention_mask''']
			_lowercase		:    List[str]			     =							CodeGenTokenizer






			def __init__(       self		,					a=None		,					a=None		,					a=None		,					a="<|endoftext|>"		,					a="<|endoftext|>"		,					a="<|endoftext|>"		,					a=False		,					**a		,					)      ->       int:
						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    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       BatchEncoding:
						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    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       BatchEncoding:
						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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None)      ->       Tuple[str]:
						SCREAMING_SNAKE_CASE						= self._tokenizer.model.save(a		,					name=a)
						return tuple(a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = False		,					a = None		,					a = None		,					**a		,					)      ->       str:
						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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Optional[Any]:
						def find_re(a		,					a		,					a):
									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












import os

# Precomputes a list of the 100 first triangular numbers
a_		:						str             =    [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= os.path.dirname(os.path.realpath(_UpperCAmelCase))
			SCREAMING_SNAKE_CASE						= os.path.join(_UpperCAmelCase							,       'words.txt')

			SCREAMING_SNAKE_CASE						= ''
			with open(_UpperCAmelCase) as f:
						SCREAMING_SNAKE_CASE						= f.readline()

			SCREAMING_SNAKE_CASE						= [word.strip('"') for word in words.strip('\r\n').split(',')]
			SCREAMING_SNAKE_CASE						= [
			    word
			    for word in [sum(ord(_UpperCAmelCase) - 64 for x in word) for word in words]
			    if word in TRIANGULAR_NUMBERS
			]
			return len(_UpperCAmelCase)


if __name__ == "__main__":
				print(solution())




