



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       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					VOCAB_FILES_NAMES
			UpperCamelCase        =					PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase        =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase        =					['''input_ids''', '''attention_mask''']
			UpperCamelCase        =					CodeGenTokenizer



			def __init__(						self       :  Optional[Any]			,		A       :  str=None			,		A       :  List[str]=None			,		A       :  Tuple=None			,		A       :  List[str]="<|endoftext|>"			,		A       :  Union[str, Any]="<|endoftext|>"			,		A       :  Union[str, Any]="<|endoftext|>"			,		A       :  List[str]=False			,		**A       :  List[Any]			,		)       ->   Optional[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):
									_UpperCAmelCase			=       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			=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
						if pre_tok_state.get('add_prefix_space'			,		A) != add_prefix_space:
									_UpperCAmelCase			=       getattr(A			,		pre_tok_state.pop('type'))
									_UpperCAmelCase			=       add_prefix_space
									_UpperCAmelCase			=       pre_tok_class(**A)

						_UpperCAmelCase			=       add_prefix_space



			def    _lowerCamelCase	(						self       :  str			,		*A       :  List[str]			,		**A       :  Dict)       ->   BatchEncoding:



						"""simple docstring"""






						_UpperCAmelCase			=       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       :  Optional[Any]			,		*A       :  List[str]			,		**A       :  List[str])       ->   BatchEncoding:



						"""simple docstring"""






						_UpperCAmelCase			=       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       :  str			,		A       :  str			,		A       :  Optional[str] = None)       ->   Tuple[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self._tokenizer.model.save(A			,		name=A)
						return tuple(A)



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



						"""simple docstring"""






						_UpperCAmelCase			=       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:
									_UpperCAmelCase			=       self.truncate(A			,		A)

						return decoded_text



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Optional[int]			,		A       :  Optional[int])       ->   int:



						"""simple docstring"""






						def find_re(A       :  Any			,		A       :  Optional[int]			,		A       :  Dict):
									_UpperCAmelCase			=       pattern.search(A			,		A)
									return m.start() if m else -1

						_UpperCAmelCase			=       [re.compile(A			,		re.MULTILINE) for pattern in truncate_before_pattern]

						_UpperCAmelCase			=       list(re.finditer('^print'			,		A			,		re.MULTILINE))

						if len(A) > 1:
									_UpperCAmelCase			=       completion[: prints[1].start()]

						_UpperCAmelCase			=       list(re.finditer('^def'			,		A			,		re.MULTILINE))

						if len(A) > 1:
									_UpperCAmelCase			=       completion[: defs[1].start()]

						_UpperCAmelCase			=       0

						_UpperCAmelCase			=       [
						    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





from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat





def 							A     (					)    ->			tuple[list[int], int]:







			'''simple docstring'''
			_UpperCAmelCase			=       [randint(-1_000							,       1_000   ) for i in range(10   )]
			_UpperCAmelCase			=       randint(-5_000							,       5_000   )
			return (arr, r)


UpperCAmelCase__       =	make_dataset()





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int   )    ->			tuple[int, ...]:







			'''simple docstring'''
			for triplet in permutations(_UpperCAmelCase							,       3   ):
						if sum(_UpperCAmelCase   ) == target:
									return tuple(sorted(_UpperCAmelCase   )   )
			return (0, 0, 0)





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int   )    ->			tuple[int, int, int]:







			'''simple docstring'''
			arr.sort()
			_UpperCAmelCase			=       len(_UpperCAmelCase   )
			for i in range(n - 1   ):
						_UpperCAmelCase	,					_UpperCAmelCase			=       i + 1, n - 1
						while left < right:
									if arr[i] + arr[left] + arr[right] == target:
												return (arr[i], arr[left], arr[right])
									elif arr[i] + arr[left] + arr[right] < target:
												left += 1
									elif arr[i] + arr[left] + arr[right] > target:
												right -= 1
			return (0, 0, 0)





def 							A     (					)    ->			tuple[float, float]:







			'''simple docstring'''
			_UpperCAmelCase			=       '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
			_UpperCAmelCase			=       '\ntriplet_sum1(*dataset)\n'
			_UpperCAmelCase			=       '\ntriplet_sum2(*dataset)\n'
			_UpperCAmelCase			=       repeat(setup=_UpperCAmelCase							,       stmt=_UpperCAmelCase							,       repeat=5							,       number=10_000   )
			_UpperCAmelCase			=       repeat(setup=_UpperCAmelCase							,       stmt=_UpperCAmelCase							,       repeat=5							,       number=10_000   )
			return (min(_UpperCAmelCase   ), min(_UpperCAmelCase   ))


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							UpperCAmelCase__       =	solution_times()
							print(f"""The time for naive implementation is {times[0]}.""")
							print(f"""The time for optimized implementation is {times[1]}.""")
