



import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_ta import TaTokenizer
else:
						UpperCAmelCase_							:      List[Any]      =  None


UpperCAmelCase_							:      int      =  logging.get_logger(__name__)

UpperCAmelCase_							:      Tuple      =  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_							:      int      =  {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_							:      Dict      =  {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   str				=	VOCAB_FILES_NAMES
	lowerCAmelCase	:   Optional[int]				=	PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase	:   Optional[Any]				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase	:   Tuple				=	["""input_ids""", """attention_mask"""]
	lowerCAmelCase	:   Optional[int]				=	TaTokenizer

	lowerCAmelCase	:   List[int]				=	[]



	def __init__(       self			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	UpperCAmelCase__="</s>"			,	UpperCAmelCase__="<unk>"			,	UpperCAmelCase__="<pad>"			,	UpperCAmelCase__=100			,	UpperCAmelCase__=None			,	**UpperCAmelCase__			,	):
							# Add extra_ids to the special token list
							if extra_ids > 0 and additional_special_tokens is None:
													A__							=       [F"""<extra_id_{i}>""" for i in range(UpperCAmelCase__		)]
							elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra special tokens
													A__							=       len(set(filter(lambda UpperCAmelCase__		: bool("extra_id_" in str(UpperCAmelCase__		)		)			,	UpperCAmelCase__		)		)		)
													if extra_tokens != extra_ids:
																			raise ValueError(
																			    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																			    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
																			    " tokens"		)

							super().__init__(
							    UpperCAmelCase__			,	tokenizer_file=UpperCAmelCase__			,	eos_token=UpperCAmelCase__			,	unk_token=UpperCAmelCase__			,	pad_token=UpperCAmelCase__			,	extra_ids=UpperCAmelCase__			,	additional_special_tokens=UpperCAmelCase__			,	**UpperCAmelCase__			,	)

							A__							=       vocab_file
							A__							=       False if not self.vocab_file else True
							A__							=       extra_ids



	@staticmethod
	def 			__A							(       UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
													A__							=       TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
													if init_max_model_length is not None and init_max_model_length != max_model_length:
																			return init_max_model_length
													elif init_max_model_length is None:
																			warnings.warn(
																			    "This tokenizer was incorrectly instantiated with a model max length of"
																			    F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																			    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
																			    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
																			    F""" {pretrained_model_name_or_path} automatically truncating your input to"""
																			    F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																			    F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																			    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
																			    " instantiate this tokenizer with `model_max_length` set to your preferred value."			,	UpperCAmelCase__			,	)

							return max_model_length



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
													    "tokenizer."		)

							if not os.path.isdir(UpperCAmelCase__		):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
													return
							A__							=       os.path.join(
							    UpperCAmelCase__			,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

							if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCAmelCase__		):
													copyfile(self.vocab_file			,	UpperCAmelCase__		)
													logger.info(F"""Copy vocab file to {out_vocab_file}"""		)

							return (out_vocab_file,)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							A__							=       token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a
							else:
													A__							=       token_ids_a + [self.eos_token_id]
													return self.prefix_tokens + token_ids_a + token_ids_a



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							A__							=       [self.eos_token_id]

							if token_ids_a is None:
													return len(token_ids_a + eos		) * [0]
							return len(token_ids_a + eos + token_ids_a + eos		) * [0]



	def 			__A							(       self		):
							return list(
							    set(filter(lambda UpperCAmelCase__		: bool(re.search(R"<extra_id_\d+>"			,	UpperCAmelCase__		)		) is not None			,	self.additional_special_tokens		)		)		)



	def 			__A							(       self		):
							return [self.convert_tokens_to_ids(UpperCAmelCase__		) for token in self.get_sentinel_tokens()]










import math
from collections.abc import Iterator
from itertools import takewhile
def  UpperCamelCase						(       _A			:				int    )->       bool:





						"""simple docstring"""





						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5			,							int(math.sqrt(_A    ) + 1    )			,							6    ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True
def  UpperCamelCase						(       )->       Iterator[int]:





						"""simple docstring"""





						A__							=       2
						while True:
												if is_prime(_A    ):
																		yield num
												num += 1
def  UpperCamelCase						(       _A			:				int = 2000000    )->       int:





						"""simple docstring"""





						return sum(takewhile(lambda _A    : x < n			,							prime_generator()    )    )


if __name__ == "__main__":
						print(F'''{solution() = }''')





