


"""simple docstring"""







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:
		a__				:							Any						=     None


a__				:							List[str]						=     logging.get_logger(__name__)

a__				:							int						=     {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a__				:							Tuple						=     {
    '''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
a__				:							int						=     {
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  List[Any]    					=			VOCAB_FILES_NAMES
	snake_case__				:  Dict    					=			PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:  Optional[Any]    					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:  List[str]    					=			["input_ids", "attention_mask"]
	snake_case__				:  Tuple    					=			TaTokenizer

	snake_case__				:  List[int]    					=			[]




	def __init__( self			:				Tuple			,			UpperCAmelCase__			:				Tuple=None			,			UpperCAmelCase__			:				int=None			,			UpperCAmelCase__			:				str="</s>"			,			UpperCAmelCase__			:				str="<unk>"			,			UpperCAmelCase__			:				str="<pad>"			,			UpperCAmelCase__			:				int=1_0_0			,			UpperCAmelCase__			:				Optional[int]=None			,			**UpperCAmelCase__			:				Tuple			,			)       ->	Optional[int]:
						# Add extra_ids to the special token list
						if extra_ids > 0 and additional_special_tokens is None:
											__SCREAMING_SNAKE_CASE						   =				[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
											__SCREAMING_SNAKE_CASE						   =				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__			,			)

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




	@staticmethod
	def   UpperCAmelCase_      ( UpperCAmelCase__			:				List[Any]			,			UpperCAmelCase__			:				Optional[int]			,			UpperCAmelCase__			:				Optional[Any]   )       ->	List[str]:
						if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
											__SCREAMING_SNAKE_CASE						   =				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   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Optional[str] = None   )       ->	Tuple[str]:
						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
						__SCREAMING_SNAKE_CASE						   =				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   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				List[int]			,			UpperCAmelCase__			:				Optional[List[int]] = None   )       ->	List[int]:
						__SCREAMING_SNAKE_CASE						   =				token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
											return self.prefix_tokens + token_ids_a
						else:
											__SCREAMING_SNAKE_CASE						   =				token_ids_a + [self.eos_token_id]
											return self.prefix_tokens + token_ids_a + token_ids_a




	def   UpperCAmelCase_      ( self			:				Union[str, Any]			,			UpperCAmelCase__			:				List[int]			,			UpperCAmelCase__			:				Optional[List[int]] = None   )       ->	List[int]:
						__SCREAMING_SNAKE_CASE						   =				[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   UpperCAmelCase_      ( self			:				int   )       ->	Union[str, Any]:
						return list(
						    set(filter(lambda UpperCAmelCase__   : bool(re.search(R"<extra_id_\d+>"			,			UpperCAmelCase__   )   ) is not None			,			self.additional_special_tokens   )   )   )




	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	Tuple:
						return [self.convert_tokens_to_ids(UpperCAmelCase__   ) for token in self.get_sentinel_tokens()]




"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				set(range(3  ,						lowerCAmelCase_  ,						2   )   )
					primes.add(2   )
					for p in range(3  ,						lowerCAmelCase_  ,						2   ):
										if p not in primes:
															continue
										primes.difference_update(set(range(p * p  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )   )   )

					__SCREAMING_SNAKE_CASE						   =				[float(lowerCAmelCase_   ) for n in range(limit + 1   )]

					for p in primes:
										for n in range(lowerCAmelCase_  ,						limit + 1  ,						lowerCAmelCase_   ):
															phi[n] *= 1 - 1 / p

					return int(sum(phi[2:]   )   )


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