





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:
		lowercase_		:				int				=						None


lowercase_		:				List[Any]				=						logging.get_logger(__name__)

lowercase_		:				Any				=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowercase_		:				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
lowercase_		:				Dict				=						{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}


class 			lowercase				(							__UpperCAmelCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	Union[str, Any]								=	VOCAB_FILES_NAMES
						_UpperCamelCase  :	Optional[int]								=	PRETRAINED_VOCAB_FILES_MAP
						_UpperCamelCase  :	Union[str, Any]								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCamelCase  :	int								=	["input_ids", "attention_mask"]
						_UpperCamelCase  :	List[str]								=	TaTokenizer

						_UpperCamelCase  :	str								=	[]







						def __init__(    self			:		str ,				lowerCamelCase_			:		Any=None ,				lowerCamelCase_			:		List[Any]=None ,				lowerCamelCase_			:		str="</s>" ,				lowerCamelCase_			:		List[str]="<unk>" ,				lowerCamelCase_			:		int="<pad>" ,				lowerCamelCase_			:		Optional[int]=1_00 ,				lowerCamelCase_			:		Any=None ,				**lowerCamelCase_			:		Any ,				):





										'''simple docstring'''







										if extra_ids > 0 and additional_special_tokens is None:
														_snake_case :			Union[str, Any]  						=   [f'''<extra_id_{i}>''' for i in range(lowerCAmelCase_					)]
										elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra special tokens
														_snake_case :			List[Any]  						=   len(set(filter(lambda lowerCamelCase_					: bool('extra_id_' in str(lowerCAmelCase_					)					) ,				lowerCAmelCase_					)					)					)
														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__(
										    lowerCAmelCase_ ,				tokenizer_file=lowerCAmelCase_ ,				eos_token=lowerCAmelCase_ ,				unk_token=lowerCAmelCase_ ,				pad_token=lowerCAmelCase_ ,				extra_ids=lowerCAmelCase_ ,				additional_special_tokens=lowerCAmelCase_ ,				**lowerCAmelCase_ ,				)

										_snake_case :			str  						=   vocab_file
										_snake_case :			int  						=   False if not self.vocab_file else True
										_snake_case :			int  						=   extra_ids







						@staticmethod
						def 		__UpperCAmelCase       (    lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		List[str]					):





										'''simple docstring'''







										if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
														_snake_case :			int  						=   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.' ,				lowerCAmelCase_ ,				)

										return max_model_length







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		Optional[str] = None					):





										'''simple docstring'''







										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(lowerCAmelCase_					):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
														return
										_snake_case :			Tuple  						=   os.path.join(
										    lowerCAmelCase_ ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

										if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase_					):
														copyfile(self.vocab_file ,				lowerCAmelCase_					)
														logger.info(f'''Copy vocab file to {out_vocab_file}'''					)

										return (out_vocab_file,)







						def 		__UpperCAmelCase       (    self			:		int ,				lowerCamelCase_			:		List[int] ,				lowerCamelCase_			:		Optional[List[int]] = None					):





										'''simple docstring'''







										_snake_case :			str  						=   token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
														return self.prefix_tokens + token_ids_a
										else:
														_snake_case :			int  						=   token_ids_a + [self.eos_token_id]
														return self.prefix_tokens + token_ids_a + token_ids_a







						def 		__UpperCAmelCase       (    self			:		Tuple ,				lowerCamelCase_			:		List[int] ,				lowerCamelCase_			:		Optional[List[int]] = None					):





										'''simple docstring'''







										_snake_case :			Dict  						=   [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			:		Any					):





										'''simple docstring'''







										return list(
										    set(filter(lambda lowerCamelCase_					: bool(re.search(R'<extra_id_\d+>' ,				lowerCAmelCase_					)					) is not None ,				self.additional_special_tokens					)					)					)







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										return [self.convert_tokens_to_ids(lowerCAmelCase_					) for token in self.get_sentinel_tokens()]













from __future__ import annotations

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Dict  						=   list(range(len(__lowerCAmelCase ) ) )
				_snake_case :			Optional[int]  						=   [v / w for v, w in zip(__lowerCAmelCase	,			__lowerCAmelCase )]
				index.sort(key=lambda __lowerCAmelCase : ratio[i]	,			reverse=__lowerCAmelCase )

				_snake_case :			float  						=   0
				_snake_case :			list[float]  						=   [0] * len(__lowerCAmelCase )
				for i in index:
								if weight[i] <= capacity:
												_snake_case :			List[Any]  						=   1
												max_value += value[i]
												capacity -= weight[i]
								else:
												_snake_case :			Any  						=   capacity / weight[i]
												max_value += value[i] * capacity / weight[i]
												break

				return max_value, fractions


if __name__ == "__main__":
		import doctest

		doctest.testmod()






