



"""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:
	lowercase__			   =  None


lowercase__			   =  logging.get_logger(__name__)

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

lowercase__			   =  {
    "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__			   =  {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}




class SCREAMING_SNAKE_CASE__					(		lowercase__      ):
						_lowerCAmelCase							=					VOCAB_FILES_NAMES
						_lowerCAmelCase							=					PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase							=					['input_ids', 'attention_mask']
						_lowerCAmelCase							=					TaTokenizer

						_lowerCAmelCase							=					[]



						def __init__(self						,     _lowercase=None						,     _lowercase=None						,     _lowercase="</s>"						,     _lowercase="<unk>"						,     _lowercase="<pad>"						,     _lowercase=100						,     _lowercase=None						,     **_lowercase						,     ):





													'''simple docstring'''

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

													__a		:		Dict									=       vocab_file
													__a		:		Tuple									=       False if not self.vocab_file else True
													__a		:		Tuple									=       extra_ids



						@staticmethod
						def     lowerCAmelCase__(_lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																				__a		:		Optional[Any]									=       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."""						,     _lowercase						,     )

													return max_model_length



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = 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(_lowercase							):
																				logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
																				return
													__a		:		str									=       os.path.join(
													    _lowercase						,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

													return (out_vocab_file,)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None							):





													'''simple docstring'''

													__a		:		Tuple									=       token_ids_a + [self.eos_token_id]
													if token_ids_a is None:
																				return self.prefix_tokens + token_ids_a
													else:
																				__a		:		Dict									=       token_ids_a + [self.eos_token_id]
																				return self.prefix_tokens + token_ids_a + token_ids_a



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None							):





													'''simple docstring'''

													__a		:		List[str]									=       [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     lowerCAmelCase__(self							):





													'''simple docstring'''

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





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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








"""simple docstring"""

def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float(moles / volume					) * nfactor					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((moles * 0.08_21 * temperature) / (volume)					)					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((moles * 0.08_21 * temperature) / (pressure)					)					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((pressure * volume) / (0.08_21 * moles)					)					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

