



'''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:
						lowerCamelCase							:Optional[int]       = None


lowerCamelCase							:Union[str, Any]       = logging.get_logger(__name__)

lowerCamelCase							:Optional[int]       = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase							:List[Any]       = {
    '''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
lowerCamelCase							: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      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE       :		List[str]		      = PRETRAINED_VOCAB_FILES_MAP
	__SCREAMING_SNAKE_CASE       :		Dict		      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__SCREAMING_SNAKE_CASE       :		Dict		      = ['input_ids', 'attention_mask']
	__SCREAMING_SNAKE_CASE       :		int		      = TaTokenizer

	__SCREAMING_SNAKE_CASE       :		List[int]		      = []


	def __init__(self	,				lowercase=None	,				lowercase=None	,				lowercase="</s>"	,				lowercase="<unk>"	,				lowercase="<pad>"	,				lowercase=100	,				lowercase=None	,				**lowercase	,				):
				# Add extra_ids to the special token list
				if extra_ids > 0 and additional_special_tokens is None:
							A_     :						Dict     =					[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_     :						Optional[int]     =					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_     :						Any     =					vocab_file
				A_     :						List[Any]     =					False if not self.vocab_file else True
				A_     :						Any     =					extra_ids


	@staticmethod
	def    _a     (lowercase	,				lowercase	,				lowercase							):
				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    _a     (self	,				lowercase	,				lowercase = 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(lowercase							):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
							return
				A_     :						Dict     =					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    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Tuple     =					token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a
				else:
							A_     :						Any     =					token_ids_a + [self.eos_token_id]
							return self.prefix_tokens + token_ids_a + token_ids_a


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						int     =					[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 lowercase							: bool(re.search(R"""<extra_id_\d+>"""	,				lowercase							)							) is not None	,				self.additional_special_tokens							)							)							)


	def    _a     (self							):
				return [self.convert_tokens_to_ids(lowercase							) for token in self.get_sentinel_tokens()]




'''simple docstring'''




from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase							:Dict       = {'''configuration_mmbt''': ['''MMBTConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase							:str       = ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
						from .configuration_mmbt import MMBTConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
						import sys

						lowerCamelCase							:Tuple       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)