
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   =     None


_A   =     logging.get_logger(__name__)

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

_A   =     {
    "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   =     {
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}





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

				_lowercase					=[]




				def __init__(					self				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase="</s>"				,				_UpperCamelCase="<unk>"				,				_UpperCamelCase="<pad>"				,				_UpperCamelCase=100				,				_UpperCamelCase=None				,				**_UpperCamelCase				,				)				->      int:
							# Add extra_ids to the special token list
							if extra_ids > 0 and additional_special_tokens is None:
										lowerCAmelCase_						=	[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
										lowerCAmelCase_						=	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				,				)

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




				@staticmethod
				def      __a						(					_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      int:
							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
										lowerCAmelCase_						=	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     )				->      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
							lowerCAmelCase_						=	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     )				->      List[int]:
							lowerCAmelCase_						=	token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
										return self.prefix_tokens + token_ids_a
							else:
										lowerCAmelCase_						=	token_ids_a + [self.eos_token_id]
										return self.prefix_tokens + token_ids_a + token_ids_a




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase = None     )				->      List[int]:
							lowerCAmelCase_						=	[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     )				->      Tuple:
							return list(
							    set(filter(lambda _UpperCamelCase     : bool(re.search(r"<extra_id_\d+>"				,				_UpperCamelCase     )     ) is not None				,				self.additional_special_tokens     )     )     )




				def      __a						(					self     )				->      Union[str, Any]:
							return [self.convert_tokens_to_ids(_UpperCamelCase     ) for token in self.get_sentinel_tokens()]





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A   =     {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["NllbTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["NllbTokenizerFast"]


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb import NllbTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb_fast import NllbTokenizerFast

else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



