




"""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  :			int        =       None


_lowerCamelCase  :			Optional[Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Tuple        =       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase  :			int        =       {
    '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': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Dict         =   VOCAB_FILES_NAMES
				__lowerCAmelCase    :    int         =   PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase    :    List[Any]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase    :    str         =   ["""input_ids""", """attention_mask"""]
				__lowerCAmelCase    :    str         =   TaTokenizer

				__lowerCAmelCase    :    List[int]         =   []







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



					"""simple docstring"""

					if extra_ids > 0 and additional_special_tokens is None:
						A_  :				List[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
						A_  :				str	   =		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      ,     )

					A_  :				str	   =		vocab_file
					A_  :				List[str]	   =		False if not self.vocab_file else True
					A_  :				str	   =		extra_ids







				@staticmethod
				def 					a_  (							_lowerCamelCase   :					str      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					Dict       ):



					"""simple docstring"""

					if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
						A_  :				List[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.'''      ,     _lowerCamelCase      ,     )

					return max_model_length







				def 					a_  (							self   :					List[str]      ,     _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
					A_  :				Dict	   =		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 					a_  (							self   :					str      ,     _lowerCamelCase   :					List[int]      ,     _lowerCamelCase   :					Optional[List[int]] = None       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
						return self.prefix_tokens + token_ids_a
					else:
						A_  :				str	   =		token_ids_a + [self.eos_token_id]
						return self.prefix_tokens + token_ids_a + token_ids_a







				def 					a_  (							self   :					Union[str, Any]      ,     _lowerCamelCase   :					List[int]      ,     _lowerCamelCase   :					Optional[List[int]] = None       ):



					"""simple docstring"""

					A_  :				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 					a_  (							self   :					Optional[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 					a_  (							self   :					str       ):



					"""simple docstring"""

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











"""simple docstring"""

from typing import TYPE_CHECKING

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


_lowerCamelCase  :			Optional[Any]        =       {
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase  :			int        =       ['Mask2FormerImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase  :			Tuple        =       [
		    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Mask2FormerForUniversalSegmentation',
		    'Mask2FormerModel',
		    'Mask2FormerPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_maskaformer import MaskaFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_maskaformer import (
				    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaskaFormerForUniversalSegmentation,
				    MaskaFormerModel,
				    MaskaFormerPreTrainedModel,
				)


else:
		import sys

		_lowerCamelCase  :			Optional[Any]        =       _LazyModule(__name__, globals()['__file__'], _import_structure)





