




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:
				SCREAMING_SNAKE_CASE__     :     Dict		    =       None


SCREAMING_SNAKE_CASE__     :     List[str]		    =       logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__     :     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
SCREAMING_SNAKE_CASE__     :     List[Any]		    =       {
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}



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

	A    							= []





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE="</s>"      ,     SCREAMING_SNAKE_CASE="<unk>"      ,     SCREAMING_SNAKE_CASE="<pad>"      ,     SCREAMING_SNAKE_CASE=100      ,     SCREAMING_SNAKE_CASE=None      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					List[str]:




				"""simple docstring"""



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

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





	@staticmethod
	def 						A_(							SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



				if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
							SCREAMING_SNAKE_CASE_								=						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.'      ,     SCREAMING_SNAKE_CASE      ,     )

				return max_model_length





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      )  ->					Tuple[str]:




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

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

				return (out_vocab_file,)





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      )  ->					List[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a
				else:
							SCREAMING_SNAKE_CASE_								=						token_ids_a + [self.eos_token_id]
							return self.prefix_tokens + token_ids_a + token_ids_a





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      )  ->					List[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[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      )  ->					Union[str, Any]:




				"""simple docstring"""



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





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



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






import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


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



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     *SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				warnings.warn(
				    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
				    ' Please use PerceiverImageProcessor instead.'      ,     SCREAMING_SNAKE_CASE      ,     )
				super().__init__(*SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )
