



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__		:					Tuple							=None


lowerCAmelCase__		:					Tuple							=logging.get_logger(__name__)

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

lowerCAmelCase__		:					Union[str, 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''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			List[Any]      =  VOCAB_FILES_NAMES
						UpperCamelCase__      :			List[str]      =  PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase__      :			Dict      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase__      :			Any      =  ['''input_ids''', '''attention_mask''']
						UpperCamelCase__      :			str      =  TaTokenizer

						UpperCamelCase__      :			List[int]      =  []






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







													'''simple docstring'''

													if extra_ids > 0 and additional_special_tokens is None:
																				__SCREAMING_SNAKE_CASE									= [f"""<extra_id_{i}>""" for i in range(_A		)]
													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 _A		: bool('extra_id_' in str(_A		)		)				,							_A		)		)		)
																				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__(
													    _A				,							tokenizer_file=_A				,							eos_token=_A				,							unk_token=_A				,							pad_token=_A				,							extra_ids=_A				,							additional_special_tokens=_A				,							**_A				,							)

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






						@staticmethod
						def 	_A     (   _A				,							_A				,							_A		):







													'''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.'				,							_A				,							)

													return max_model_length






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

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

													return (out_vocab_file,)






						def 	_A     (   self				,							_A				,							_A = None		):







													'''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				,							_A				,							_A = None		):







													'''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		):







													'''simple docstring'''

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






						def 	_A     (   self		):







													'''simple docstring'''

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











from ..utils import DummyObject, requires_backends

class        UpperCAmelCase_  (       metaclass=UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  ['''speech''']






						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													requires_backends(self				,							['speech']		)







class        UpperCAmelCase_  (       metaclass=UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  ['''speech''']






						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													requires_backends(self				,							['speech']		)






