
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_						     =					None


lowerCamelCase_						     =					logging.get_logger(__name__)

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

lowerCamelCase_						     =					{
    '''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_						     =					{
    '''t5-small''': 5_12,
    '''t5-base''': 5_12,
    '''t5-large''': 5_12,
    '''t5-3b''': 5_12,
    '''t5-11b''': 5_12,
}




class      __A(    a_			):

		"""simple docstring"""
		SCREAMING_SNAKE_CASE__         =      VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE__         =      PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE__         =      ['''input_ids''', '''attention_mask''']
		SCREAMING_SNAKE_CASE__         =      TaTokenizer

		SCREAMING_SNAKE_CASE__         =      []







		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_=1_00	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_	,      ):

							if extra_ids > 0 and additional_special_tokens is None:
												UpperCamelCase__				=    [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
												UpperCamelCase__				=    len(set(filter(lambda SCREAMING_SNAKE_CASE_						: 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_	,      )

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







		@staticmethod
		def 	UpperCAmelCase_ (SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):

							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
												UpperCamelCase__				=    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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = 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
							UpperCamelCase__				=    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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None						):
							UpperCamelCase__				=    token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
												return self.prefix_tokens + token_ids_a
							else:
												UpperCamelCase__				=    token_ids_a + [self.eos_token_id]
												return self.prefix_tokens + token_ids_a + token_ids_a







		def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None						):
							UpperCamelCase__				=    [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 	UpperCAmelCase_ (self						):
							return list(
							    set(filter(lambda SCREAMING_SNAKE_CASE_						: bool(re.search(r"""<extra_id_\d+>"""	,      lowercase_						)						) is not None	,      self.additional_special_tokens						)						)						)







		def 	UpperCAmelCase_ (self						):
							return [self.convert_tokens_to_ids(lowercase_						) for token in self.get_sentinel_tokens()]







from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase_						     =					{'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}


if TYPE_CHECKING:
    from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
    import sys

    lowerCamelCase_						     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





