



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     				=						None


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE     				=						{
    "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     				=						{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Tuple									=  VOCAB_FILES_NAMES
					lowerCAmelCase_				:    str									=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				:    int									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				:    Union[str, Any]									=  ['input_ids', 'attention_mask']
					lowerCAmelCase_				:    Dict									=  TaTokenizer

					lowerCAmelCase_				:    List[int]									=  []



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

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



					@staticmethod
					def    A__		(			lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							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."    ,	lowerCAmelCase    ,	)

							return max_model_length



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = 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(lowerCAmelCase     ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
									return
							UpperCAmelCase_						=				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    ,	lowerCAmelCase    ,	lowerCAmelCase = 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    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = 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    A__		(			self     ):
							return list(
							    set(filter(lambda lowerCAmelCase     : bool(re.search(r"<extra_id_\d+>"    ,	lowerCAmelCase     )     ) is not None    ,	self.additional_special_tokens     )     )     )



					def    A__		(			self     ):
							return [self.convert_tokens_to_ids(lowerCAmelCase     ) for token in self.get_sentinel_tokens()]










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE     				=						{
    "vocab_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-german-cased": (
            "https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
        ),
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE     				=						{
    "distilbert-base-uncased": 512,
    "distilbert-base-uncased-distilled-squad": 512,
    "distilbert-base-cased": 512,
    "distilbert-base-cased-distilled-squad": 512,
    "distilbert-base-german-cased": 512,
    "distilbert-base-multilingual-cased": 512,
}


SCREAMING_SNAKE_CASE     				=						{
    "distilbert-base-uncased": {"do_lower_case": True},
    "distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
    "distilbert-base-cased": {"do_lower_case": False},
    "distilbert-base-cased-distilled-squad": {"do_lower_case": False},
    "distilbert-base-german-cased": {"do_lower_case": False},
    "distilbert-base-multilingual-cased": {"do_lower_case": False},
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Any									=  VOCAB_FILES_NAMES
					lowerCAmelCase_				:    List[str]									=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				:    str									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				:    Union[str, Any]									=  PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase_				:    int									=  ['input_ids', 'attention_mask']
					lowerCAmelCase_				:    str									=  DistilBertTokenizer



					def __init__(			self    ,	lowerCAmelCase=None    ,	lowerCAmelCase=None    ,	lowerCAmelCase=True    ,	lowerCAmelCase="[UNK]"    ,	lowerCAmelCase="[SEP]"    ,	lowerCAmelCase="[PAD]"    ,	lowerCAmelCase="[CLS]"    ,	lowerCAmelCase="[MASK]"    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							super().__init__(
							    lowerCAmelCase    ,	tokenizer_file=lowerCAmelCase    ,	do_lower_case=lowerCAmelCase    ,	unk_token=lowerCAmelCase    ,	sep_token=lowerCAmelCase    ,	pad_token=lowerCAmelCase    ,	cls_token=lowerCAmelCase    ,	mask_token=lowerCAmelCase    ,	tokenize_chinese_chars=lowerCAmelCase    ,	strip_accents=lowerCAmelCase    ,	**lowerCAmelCase    ,	)

							UpperCAmelCase_						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
							if (
							    normalizer_state.get("lowercase"    ,	lowerCAmelCase     ) != do_lower_case
							    or normalizer_state.get("strip_accents"    ,	lowerCAmelCase     ) != strip_accents
							    or normalizer_state.get("handle_chinese_chars"    ,	lowerCAmelCase     ) != tokenize_chinese_chars
							):
									UpperCAmelCase_						=				getattr(lowerCAmelCase    ,	normalizer_state.pop("type"     )     )
									UpperCAmelCase_						=				do_lower_case
									UpperCAmelCase_						=				strip_accents
									UpperCAmelCase_						=				tokenize_chinese_chars
									UpperCAmelCase_						=				normalizer_class(**lowerCAmelCase     )

							UpperCAmelCase_						=				do_lower_case



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

							return output



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				[self.sep_token_id]
							UpperCAmelCase_						=				[self.cls_token_id]
							if token_ids_a is None:
									return len(cls + token_ids_a + sep     ) * [0]
							return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				self._tokenizer.model.save(lowerCAmelCase    ,	name=lowerCAmelCase     )
							return tuple(lowerCAmelCase     )

