

'''simple docstring'''





import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
							from ...tokenization_utils_base import TextInput
from ...utils import logging


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{"""vocab_file""": """spiece.model"""}

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""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase__								=		{
    """t5-small""": 5_1_2,
    """t5-base""": 5_1_2,
    """t5-large""": 5_1_2,
    """t5-3b""": 5_1_2,
    """t5-11b""": 5_1_2,
}

lowerCAmelCase__								=		"""▁"""







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  VOCAB_FILES_NAMES
		a__					       =  PRETRAINED_VOCAB_FILES_MAP
		a__					       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a__					       =  ["input_ids", "attention_mask"]


		def __init__(				self	,       __snake_case	,       __snake_case="</s>"	,       __snake_case="<unk>"	,       __snake_case="<pad>"	,       __snake_case=1_00	,       __snake_case=None	,       __snake_case = None	,       __snake_case=True	,       **__snake_case	,       ):
			# Add extra_ids to the special token list
			if extra_ids > 0 and additional_special_tokens is None:
				_UpperCamelCase   :		Union[str, Any]			=						[f'''<extra_id_{i}>''' for i in range(__snake_case)]
			elif extra_ids > 0 and additional_special_tokens is not None:
				# Check that we have the right number of extra_id special tokens
				_UpperCamelCase   :		List[str]			=						len(set(filter(lambda __snake_case: bool('extra_id' in str(__snake_case))	,       __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')
			if legacy:
				logger.warning_once(
				    f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
				    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565')

			_UpperCamelCase   :		Any			=						legacy
			_UpperCamelCase   :		List[str]			=						{} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    eos_token=__snake_case	,       unk_token=__snake_case	,       pad_token=__snake_case	,       extra_ids=__snake_case	,       additional_special_tokens=__snake_case	,       sp_model_kwargs=self.sp_model_kwargs	,       legacy=__snake_case	,       **__snake_case	,       )

			_UpperCamelCase   :		Tuple			=						vocab_file
			_UpperCamelCase   :		Any			=						extra_ids

			_UpperCamelCase   :		Optional[Any]			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.Load(__snake_case)


		@staticmethod
		def 		A__					(				__snake_case	,       __snake_case	,       __snake_case):
			if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
				_UpperCamelCase   :		Dict			=						TaTokenizer.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.'	,       __snake_case	,       )

			return max_model_length


		@property
		def 		A__					(				self):
			return self.sp_model.get_piece_size() + self._extra_ids


		def 		A__					(				self):
			_UpperCamelCase   :		int			=						{self.convert_ids_to_tokens(__snake_case): i for i in range(self.vocab_size)}
			vocab.update(self.added_tokens_encoder)
			return vocab


		def 		A__					(				self	,       __snake_case	,       __snake_case = None	,       __snake_case = False):
			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=__snake_case	,       token_ids_a=__snake_case	,       already_has_special_tokens=__snake_case)

			# normal case: some special tokens
			if token_ids_a is None:
				return ([0] * len(__snake_case)) + [1]
			return ([0] * len(__snake_case)) + [1] + ([0] * len(__snake_case)) + [1]


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


		def 		A__					(				self):
			return [self._convert_token_to_id(__snake_case) for token in self.get_sentinel_tokens()]


		def 		A__					(				self	,       __snake_case):
			if len(__snake_case) > 0 and token_ids[-1] == self.eos_token_id:
				warnings.warn(
				    f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
				    ' eos tokens being added.')
				return token_ids
			else:
				return token_ids + [self.eos_token_id]


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			_UpperCamelCase   :		Any			=						[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	,       __snake_case	,       __snake_case = None):
			_UpperCamelCase   :		Tuple			=						self._add_eos_if_not_present(__snake_case)
			if token_ids_a is None:
				return token_ids_a
			else:
				_UpperCamelCase   :		Tuple			=						self._add_eos_if_not_present(__snake_case)
				return token_ids_a + token_ids_a


		def __getstate__(				self):
			_UpperCamelCase   :		Any			=						self.__dict__.copy()
			_UpperCamelCase   :		Optional[Any]			=						None
			return state


		def __setstate__(				self	,       __snake_case):
			_UpperCamelCase   :		str			=						d

			# for backward compatibility
			if not hasattr(self	,       'sp_model_kwargs'):
				_UpperCamelCase   :		List[str]			=						{}

			_UpperCamelCase   :		Dict			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.Load(self.vocab_file)


		def 		A__					(				self	,       __snake_case	,       **__snake_case):
			# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
			# the beginning of the text
			if not self.legacy:
				_UpperCamelCase   :		Any			=						SPIECE_UNDERLINE + text.replace(__snake_case	,       ' ')
			return super().tokenize(__snake_case	,       **__snake_case)


		def 		A__					(				self	,       __snake_case	,       **__snake_case):
			if not self.legacy:
				_UpperCamelCase   :		int			=						text.startswith(__snake_case)
				if is_first:
					_UpperCamelCase   :		Union[str, Any]			=						text[1:]

			_UpperCamelCase   :		Optional[Any]			=						self.sp_model.encode(__snake_case	,       out_type=__snake_case)

			if not self.legacy and not is_first and not text.startswith(' ') and tokens[0].startswith(__snake_case):
				_UpperCamelCase   :		Union[str, Any]			=						([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]
			return tokens


		def 		A__					(				self	,       __snake_case):
			if token.startswith('<extra_id_'):
				_UpperCamelCase   :		int			=						re.match(r'<extra_id_(\d+)>'	,       __snake_case)
				_UpperCamelCase   :		Union[str, Any]			=						int(match.group(1))
				return self.vocab_size - num - 1
			return self.sp_model.piece_to_id(__snake_case)


		def 		A__					(				self	,       __snake_case):
			if index < self.sp_model.get_piece_size():
				_UpperCamelCase   :		Tuple			=						self.sp_model.IdToPiece(__snake_case)
			else:
				_UpperCamelCase   :		List[str]			=						f'''<extra_id_{self.vocab_size - 1 - index}>'''
			return token


		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		List[Any]			=						[]
			_UpperCamelCase   :		List[Any]			=						''
			_UpperCamelCase   :		Tuple			=						False
			for token in tokens:
				# make sure that special tokens are not decoded using sentencepiece model
				if token in self.all_special_tokens:
					if not prev_is_special:
						out_string += " "
					out_string += self.sp_model.decode(__snake_case) + token
					_UpperCamelCase   :		str			=						True
					_UpperCamelCase   :		Optional[Any]			=						[]
				else:
					current_sub_tokens.append(__snake_case)
					_UpperCamelCase   :		List[str]			=						False
			out_string += self.sp_model.decode(__snake_case)
			return out_string.strip()






		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			if not os.path.isdir(__snake_case):
				logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
				return
			_UpperCamelCase   :		Tuple			=						os.path.join(
			    __snake_case	,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

			if os.path.abspath(self.vocab_file) != os.path.abspath(__snake_case) and os.path.isfile(self.vocab_file):
				copyfile(self.vocab_file	,       __snake_case)
			elif not os.path.isfile(self.vocab_file):
				with open(__snake_case	,       'wb') as fi:
					_UpperCamelCase   :		Optional[int]			=						self.sp_model.serialized_model_proto()
					fi.write(__snake_case)

			return (out_vocab_file,)







import logging

from transformers.configuration_utils import PretrainedConfig


lowerCAmelCase__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "masked_bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="topK"	,       __snake_case="constant"	,       __snake_case=0.0	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		List[Any]			=						vocab_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Any			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		str			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		Any			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Tuple			=						max_position_embeddings
			_UpperCamelCase   :		Dict			=						type_vocab_size
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		List[Any]			=						layer_norm_eps
			_UpperCamelCase   :		Tuple			=						pruning_method
			_UpperCamelCase   :		Tuple			=						mask_init
			_UpperCamelCase   :		Dict			=						mask_scale




