'''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


snake_case_			=     logging.get_logger(__name__)

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

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""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
snake_case_			=     {
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

snake_case_			=     """▁"""


class 	a__     (				_a	):
	__magic_name__		:    Dict	    =     VOCAB_FILES_NAMES
	__magic_name__		:    str	    =     PRETRAINED_VOCAB_FILES_MAP
	__magic_name__		:    Union[str, Any]	    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__		:    List[str]	    =     ["""input_ids""", """attention_mask"""]


	def __init__(self : int,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Dict="</s>",					__UpperCAmelCase : int="<unk>",					__UpperCAmelCase : Optional[int]="<pad>",					__UpperCAmelCase : int=100,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : Optional[Any] = None,					__UpperCAmelCase : Any=True,					**__UpperCAmelCase : List[str],					)     ->			None:

					"""simple docstring"""

					if extra_ids > 0 and additional_special_tokens is None:
									SCREAMING_SNAKE_CASE  :						Dict      					= [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
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= len(set(filter(lambda __UpperCAmelCase      : 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'''      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= legacy
					SCREAMING_SNAKE_CASE  :						List[Any]      					= {} 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_,					)

					SCREAMING_SNAKE_CASE  :						str      					= vocab_file
					SCREAMING_SNAKE_CASE  :						int      					= extra_ids

					SCREAMING_SNAKE_CASE  :						int      					= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
					self.sp_model.Load(snake_case_      )


	@staticmethod
	def 				lowercase__  (__UpperCAmelCase : int,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Optional[Any]      )     ->			Optional[Any]:

					"""simple docstring"""

					if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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 				lowercase__  (self : List[str]      )     ->			Any:

					"""simple docstring"""

					return self.sp_model.get_piece_size() + self._extra_ids


	def 				lowercase__  (self : List[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {self.convert_ids_to_tokens(snake_case_      ): i for i in range(self.vocab_size      )}
					vocab.update(self.added_tokens_encoder      )
					return vocab


	def 				lowercase__  (self : int,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Any = None,					__UpperCAmelCase : Tuple = False      )     ->			List[int]:

					"""simple docstring"""

					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 				lowercase__  (self : Tuple      )     ->			List[Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : int      )     ->			int:

					"""simple docstring"""

					return [self._convert_token_to_id(snake_case_      ) for token in self.get_sentinel_tokens()]


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : Union[str, Any]      )     ->			List[int]:

					"""simple docstring"""

					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 				lowercase__  (self : Dict,					__UpperCAmelCase : List[str],					__UpperCAmelCase : Any = None      )     ->			List[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						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 				lowercase__  (self : Tuple,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Tuple = None      )     ->			List[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self._add_eos_if_not_present(snake_case_      )
					if token_ids_a is None:
									return token_ids_a
					else:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self._add_eos_if_not_present(snake_case_      )
									return token_ids_a + token_ids_a


	def __getstate__(self : List[str]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.__dict__.copy()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= None
					return state


	def __setstate__(self : List[str],					__UpperCAmelCase : Optional[Any]      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= d

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

					SCREAMING_SNAKE_CASE  :						int      					= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
					self.sp_model.Load(self.vocab_file      )


	def 				lowercase__  (self : int,					__UpperCAmelCase : List[str],					**__UpperCAmelCase : str      )     ->			List[str]:

					"""simple docstring"""

					if not self.legacy:
									SCREAMING_SNAKE_CASE  :						Any      					= SPIECE_UNDERLINE + text.replace(snake_case_,					''' '''      )
					return super().tokenize(snake_case_,					**snake_case_      )


	def 				lowercase__  (self : int,					__UpperCAmelCase : int,					**__UpperCAmelCase : Tuple      )     ->			List[Any]:

					"""simple docstring"""

					if not self.legacy:
									SCREAMING_SNAKE_CASE  :						int      					= text.startswith(snake_case_      )
									if is_first:
													SCREAMING_SNAKE_CASE  :						List[str]      					= text[1:]

					SCREAMING_SNAKE_CASE  :						List[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_      ):
									SCREAMING_SNAKE_CASE  :						str      					= ([tokens[0][1:]] if len(tokens[0]      ) > 1 else []) + tokens[1:]
					return tokens


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Union[str, Any]      )     ->			List[str]:

					"""simple docstring"""

					if token.startswith('''<extra_id_'''      ):
									SCREAMING_SNAKE_CASE  :						Dict      					= re.match(R'''<extra_id_(\d+)>''',					snake_case_      )
									SCREAMING_SNAKE_CASE  :						Dict      					= int(match.group(1      )      )
									return self.vocab_size - num - 1
					return self.sp_model.piece_to_id(snake_case_      )


	def 				lowercase__  (self : str,					__UpperCAmelCase : Union[str, Any]      )     ->			Optional[int]:

					"""simple docstring"""

					if index < self.sp_model.get_piece_size():
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.sp_model.IdToPiece(snake_case_      )
					else:
									SCREAMING_SNAKE_CASE  :						Any      					= F'''<extra_id_{self.vocab_size - 1 - index}>'''
					return token


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : Optional[int]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					SCREAMING_SNAKE_CASE  :						int      					= ''''''
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 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
													SCREAMING_SNAKE_CASE  :						Any      					= True
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []
									else:
													current_sub_tokens.append(snake_case_      )
													SCREAMING_SNAKE_CASE  :						Dict      					= False
					out_string += self.sp_model.decode(snake_case_      )
					return out_string.strip()






	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : str,					__UpperCAmelCase : Union[str, Any] = None      )     ->			Tuple[str]:

					"""simple docstring"""

					if not os.path.isdir(snake_case_      ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
									return
					SCREAMING_SNAKE_CASE  :						Any      					= 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:
													SCREAMING_SNAKE_CASE  :						Dict      					= self.sp_model.serialized_model_proto()
													fi.write(snake_case_      )

					return (out_vocab_file,)









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
	from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




