



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''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

lowerCamelCase__	=       '''▁'''






class       SCREAMING_SNAKE_CASE	(							__UpperCAmelCase    ):
							__lowerCamelCase		:		Optional[Any]			=VOCAB_FILES_NAMES
							__lowerCamelCase		:		Optional[int]			=PRETRAINED_VOCAB_FILES_MAP
							__lowerCamelCase		:		Optional[int]			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCamelCase		:		Any			=['input_ids', 'attention_mask']





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

											'''simple docstring'''
											# Add extra_ids to the special token list
											if extra_ids > 0 and additional_special_tokens is None:
															__a        =						[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_id special tokens
															__a        =						len(set(filter(lambda __lowercase		: 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"""		)
											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"""		)

											__a        =						legacy
											__a        =						{} if sp_model_kwargs is None else sp_model_kwargs

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

											__a        =						vocab_file
											__a        =						extra_ids

											__a        =						spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(__lowercase		)





							@staticmethod
							def   UpperCamelCase_    (      __lowercase						:						Optional[int]					, __lowercase						:						Any					, __lowercase						:						Optional[Any]		):

											'''simple docstring'''
											if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
															__a        =						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."""					, __lowercase					, )

											return max_model_length





							@property
							def   UpperCamelCase_    (      self						:						Union[str, Any]		):

											'''simple docstring'''
											return self.sp_model.get_piece_size() + self._extra_ids





							def   UpperCamelCase_    (      self						:						Optional[int]		):

											'''simple docstring'''
											__a        =						{self.convert_ids_to_tokens(__lowercase		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab





							def   UpperCamelCase_    (      self						:						List[str]					, __lowercase						:						Optional[int]					, __lowercase						:						Optional[Any] = None					, __lowercase						:						List[Any] = False		):

											'''simple docstring'''
											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__lowercase					, token_ids_a=__lowercase					, already_has_special_tokens=__lowercase		)

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





							def   UpperCamelCase_    (      self						:						Dict		):

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





							def   UpperCamelCase_    (      self						:						Optional[int]		):

											'''simple docstring'''
											return [self._convert_token_to_id(__lowercase		) for token in self.get_sentinel_tokens()]





							def   UpperCamelCase_    (      self						:						str					, __lowercase						:						List[Any]		):

											'''simple docstring'''
											if len(__lowercase		) > 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   UpperCamelCase_    (      self						:						Optional[int]					, __lowercase						:						Optional[int]					, __lowercase						:						List[str] = None		):

											'''simple docstring'''
											__a        =						[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						:						Dict					, __lowercase						:						Optional[Any]					, __lowercase						:						List[str] = None		):

											'''simple docstring'''
											__a        =						self._add_eos_if_not_present(__lowercase		)
											if token_ids_a is None:
															return token_ids_a
											else:
															__a        =						self._add_eos_if_not_present(__lowercase		)
															return token_ids_a + token_ids_a





							def __getstate__(      self						:						int		):

											'''simple docstring'''
											__a        =						self.__dict__.copy()
											__a        =						None
											return state





							def __setstate__(      self						:						Tuple					, __lowercase						:						int		):

											'''simple docstring'''
											__a        =						d

											# for backward compatibility
											if not hasattr(self					, """sp_model_kwargs"""		):
															__a        =						{}

											__a        =						spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)





							def   UpperCamelCase_    (      self						:						Optional[Any]					, __lowercase						:						List[Any]					, **__lowercase						:						List[str]		):

											'''simple docstring'''
											# 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:
															__a        =						SPIECE_UNDERLINE + text.replace(__lowercase					, """ """		)
											return super().tokenize(__lowercase					, **__lowercase		)





							def   UpperCamelCase_    (      self						:						Union[str, Any]					, __lowercase						:						str					, **__lowercase						:						Union[str, Any]		):

											'''simple docstring'''
											if not self.legacy:
															__a        =						text.startswith(__lowercase		)
															if is_first:
																			__a        =						text[1:]

											__a        =						self.sp_model.encode(__lowercase					, out_type=__lowercase		)

											if not self.legacy and not is_first and not text.startswith(""" """		) and tokens[0].startswith(__lowercase		):
															__a        =						([tokens[0][1:]] if len(tokens[0]		) > 1 else []) + tokens[1:]
											return tokens





							def   UpperCamelCase_    (      self						:						List[str]					, __lowercase						:						List[str]		):

											'''simple docstring'''
											if token.startswith("""<extra_id_"""		):
															__a        =						re.match(r"""<extra_id_(\d+)>"""					, __lowercase		)
															__a        =						int(match.group(1		)		)
															return self.vocab_size - num - 1
											return self.sp_model.piece_to_id(__lowercase		)





							def   UpperCamelCase_    (      self						:						Tuple					, __lowercase						:						Tuple		):

											'''simple docstring'''
											if index < self.sp_model.get_piece_size():
															__a        =						self.sp_model.IdToPiece(__lowercase		)
											else:
															__a        =						F"<extra_id_{self.vocab_size - 1 - index}>"
											return token





							def   UpperCamelCase_    (      self						:						Optional[int]					, __lowercase						:						Optional[int]		):

											'''simple docstring'''
											__a        =						[]
											__a        =						""""""
											__a        =						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(__lowercase		) + token
																			__a        =						True
																			__a        =						[]
															else:
																			current_sub_tokens.append(__lowercase		)
																			__a        =						False
											out_string += self.sp_model.decode(__lowercase		)
											return out_string.strip()







							def   UpperCamelCase_    (      self						:						Dict					, __lowercase						:						Optional[Any]					, __lowercase						:						Any = None		):

											'''simple docstring'''
											if not os.path.isdir(__lowercase		):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
															return
											__a        =						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		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file					, __lowercase		)
											elif not os.path.isfile(self.vocab_file		):
															with open(__lowercase					, """wb"""		) as fi:
																			__a        =						self.sp_model.serialized_model_proto()
																			fi.write(__lowercase		)

											return (out_vocab_file,)





'''simple docstring'''




import torch

from diffusers import StableDiffusionPipeline


lowerCamelCase							:Tuple				    =					'''path-to-your-trained-model'''
lowerCamelCase							:Optional[int]				    =					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowerCamelCase							:Optional[int]				    =					'''A photo of sks dog in a bucket'''
lowerCamelCase							:List[Any]				    =					pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')