



"""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


lowercase__			   =  logging.get_logger(__name__)

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

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

lowercase__			   =  "▁"




class SCREAMING_SNAKE_CASE__					(		lowercase__      ):
						_lowerCAmelCase							=					VOCAB_FILES_NAMES
						_lowerCAmelCase							=					PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase							=					["""input_ids""", """attention_mask"""]



						def __init__(self						,     _lowercase						,     _lowercase="</s>"						,     _lowercase="<unk>"						,     _lowercase="<pad>"						,     _lowercase=100						,     _lowercase=None						,     _lowercase = None						,     _lowercase=True						,     **_lowercase						,     ):





													'''simple docstring'''

													if extra_ids > 0 and additional_special_tokens is None:
																				__a		:		str									=       [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		:		Optional[int]									=       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		:		Union[str, Any]									=       legacy
													__a		:		List[str]									=       {} 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		:		List[str]									=       vocab_file
													__a		:		Optional[int]									=       extra_ids

													__a		:		Union[str, Any]									=       spm.SentencePieceProcessor(**self.sp_model_kwargs							)
													self.sp_model.Load(__lowercase							)



						@staticmethod
						def     lowerCAmelCase__(_lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																				__a		:		Any									=       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     lowerCAmelCase__(self							):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       {self.convert_ids_to_tokens(__lowercase							): i for i in range(self.vocab_size							)}
													vocab.update(self.added_tokens_encoder							)
													return vocab



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None						,     _lowercase = 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     lowerCAmelCase__(self							):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''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     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None							):





													'''simple docstring'''

													__a		:		Optional[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     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None							):





													'''simple docstring'''

													__a		:		int									=       self._add_eos_if_not_present(__lowercase							)
													if token_ids_a is None:
																				return token_ids_a
													else:
																				__a		:		Dict									=       self._add_eos_if_not_present(__lowercase							)
																				return token_ids_a + token_ids_a



						def __getstate__(self							):





													'''simple docstring'''

													__a		:		Any									=       self.__dict__.copy()
													__a		:		Union[str, Any]									=       None
													return state



						def __setstate__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       d

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

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



						def     lowerCAmelCase__(self						,     _lowercase						,     **_lowercase							):





													'''simple docstring'''

													if not self.legacy:
																				__a		:		Union[str, Any]									=       SPIECE_UNDERLINE + text.replace(__lowercase						,     """ """							)
													return super().tokenize(__lowercase						,     **__lowercase							)



						def     lowerCAmelCase__(self						,     _lowercase						,     **_lowercase							):





													'''simple docstring'''

													if not self.legacy:
																				__a		:		int									=       text.startswith(__lowercase							)
																				if is_first:
																											__a		:		Optional[int]									=       text[1:]

													__a		:		Union[str, Any]									=       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		:		Union[str, Any]									=       ([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
													return tokens



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		int									=       []
													__a		:		Tuple									=       """"""
													__a		:		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(__lowercase							) + token
																											__a		:		Any									=       True
																											__a		:		List[Any]									=       []
																				else:
																											current_sub_tokens.append(__lowercase							)
																											__a		:		int									=       False
													out_string += self.sp_model.decode(__lowercase							)
													return out_string.strip()





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None							):





													'''simple docstring'''

													if not os.path.isdir(__lowercase							):
																				logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
																				return
													__a		:		str									=       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		:		Union[str, Any]									=       self.sp_model.serialized_model_proto()
																											fi.write(__lowercase							)

													return (out_vocab_file,)





"""simple docstring"""

import torch

from diffusers import DiffusionPipeline




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													super().__init__()

													self.register_modules(unet=_lowercase						,     scheduler=_lowercase							)





						def __call__(self							):





													'''simple docstring'''

													__a		:		Dict									=       torch.randn(
													    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,     )
													__a		:		Optional[Any]									=       1

													__a		:		List[str]									=       self.unet(_lowercase						,     _lowercase							).sample
													__a		:		Union[str, Any]									=       self.scheduler.step(_lowercase						,     _lowercase						,     _lowercase							).prev_sample

													__a		:		Optional[int]									=       scheduler_output - scheduler_output + torch.ones_like(_lowercase							)

													return result

