






"""simple docstring"""







import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_lowerCAmelCase            =	logging.get_logger(__name__)

_lowerCAmelCase            =	{'vocab_file': 'spiece.model'}

_lowerCAmelCase            =	{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

_lowerCAmelCase            =	{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
_lowerCAmelCase            =	0
_lowerCAmelCase            =	1
_lowerCAmelCase            =	2
_lowerCAmelCase            =	3
_lowerCAmelCase            =	4


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : Tuple												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : List[str]												=		"""left"""






						def __init__(		self  :Union[str, Any]			, __magic_name__  :int			, __magic_name__  :Optional[int]=False			, __magic_name__  :str=True			, __magic_name__  :List[Any]=False			, __magic_name__  :Union[str, Any]="<s>"			, __magic_name__  :Optional[Any]="</s>"			, __magic_name__  :str="<unk>"			, __magic_name__  :Optional[Any]="<sep>"			, __magic_name__  :Optional[Any]="<pad>"			, __magic_name__  :Union[str, Any]="<cls>"			, __magic_name__  :int="<mask>"			, __magic_name__  :List[Any]=["<eop>", "<eod>"]			, __magic_name__  :Optional[Dict[str, Any]] = None			, **__magic_name__  :Optional[Any]			, )      ->None:
										# Mask token behave like a normal word, i.e. include the space before it
										lowercase    :      Any       = AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__				) if isinstance(__magic_name__			, __magic_name__				) else mask_token

										lowercase    :      Dict       = {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    do_lower_case=__magic_name__			, remove_space=__magic_name__			, keep_accents=__magic_name__			, bos_token=__magic_name__			, eos_token=__magic_name__			, unk_token=__magic_name__			, sep_token=__magic_name__			, pad_token=__magic_name__			, cls_token=__magic_name__			, mask_token=__magic_name__			, additional_special_tokens=__magic_name__			, sp_model_kwargs=self.sp_model_kwargs			, **__magic_name__			, )

										lowercase    :      str       = 3

										lowercase    :      Optional[int]       = do_lower_case
										lowercase    :      List[Any]       = remove_space
										lowercase    :      List[Any]       = keep_accents
										lowercase    :      int       = vocab_file

										lowercase    :      List[str]       = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(__magic_name__				)






						@property
						def      __snake_case      (		self  :str				)      ->Optional[Any]:
										return len(self.sp_model				)






						def      __snake_case      (		self  :List[Any]				)      ->List[Any]:
										lowercase    :      List[Any]       = {self.convert_ids_to_tokens(__magic_name__				): i for i in range(self.vocab_size				)}
										vocab.update(self.added_tokens_encoder				)
										return vocab






						def __getstate__(		self  :Tuple				)      ->str:
										lowercase    :      Optional[Any]       = self.__dict__.copy()
										lowercase    :      Optional[Any]       = None
										return state






						def __setstate__(		self  :Tuple			, __magic_name__  :Dict				)      ->Optional[Any]:
										lowercase    :      Any       = d

										# for backward compatibility
										if not hasattr(self			, """sp_model_kwargs"""				):
														lowercase    :      Optional[int]       = {}

										lowercase    :      Optional[int]       = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(self.vocab_file				)






						def      __snake_case      (		self  :str			, __magic_name__  :str				)      ->List[Any]:
										if self.remove_space:
														lowercase    :      Optional[Any]       = """ """.join(inputs.strip().split()				)
										else:
														lowercase    :      Optional[int]       = inputs
										lowercase    :      List[str]       = outputs.replace("""``"""			, """\""""				).replace("""''"""			, """\""""				)

										if not self.keep_accents:
														lowercase    :      Optional[int]       = unicodedata.normalize("""NFKD"""			, __magic_name__				)
														lowercase    :      Dict       = """""".join([c for c in outputs if not unicodedata.combining(__magic_name__				)]				)
										if self.do_lower_case:
														lowercase    :      Tuple       = outputs.lower()

										return outputs






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :str				)      ->List[str]:
										lowercase    :      Union[str, Any]       = self.preprocess_text(__magic_name__				)
										lowercase    :      Tuple       = self.sp_model.encode(__magic_name__			, out_type=__magic_name__				)
										lowercase    :      List[str]       = []
										for piece in pieces:
														if len(__magic_name__				) > 1 and piece[-1] == str(""","""				) and piece[-2].isdigit():
																		lowercase    :      List[Any]       = self.sp_model.EncodeAsPieces(piece[:-1].replace(__magic_name__			, """"""				)				)
																		if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																						if len(cur_pieces[0]				) == 1:
																										lowercase    :      Any       = cur_pieces[1:]
																						else:
																										lowercase    :      List[Any]       = cur_pieces[0][1:]
																		cur_pieces.append(piece[-1]				)
																		new_pieces.extend(__magic_name__				)
														else:
																		new_pieces.append(__magic_name__				)

										return new_pieces






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :List[str]				)      ->List[str]:
										return self.sp_model.PieceToId(__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :Optional[Any]				)      ->int:
										return self.sp_model.IdToPiece(__magic_name__				)






						def      __snake_case      (		self  :int			, __magic_name__  :List[Any]				)      ->Union[str, Any]:
										lowercase    :      Any       = """""".join(__magic_name__				).replace(__magic_name__			, """ """				).strip()
										return out_string






						def      __snake_case      (		self  :List[str]			, __magic_name__  :List[int]			, __magic_name__  :bool = False			, __magic_name__  :bool = None			, __magic_name__  :bool = True			, **__magic_name__  :int			, )      ->str:
										lowercase    :      str       = kwargs.pop("""use_source_tokenizer"""			, __magic_name__				)

										lowercase    :      Union[str, Any]       = self.convert_ids_to_tokens(__magic_name__			, skip_special_tokens=__magic_name__				)

										# To avoid mixing byte-level and unicode for byte-level BPT
										# we need to build string separately for added tokens and byte-level tokens
										# cf. https://github.com/huggingface/transformers/issues/1133
										lowercase    :      str       = []
										lowercase    :      int       = []
										for token in filtered_tokens:
														if skip_special_tokens and token in self.all_special_ids:
																		continue
														if token in self.added_tokens_encoder:
																		if current_sub_text:
																						sub_texts.append(self.convert_tokens_to_string(__magic_name__				)				)
																						lowercase    :      Tuple       = []
																		sub_texts.append(__magic_name__				)
														else:
																		current_sub_text.append(__magic_name__				)
										if current_sub_text:
														sub_texts.append(self.convert_tokens_to_string(__magic_name__				)				)

										# Mimic the behavior of the Rust tokenizer:
										# By default, there are no spaces between special tokens
										lowercase    :      Any       = """""".join(__magic_name__				)

										lowercase    :      List[str]       = (
										    clean_up_tokenization_spaces
										    if clean_up_tokenization_spaces is not None
										    else self.clean_up_tokenization_spaces
										)
										if clean_up_tokenization_spaces:
														lowercase    :      Optional[int]       = self.clean_up_tokenization(__magic_name__				)
														return clean_text
										else:
														return text






						def      __snake_case      (		self  :Tuple			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      Union[str, Any]       = [self.sep_token_id]
										lowercase    :      Any       = [self.cls_token_id]
										if token_ids_a is None:
														return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None			, __magic_name__  :bool = False				)      ->List[int]:

										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=__magic_name__			, token_ids_a=__magic_name__			, already_has_special_tokens=__magic_name__				)

										if token_ids_a is not None:
														return ([0] * len(__magic_name__				)) + [1] + ([0] * len(__magic_name__				)) + [1, 1]
										return ([0] * len(__magic_name__				)) + [1, 1]






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      Optional[int]       = [self.sep_token_id]
										lowercase    :      Union[str, Any]       = [2]

										if token_ids_a is None:
														return len(token_ids_a + sep				) * [0] + cls_segment_id
										return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id






						def      __snake_case      (		self  :Any			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return
										lowercase    :      List[Any]       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				) and os.path.isfile(self.vocab_file				):
														copyfile(self.vocab_file			, __magic_name__				)
										elif not os.path.isfile(self.vocab_file				):
														with open(__magic_name__			, """wb"""				) as fi:
																		lowercase    :      str       = self.sp_model.serialized_model_proto()
																		fi.write(__magic_name__				)

										return (out_vocab_file,)








"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      UpperCamelCase		(__snake_case     ):






						def __init__(		self  :Any			, __magic_name__  :Union[str, Any]			, __magic_name__  :Optional[Any]				)      ->Dict:
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase    :      Optional[Any]       = DDIMScheduler.from_config(scheduler.config				)

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






						@torch.no_grad()
						def __call__(		self  :Optional[int]			, __magic_name__  :int = 1			, __magic_name__  :Optional[Union[torch.Generator, List[torch.Generator]]] = None			, __magic_name__  :float = 0.0			, __magic_name__  :int = 50			, __magic_name__  :Optional[bool] = None			, __magic_name__  :Optional[str] = "pil"			, __magic_name__  :bool = True			, )      ->Union[ImagePipelineOutput, Tuple]:

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size			, __magic_name__				):
														lowercase    :      int       = (
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
										else:
														lowercase    :      Tuple       = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(__magic_name__			, __magic_name__				) and len(__magic_name__				) != batch_size:
														raise ValueError(
														    f"""You have passed a list of generators of length {len(__magic_name__				)}, but requested an effective batch"""
														    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

										lowercase    :      Dict       = randn_tensor(__magic_name__			, generator=__magic_name__			, device=self.device			, dtype=self.unet.dtype				)

										# set step values
										self.scheduler.set_timesteps(__magic_name__				)

										for t in self.progress_bar(self.scheduler.timesteps				):
														# 1. predict noise model_output
														lowercase    :      Union[str, Any]       = self.unet(__magic_name__			, __magic_name__				).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														lowercase    :      int       = self.scheduler.step(
														    __magic_name__			, __magic_name__			, __magic_name__			, eta=__magic_name__			, use_clipped_model_output=__magic_name__			, generator=__magic_name__				).prev_sample

										lowercase    :      Optional[Any]       = (image / 2 + 0.5).clamp(0			, 1				)
										lowercase    :      Tuple       = image.cpu().permute(0			, 2			, 3			, 1				).numpy()
										if output_type == "pil":
														lowercase    :      Any       = self.numpy_to_pil(__magic_name__				)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__magic_name__				)
