






"""simple docstring"""







import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
				import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase            =	logging.get_logger(__name__)

_lowerCAmelCase            =	{'vocab_file': 'vocab.txt'}

_lowerCAmelCase            =	{
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

_lowerCAmelCase            =	{
    'openbmb/cpm-ant-10b': 10_24,
}


def      UpperCamelCase       ( _A )     ->       str:
				lowercase    :      Dict       = collections.OrderedDict()
				with open(_A  ,				"""r"""  ,				encoding="""utf-8""" ) as reader:
								lowercase    :      Dict       = reader.readlines()
				for index, token in enumerate(_A ):
								lowercase    :      Union[str, Any]       = token.rstrip("""\n""" )
								lowercase    :      Any       = index
				return vocab


class      UpperCamelCase		(__snake_case     ):






						def __init__(		self  :Union[str, Any]			, __magic_name__  :Optional[int]			, __magic_name__  :Any="<unk>"			, __magic_name__  :List[str]=200				)      ->int:
										lowercase    :      Union[str, Any]       = vocab
										lowercase    :      int       = unk_token
										lowercase    :      Optional[Any]       = max_input_chars_per_word






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :Optional[int]				)      ->List[str]:
										lowercase    :      Dict       = list(__magic_name__				)
										if len(__magic_name__				) > self.max_input_chars_per_word:
														return [self.unk_token]

										lowercase    :      Optional[int]       = 0
										lowercase    :      Optional[int]       = []
										while start < len(__magic_name__				):
														lowercase    :      Optional[Any]       = len(__magic_name__				)
														lowercase    :      Optional[int]       = None
														while start < end:
																		lowercase    :      int       = """""".join(chars[start:end]				)
																		if substr in self.vocab:
																						lowercase    :      Tuple       = substr
																						break
																		end -= 1
														if cur_substr is None:
																		sub_tokens.append(self.unk_token				)
																		start += 1
														else:
																		sub_tokens.append(__magic_name__				)
																		lowercase    :      List[Any]       = end

										return sub_tokens


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Dict												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : str												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : str												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : str												=		["""input_ids""", """attention_mask"""]
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		False






						def __init__(		self  :Optional[Any]			, __magic_name__  :Dict			, __magic_name__  :str="<d>"			, __magic_name__  :Union[str, Any]="</d>"			, __magic_name__  :List[str]="<s>"			, __magic_name__  :Optional[int]="</s>"			, __magic_name__  :Dict="<pad>"			, __magic_name__  :str="<unk>"			, __magic_name__  :str="</n>"			, __magic_name__  :Any="</_>"			, __magic_name__  :str="left"			, **__magic_name__  :str			, )      ->Optional[int]:
										requires_backends(self			, ["""jieba"""]				)
										super().__init__(
										    bod_token=__magic_name__			, eod_token=__magic_name__			, bos_token=__magic_name__			, eos_token=__magic_name__			, pad_token=__magic_name__			, unk_token=__magic_name__			, line_token=__magic_name__			, space_token=__magic_name__			, padding_side=__magic_name__			, **__magic_name__			, )
										lowercase    :      Any       = bod_token
										lowercase    :      Union[str, Any]       = eod_token
										lowercase    :      Any       = load_vocab(__magic_name__				)
										lowercase    :      Tuple       = self.encoder[space_token]
										lowercase    :      Tuple       = self.encoder[line_token]

										del self.encoder[space_token]
										del self.encoder[line_token]

										lowercase    :      Union[str, Any]       = collections.OrderedDict(sorted(self.encoder.items()			, key=lambda __magic_name__				: x[1]				)				)
										lowercase    :      Any       = {v: k for k, v in self.encoder.items()}

										lowercase    :      int       = WordpieceTokenizer(vocab=self.encoder			, unk_token=self.unk_token				)






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->Optional[int]:
										return self.encoder[self.bod_token]






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->Union[str, Any]:
										return self.encoder[self.eod_token]






						@property
						def      __snake_case      (		self  :List[str]				)      ->Tuple:
										return self.encoder["\n"]






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->int:
										return len(self.encoder				)






						def      __snake_case      (		self  :str				)      ->Any:
										return dict(self.encoder			, **self.added_tokens_encoder				)






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :Dict				)      ->int:
										lowercase    :      List[str]       = []
										for x in jieba.cut(__magic_name__			, cut_all=__magic_name__				):
														output_tokens.extend(self.wordpiece_tokenizer.tokenize(__magic_name__				)				)
										return output_tokens






						def      __snake_case      (		self  :Tuple			, __magic_name__  :Tuple			, **__magic_name__  :str				)      ->str:
										lowercase    :      Any       = [i for i in token_ids if i >= 0]
										lowercase    :      List[Any]       = [
										    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
										]
										return super()._decode(__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :str			, __magic_name__  :Any				)      ->Optional[int]:
										return token in self.encoder






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :List[str]				)      ->str:
										return "".join(__magic_name__				)






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :Union[str, Any]				)      ->Union[str, Any]:
										return self.encoder.get(__magic_name__			, self.encoder.get(self.unk_token				)				)






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :List[Any]				)      ->Optional[Any]:
										return self.decoder.get(__magic_name__			, self.unk_token				)






						def      __snake_case      (		self  :Any			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if os.path.isdir(__magic_name__				):
														lowercase    :      Tuple       = os.path.join(
														    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)
										else:
														lowercase    :      Any       = (filename_prefix + """-""" if filename_prefix else """""") + save_directory
										lowercase    :      int       = 0
										if " " in self.encoder:
														lowercase    :      int       = self.encoder[""" """]
														del self.encoder[" "]
										if "\n" in self.encoder:
														lowercase    :      Tuple       = self.encoder["""\n"""]
														del self.encoder["\n"]
										lowercase    :      str       = collections.OrderedDict(sorted(self.encoder.items()			, key=lambda __magic_name__				: x[1]				)				)
										with open(__magic_name__			, """w"""			, encoding="""utf-8"""				) as writer:
														for token, token_index in self.encoder.items():
																		if index != token_index:
																						logger.warning(
																						    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																						    """ Please check that the vocabulary is not corrupted!"""				)
																						lowercase    :      Union[str, Any]       = token_index
																		writer.write(token + """\n"""				)
																		index += 1
										return (vocab_file,)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :List[int]			, __magic_name__  :List[int] = None				)      ->List[int]:
										if token_ids_a is None:
														return [self.bos_token_id] + token_ids_a
										return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






						def      __snake_case      (		self  :Any			, __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 [1] + ([0] * len(__magic_name__				)) + [1] + ([0] * len(__magic_name__				))
										return [1] + ([0] * len(__magic_name__				))








"""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__				)
