


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[int]						):
       # This defines a "chinese character" as anything in the CJK Unicode block:
       #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
       #
       # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
       # despite its name. The modern Korean Hangul alphabet is a different block,
       # as is Japanese Hiragana and Katakana. Those alphabets are used to write
       # space-separated words, so they are not treated specially and handled
       # like the all of the other languages.
       if (
           (cp >= 0x4_E00 and cp <= 0x9_FFF)
           or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
           or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
           or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
           or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
           or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
           or (cp >= 0xF_900 and cp <= 0xF_AFF)
           or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
       ):  #
              return True

       return False
def 							_UpperCAmelCase				(UpperCamelCase__     :  str						):
       # word like '180' or '身高' or '神'
       for char in word:
              _A      :       str				    = ord(UpperCamelCase__						)
              if not _is_chinese_char(UpperCamelCase__						):
                     return 0
       return 1
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]						):
       _A      :       List[str]				    = set()

       for token in tokens:
              _A      :       List[Any]				    = len(UpperCamelCase__						) > 1 and is_chinese(UpperCamelCase__						)
              if chinese_word:
                     word_set.add(UpperCamelCase__						)
       _A      :       Dict				    = list(UpperCamelCase__						)
       return word_list
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  set()						):
       if not chinese_word_set:
              return bert_tokens
       _A      :       Any				    = max([len(UpperCamelCase__						) for w in chinese_word_set]						)

       _A      :       List[str]				    = bert_tokens
       _A    ,			_A      :       str				    = 0, len(UpperCamelCase__						)
       while start < end:
              _A      :       Optional[Any]				    = True
              if is_chinese(bert_word[start]						):
                     _A      :       List[str]				    = min(end - start				,							UpperCamelCase__						)
                     for i in range(UpperCamelCase__				,							1				,							-1						):
                            _A      :       Union[str, Any]				    = "".join(bert_word[start : start + i]						)
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1				,							start + i						):
                                          _A      :       Union[str, Any]				    = "##" + bert_word[j]
                                   _A      :       List[Any]				    = start + i
                                   _A      :       Optional[Any]				    = False
                                   break
              if single_word:
                     start += 1
       return bert_word
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  LTP				,							UpperCamelCase__     :  BertTokenizer						):
       _A      :       str				    = []

       for i in range(0				,							len(UpperCamelCase__						)				,							100						):
              _A      :       Dict				    = ltp_tokenizer.pipeline(lines[i : i + 100]				,							tasks=["cws"]						).cws
              _A      :       Union[str, Any]				    = [get_chinese_word(UpperCamelCase__						) for r in res]
              ltp_res.extend(UpperCamelCase__						)
       assert len(UpperCamelCase__						) == len(UpperCamelCase__						)

       _A      :       str				    = []
       for i in range(0				,							len(UpperCamelCase__						)				,							100						):
              _A      :       Tuple				    = bert_tokenizer(lines[i : i + 100]				,							add_special_tokens=UpperCamelCase__				,							truncation=UpperCamelCase__				,							max_length=512						)
              bert_res.extend(res["input_ids"]						)
       assert len(UpperCamelCase__						) == len(UpperCamelCase__						)

       _A      :       List[Any]				    = []
       for input_ids, chinese_word in zip(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       List[Any]				    = []
              for id in input_ids:
                     _A      :       List[Any]				    = bert_tokenizer._convert_id_to_token(UpperCamelCase__						)
                     input_tokens.append(UpperCamelCase__						)
              _A      :       List[str]				    = add_sub_symbol(UpperCamelCase__				,							UpperCamelCase__						)
              _A      :       Dict				    = []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(UpperCamelCase__						):
                     if token[:2] == "##":
                            _A      :       Optional[int]				    = token[2:]
                            # save chinese tokens' pos
                            if len(UpperCamelCase__						) == 1 and _is_chinese_char(ord(UpperCamelCase__						)						):
                                   ref_id.append(UpperCamelCase__						)
              ref_ids.append(UpperCamelCase__						)

       assert len(UpperCamelCase__						) == len(UpperCamelCase__						)

       return ref_ids
def 							_UpperCAmelCase				(UpperCamelCase__     :  Any						):
       # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
       # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
       with open(args.file_name				,							"r"				,							encoding="utf-8"						) as f:
              _A      :       List[Any]				    = f.readlines()
       _A      :       Optional[int]				    = [line.strip() for line in data if len(UpperCamelCase__						) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       _A      :       int				    = LTP(args.ltp						)  # faster in GPU device
       _A      :       Optional[Any]				    = BertTokenizer.from_pretrained(args.bert						)

       _A      :       Optional[int]				    = prepare_ref(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						)

       with open(args.save_path				,							"w"				,							encoding="utf-8"						) as f:
              _A      :       int				    = [json.dumps(UpperCamelCase__						) + "\n" for ref in ref_ids]
              f.writelines(UpperCamelCase__						)


if __name__ == "__main__":
    lowerCAmelCase__      		= argparse.ArgumentParser(description='prepare_chinese_ref')
    parser.add_argument(
        '--file_name',
        required=False,
        type=str,
        default='./resources/chinese-demo.txt',
        help='file need process, same as training data in lm',
    )
    parser.add_argument(
        '--ltp',
        required=False,
        type=str,
        default='./resources/ltp',
        help='resources for LTP tokenizer, usually a path',
    )
    parser.add_argument(
        '--bert',
        required=False,
        type=str,
        default='./resources/robert',
        help='resources for Bert tokenizer',
    )
    parser.add_argument(
        '--save_path',
        required=False,
        type=str,
        default='./resources/ref.txt',
        help='path to save res',
    )

    lowerCAmelCase__      		= parser.parse_args()
    main(args)




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       _A    ,			_A      :       Any				    = image.size
       _A    ,			_A      :       str				    = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
       _A      :       List[str]				    = image.resize((w, h)				,							resample=PIL_INTERPOLATION["lanczos"]						)
       _A      :       Any				    = np.array(UpperCamelCase__						).astype(np.floataa						) / 2_55.0
       _A      :       Optional[Any]				    = image[None].transpose(0				,							3				,							1				,							2						)
       _A      :       Union[str, Any]				    = torch.from_numpy(UpperCamelCase__						)
       return 2.0 * image - 1.0







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						)			->				Optional[int]:
            super().__init__()
            self.register_modules(vqvae=__lowerCamelCase ,						unet=__lowerCamelCase ,						scheduler=__lowerCamelCase)


     @torch.no_grad()
     def __call__(						self ,						__lowerCamelCase = None ,						__lowerCamelCase = 1 ,						__lowerCamelCase = 1_0_0 ,						__lowerCamelCase = 0.0 ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						)			->				Union[Tuple, ImagePipelineOutput]:
            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Tuple				    = 1
            elif isinstance(__lowerCamelCase ,						torch.Tensor):
                   _A      :       Union[str, Any]				    = image.shape[0]
            else:
                   raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase)}")

            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Union[str, Any]				    = preprocess(__lowerCamelCase)

            _A    ,			_A      :       Union[str, Any]				    = image.shape[-2:]

            # in_channels should be 6: 3 for latents, 3 for low resolution image
            _A      :       Optional[Any]				    = (batch_size, self.unet.config.in_channels // 2, height, width)
            _A      :       str				    = next(self.unet.parameters()).dtype

            _A      :       Union[str, Any]				    = randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=self.device ,						dtype=__lowerCamelCase)

            _A      :       List[Any]				    = image.to(device=self.device ,						dtype=__lowerCamelCase)

            # set timesteps and move to the correct device
            self.scheduler.set_timesteps(__lowerCamelCase ,						device=self.device)
            _A      :       Any				    = self.scheduler.timesteps

            # scale the initial noise by the standard deviation required by the scheduler
            _A      :       List[str]				    = latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            _A      :       str				    = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
            _A      :       Optional[int]				    = {}
            if accepts_eta:
                   _A      :       List[Any]				    = eta

            for t in self.progress_bar(__lowerCamelCase):
                   # concat latents and low resolution image in the channel dimension.
                   _A      :       List[Any]				    = torch.cat([latents, image] ,						dim=1)
                   _A      :       str				    = self.scheduler.scale_model_input(__lowerCamelCase ,						__lowerCamelCase)
                   # predict the noise residual
                   _A      :       Any				    = self.unet(__lowerCamelCase ,						__lowerCamelCase).sample
                   # compute the previous noisy sample x_t -> x_t-1
                   _A      :       Optional[int]				    = self.scheduler.step(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase).prev_sample

            # decode the image latents with the VQVAE
            _A      :       Union[str, Any]				    = self.vqvae.decode(__lowerCamelCase).sample
            _A      :       Dict				    = torch.clamp(__lowerCamelCase ,						-1.0 ,						1.0)
            _A      :       Tuple				    = image / 2 + 0.5
            _A      :       int				    = image.cpu().permute(0 ,						2 ,						3 ,						1).numpy()

            if output_type == "pil":
                   _A      :       Optional[int]				    = self.numpy_to_pil(__lowerCamelCase)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=__lowerCamelCase)
