
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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				image.size
      UpperCamelCase__				,UpperCamelCase__    :   Any					=				(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
      UpperCamelCase__    :   Union[str, Any]					=				image.resize((w, h) ,		resample=PIL_INTERPOLATION['''lanczos''']		)
      UpperCamelCase__    :   Optional[Any]					=				np.array(UpperCamelCase__		).astype(np.floataa		) / 255.0
      UpperCamelCase__    :   Tuple					=				image[None].transpose(0 ,		3 ,		1 ,		2		)
      UpperCamelCase__    :   int					=				torch.from_numpy(UpperCamelCase__		)
      return 2.0 * image - 1.0




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						)  ->   Dict:




           """simple docstring"""







           super().__init__()
           self.register_modules(vqvae=__SCREAMING_SNAKE_CASE						,						unet=__SCREAMING_SNAKE_CASE						,						scheduler=__SCREAMING_SNAKE_CASE  )






     @torch.no_grad()
     def __call__(							self						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = 1						,						__SCREAMING_SNAKE_CASE = 1_0_0						,						__SCREAMING_SNAKE_CASE = 0.0						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = "pil"						,						__SCREAMING_SNAKE_CASE = True						,						)  ->   Union[Tuple, ImagePipelineOutput]:




           """simple docstring"""







           if isinstance(__SCREAMING_SNAKE_CASE						,						PIL.Image.Image  ):
                 UpperCamelCase__    :   Union[str, Any]					=				1
           elif isinstance(__SCREAMING_SNAKE_CASE						,						torch.Tensor  ):
                 UpperCamelCase__    :   int					=				image.shape[0]
           else:
                 raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__SCREAMING_SNAKE_CASE  )}'''  )

           if isinstance(__SCREAMING_SNAKE_CASE						,						PIL.Image.Image  ):
                 UpperCamelCase__    :   Tuple					=				preprocess(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__				,UpperCamelCase__    :   List[Any]					=				image.shape[-2:]

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

           UpperCamelCase__    :   str					=				randn_tensor(__SCREAMING_SNAKE_CASE						,						generator=__SCREAMING_SNAKE_CASE						,						device=self.device						,						dtype=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   List[str]					=				image.to(device=self.device						,						dtype=__SCREAMING_SNAKE_CASE  )

           # set timesteps and move to the correct device
           self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE						,						device=self.device  )
           UpperCamelCase__    :   List[str]					=				self.scheduler.timesteps

           # scale the initial noise by the standard deviation required by the scheduler
           UpperCamelCase__    :   Dict					=				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]
           UpperCamelCase__    :   str					=				'''eta''' in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
           UpperCamelCase__    :   Optional[Any]					=				{}
           if accepts_eta:
                 UpperCamelCase__    :   Any					=				eta

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

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

           if output_type == "pil":
                 UpperCamelCase__    :   Union[str, Any]					=				self.numpy_to_pil(__SCREAMING_SNAKE_CASE  )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE  )




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # 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 >= 0x4e_00 and cp <= 0x9f_ff)
          or (cp >= 0x34_00 and cp <= 0x4d_bf)  #
          or (cp >= 0x2_00_00 and cp <= 0x2_a6_df)  #
          or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f)  #
          or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f)  #
          or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af)  #
          or (cp >= 0xf9_00 and cp <= 0xfa_ff)
          or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f)  #
      ):  #
            return True

      return False


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # word like '180' or '身高' or '神'
      for char in word:
            UpperCamelCase__    :   Optional[Any]					=				ord(UpperCamelCase__		)
            if not _is_chinese_char(UpperCamelCase__		):
                  return 0
      return 1


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Dict					=				set()

      for token in tokens:
            UpperCamelCase__    :   Optional[int]					=				len(UpperCamelCase__		) > 1 and is_chinese(UpperCamelCase__		)
            if chinese_word:
                  word_set.add(UpperCamelCase__		)
      UpperCamelCase__    :   List[Any]					=				list(UpperCamelCase__		)
      return word_list


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      if not chinese_word_set:
            return bert_tokens
      UpperCamelCase__    :   str					=				max([len(UpperCamelCase__		) for w in chinese_word_set]		)

      UpperCamelCase__    :   Optional[int]					=				bert_tokens
      UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				0, len(UpperCamelCase__		)
      while start < end:
            UpperCamelCase__    :   Any					=				True
            if is_chinese(bert_word[start]		):
                  UpperCamelCase__    :   Any					=				min(end - start ,		UpperCamelCase__		)
                  for i in range(UpperCamelCase__ ,		1 ,		-1		):
                        UpperCamelCase__    :   List[Any]					=				''''''.join(bert_word[start : start + i]		)
                        if whole_word in chinese_word_set:
                              for j in range(start + 1 ,		start + i		):
                                    UpperCamelCase__    :   Optional[Any]					=				'''##''' + bert_word[j]
                              UpperCamelCase__    :   Dict					=				start + i
                              UpperCamelCase__    :   List[str]					=				False
                              break
            if single_word:
                  start += 1
      return bert_word


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				[]

      for i in range(0 ,		len(UpperCamelCase__		) ,		1_0_0		):
            UpperCamelCase__    :   Optional[Any]					=				ltp_tokenizer.pipeline(lines[i : i + 1_0_0] ,		tasks=['''cws''']		).cws
            UpperCamelCase__    :   List[str]					=				[get_chinese_word(UpperCamelCase__		) for r in res]
            ltp_res.extend(UpperCamelCase__		)
      assert len(UpperCamelCase__		) == len(UpperCamelCase__		)

      UpperCamelCase__    :   str					=				[]
      for i in range(0 ,		len(UpperCamelCase__		) ,		1_0_0		):
            UpperCamelCase__    :   Dict					=				bert_tokenizer(lines[i : i + 1_0_0] ,		add_special_tokens=UpperCamelCase__ ,		truncation=UpperCamelCase__ ,		max_length=5_1_2		)
            bert_res.extend(res['''input_ids''']		)
      assert len(UpperCamelCase__		) == len(UpperCamelCase__		)

      UpperCamelCase__    :   Any					=				[]
      for input_ids, chinese_word in zip(UpperCamelCase__ ,		UpperCamelCase__		):
            UpperCamelCase__    :   Union[str, Any]					=				[]
            for id in input_ids:
                  UpperCamelCase__    :   List[str]					=				bert_tokenizer._convert_id_to_token(UpperCamelCase__		)
                  input_tokens.append(UpperCamelCase__		)
            UpperCamelCase__    :   Optional[Any]					=				add_sub_symbol(UpperCamelCase__ ,		UpperCamelCase__		)
            UpperCamelCase__    :   Any					=				[]
            # 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] == "##":
                        UpperCamelCase__    :   Union[str, Any]					=				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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # 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:
            UpperCamelCase__    :   List[Any]					=				f.readlines()
      UpperCamelCase__    :   Tuple					=				[line.strip() for line in data if len(UpperCamelCase__		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      UpperCamelCase__    :   int					=				LTP(args.ltp		)  # faster in GPU device
      UpperCamelCase__    :   int					=				BertTokenizer.from_pretrained(args.bert		)

      UpperCamelCase__    :   Dict					=				prepare_ref(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)

      with open(args.save_path ,		'''w''' ,		encoding='''utf-8'''		) as f:
            UpperCamelCase__    :   Tuple					=				[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)


