





from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			["""vqvae"""]






  def __init__(    self			:		Optional[int] ,  lowerCAmelCase_			:		AutoencoderKL ,  lowerCAmelCase_			:		UNetaDConditionModel ,  lowerCAmelCase_			:		Mel ,  lowerCAmelCase_			:		Union[DDIMScheduler, DDPMScheduler] ,  )							->						Any:
     super().__init__()
     self.register_modules(unet=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_ ,  mel=lowerCAmelCase_ ,  vqvae=lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						int:
     return 5_0 if isinstance(self.scheduler ,  lowerCAmelCase_					) else 1_0_0_0






  @torch.no_grad()
  def __call__(    self			:		List[Any] ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		str = None ,  lowerCAmelCase_			:		np.ndarray = None ,  lowerCAmelCase_			:		int = 0 ,  lowerCAmelCase_			:		int = 0 ,  lowerCAmelCase_			:		int = None ,  lowerCAmelCase_			:		torch.Generator = None ,  lowerCAmelCase_			:		float = 0 ,  lowerCAmelCase_			:		float = 0 ,  lowerCAmelCase_			:		torch.Generator = None ,  lowerCAmelCase_			:		float = 0 ,  lowerCAmelCase_			:		torch.Tensor = None ,  lowerCAmelCase_			:		torch.Tensor = None ,  lowerCAmelCase_			:		int=True ,  )							->						Union[
      Union[AudioPipelineOutput, ImagePipelineOutput],
      Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
  ]:
     __lowerCAmelCase							     =     steps or self.get_default_steps()
     self.scheduler.set_timesteps(lowerCAmelCase_					)
     __lowerCAmelCase							     =     step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size					) == int:
        __lowerCAmelCase							     =     (self.unet.config.sample_size, self.unet.config.sample_size)
     if noise is None:
        __lowerCAmelCase							     =     randn_tensor(
            (
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size[0],
                self.unet.config.sample_size[1],
            ) ,  generator=lowerCAmelCase_ ,  device=self.device ,  )
     __lowerCAmelCase							     =     noise
     __lowerCAmelCase							     =     None

     if audio_file is not None or raw_audio is not None:
        self.mel.load_audio(lowerCAmelCase_ ,  lowerCAmelCase_					)
        __lowerCAmelCase							     =     self.mel.audio_slice_to_image(lowerCAmelCase_					)
        __lowerCAmelCase							     =     np.frombuffer(input_image.tobytes() ,  dtype='uint8'					).reshape(
            (input_image.height, input_image.width)					)
        __lowerCAmelCase							     =     (input_image / 2_5_5) * 2 - 1
        __lowerCAmelCase							     =     torch.tensor(input_image[np.newaxis, :, :] ,  dtype=torch.float					).to(self.device					)

        if self.vqvae is not None:
           __lowerCAmelCase							     =     self.vqvae.encode(torch.unsqueeze(lowerCAmelCase_ ,  0					)					).latent_dist.sample(
               generator=lowerCAmelCase_					)[0]
           __lowerCAmelCase							     =     self.vqvae.config.scaling_factor * input_images

        if start_step > 0:
           __lowerCAmelCase							     =     self.scheduler.add_noise(lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.scheduler.timesteps[start_step - 1]					)

        __lowerCAmelCase							     =     (
            self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
        )
        __lowerCAmelCase							     =     int(mask_start_secs * pixels_per_second					)
        __lowerCAmelCase							     =     int(mask_end_secs * pixels_per_second					)
        __lowerCAmelCase							     =     self.scheduler.add_noise(lowerCAmelCase_ ,  lowerCAmelCase_ ,  torch.tensor(self.scheduler.timesteps[start_step:]					)					)

     for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]					)					):
        if isinstance(self.unet ,  lowerCAmelCase_					):
           __lowerCAmelCase							     =     self.unet(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)['sample']
        else:
           __lowerCAmelCase							     =     self.unet(lowerCAmelCase_ ,  lowerCAmelCase_					)['sample']

        if isinstance(self.scheduler ,  lowerCAmelCase_					):
           __lowerCAmelCase							     =     self.scheduler.step(
               model_output=lowerCAmelCase_ ,  timestep=lowerCAmelCase_ ,  sample=lowerCAmelCase_ ,  eta=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  )['prev_sample']
        else:
           __lowerCAmelCase							     =     self.scheduler.step(
               model_output=lowerCAmelCase_ ,  timestep=lowerCAmelCase_ ,  sample=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  )['prev_sample']

        if mask is not None:
           if mask_start > 0:
              __lowerCAmelCase							     =     mask[:, step, :, :mask_start]
           if mask_end > 0:
              __lowerCAmelCase							     =     mask[:, step, :, -mask_end:]

     if self.vqvae is not None:
        # 0.18215 was scaling factor used in training to ensure unit variance
        __lowerCAmelCase							     =     1 / self.vqvae.config.scaling_factor * images
        __lowerCAmelCase							     =     self.vqvae.decode(lowerCAmelCase_					)['sample']

     __lowerCAmelCase							     =     (images / 2 + 0.5).clamp(0 ,  1					)
     __lowerCAmelCase							     =     images.cpu().permute(0 ,  2 ,  3 ,  1					).numpy()
     __lowerCAmelCase							     =     (images * 2_5_5).round().astype('uint8'					)
     __lowerCAmelCase							     =     list(
         (Image.fromarray(_[:, :, 0]					) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(lowerCAmelCase_ ,  mode='RGB'					).convert('L'					) for _ in images)					)

     __lowerCAmelCase							     =     [self.mel.image_to_audio(lowerCAmelCase_					) for _ in images]
     if not return_dict:
        return images, (self.mel.get_sample_rate(), audios)

     return BaseOutput(**AudioPipelineOutput(np.array(lowerCAmelCase_					)[:, np.newaxis, :]					) ,  **ImagePipelineOutput(lowerCAmelCase_					)					)






  @torch.no_grad()
  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		List[Image.Image] ,  lowerCAmelCase_			:		int = 5_0					)							->						np.ndarray:
     assert isinstance(self.scheduler ,  lowerCAmelCase_					)
     self.scheduler.set_timesteps(lowerCAmelCase_					)
     __lowerCAmelCase							     =     np.array(
         [np.frombuffer(image.tobytes() ,  dtype='uint8'					).reshape((1, image.height, image.width)					) for image in images]					)
     __lowerCAmelCase							     =     (sample / 2_5_5) * 2 - 1
     __lowerCAmelCase							     =     torch.Tensor(lowerCAmelCase_					).to(self.device					)

     for t in self.progress_bar(torch.flip(self.scheduler.timesteps ,  (0,)					)					):
        __lowerCAmelCase							     =     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
        __lowerCAmelCase							     =     self.scheduler.alphas_cumprod[t]
        __lowerCAmelCase							     =     (
            self.scheduler.alphas_cumprod[prev_timestep]
            if prev_timestep >= 0
            else self.scheduler.final_alpha_cumprod
        )
        __lowerCAmelCase							     =     1 - alpha_prod_t
        __lowerCAmelCase							     =     self.unet(lowerCAmelCase_ ,  lowerCAmelCase_					)['sample']
        __lowerCAmelCase							     =     (1 - alpha_prod_t_prev) ** 0.5 * model_output
        __lowerCAmelCase							     =     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
        __lowerCAmelCase							     =     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

     return sample






  @staticmethod
  def     lowercase		(    lowerCAmelCase_			:		torch.Tensor ,  lowerCAmelCase_			:		torch.Tensor ,  lowerCAmelCase_			:		float					)							->						torch.Tensor:
     __lowerCAmelCase							     =     acos(torch.dot(torch.flatten(lowerCAmelCase_					) ,  torch.flatten(lowerCAmelCase_					)					) / torch.norm(lowerCAmelCase_					) / torch.norm(lowerCAmelCase_					)					)
     return sin((1 - alpha) * theta					) * xa / sin(lowerCAmelCase_					) + sin(alpha * theta					) * xa / sin(lowerCAmelCase_					)








import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def   a_   (			lowerCAmelCase_				:      Optional[Any]     ):
   # 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 >= 0X4e00 and cp <= 0X9fff)
       or (cp >= 0X3400 and cp <= 0X4dbf)  #
       or (cp >= 0X20000 and cp <= 0X2a6df)  #
       or (cp >= 0X2a700 and cp <= 0X2b73f)  #
       or (cp >= 0X2b740 and cp <= 0X2b81f)  #
       or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
       or (cp >= 0Xf900 and cp <= 0Xfaff)
       or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
   ):  #
      return True

   return False

def   a_   (			lowerCAmelCase_				:      str     ):
   # word like '180' or '身高' or '神'
   for char in word:
      __lowerCAmelCase							     =     ord(lowerCAmelCase_     )
      if not _is_chinese_char(lowerCAmelCase_     ):
         return 0
   return 1

def   a_   (			lowerCAmelCase_				:      List[str]     ):
   __lowerCAmelCase							     =     set()

   for token in tokens:
      __lowerCAmelCase							     =     len(lowerCAmelCase_     ) > 1 and is_chinese(lowerCAmelCase_     )
      if chinese_word:
         word_set.add(lowerCAmelCase_     )
   __lowerCAmelCase							     =     list(lowerCAmelCase_     )
   return word_list

def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      set()     ):
   if not chinese_word_set:
      return bert_tokens
   __lowerCAmelCase							     =     max([len(lowerCAmelCase_     ) for w in chinese_word_set]     )

   __lowerCAmelCase							     =     bert_tokens
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     0, len(lowerCAmelCase_     )
   while start < end:
      __lowerCAmelCase							     =     True
      if is_chinese(bert_word[start]     ):
         __lowerCAmelCase							     =     min(end - start,     lowerCAmelCase_     )
         for i in range(lowerCAmelCase_,     1,     -1     ):
            __lowerCAmelCase							     =     ''.join(bert_word[start : start + i]     )
            if whole_word in chinese_word_set:
               for j in range(start + 1,     start + i     ):
                  __lowerCAmelCase							     =     '##' + bert_word[j]
               __lowerCAmelCase							     =     start + i
               __lowerCAmelCase							     =     False
               break
      if single_word:
         start += 1
   return bert_word

def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      LTP,     lowerCAmelCase_				:      BertTokenizer     ):
   __lowerCAmelCase							     =     []

   for i in range(0,     len(lowerCAmelCase_     ),     100     ):
      __lowerCAmelCase							     =     ltp_tokenizer.pipeline(lines[i : i + 100],     tasks=['cws']     ).cws
      __lowerCAmelCase							     =     [get_chinese_word(lowerCAmelCase_     ) for r in res]
      ltp_res.extend(lowerCAmelCase_     )
   assert len(lowerCAmelCase_     ) == len(lowerCAmelCase_     )

   __lowerCAmelCase							     =     []
   for i in range(0,     len(lowerCAmelCase_     ),     100     ):
      __lowerCAmelCase							     =     bert_tokenizer(lines[i : i + 100],     add_special_tokens=lowerCAmelCase_,     truncation=lowerCAmelCase_,     max_length=512     )
      bert_res.extend(res['input_ids']     )
   assert len(lowerCAmelCase_     ) == len(lowerCAmelCase_     )

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

   assert len(lowerCAmelCase_     ) == len(lowerCAmelCase_     )

   return ref_ids

def   a_   (			lowerCAmelCase_				:      int     ):
   # 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:
      __lowerCAmelCase							     =     f.readlines()
   __lowerCAmelCase							     =     [line.strip() for line in data if len(lowerCAmelCase_     ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   __lowerCAmelCase							     =     LTP(args.ltp     )  # faster in GPU device
   __lowerCAmelCase							     =     BertTokenizer.from_pretrained(args.bert     )

   __lowerCAmelCase							     =     prepare_ref(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

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


if __name__ == "__main__":
     _snake_case  :   int						   =  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',
     )

     _snake_case  :   List[str]						   =  parser.parse_args()
     main(args)

