





'''simple docstring'''


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)  # pylint: disable=invalid-name

_SCREAMING_SNAKE_CASE									=				"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"


def 						__a(SCREAMING_SNAKE_CASE_	:       Tuple		,							SCREAMING_SNAKE_CASE_	:       Optional[int]		,							SCREAMING_SNAKE_CASE_	:       Tuple=8     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    _lowerCAmelCase						      =   width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor





class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  )     ->				Optional[int]:
     super().__init__()

     self.register_modules(
         unet=_lowerCAmelCase      ,  scheduler=_lowerCAmelCase      ,  movq=_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   2 ** (len(self.movq.config.block_out_channels		) - 1)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     if latents is None:
         _lowerCAmelCase						      =   randn_tensor(_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  device=_lowerCAmelCase      ,  dtype=_lowerCAmelCase		)
     else:
         if latents.shape != shape:
             raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
         _lowerCAmelCase						      =   latents.to(_lowerCAmelCase		)

     _lowerCAmelCase						      =   latents * scheduler.init_noise_sigma
     return latents




 def 				_snake_case (						self      ,  _lowerCAmelCase=0		)     ->				Optional[Any]:
     if is_accelerate_available():
         from accelerate import cpu_offload
     else:
         raise ImportError("Please install accelerate via `pip install accelerate`"		)

     _lowerCAmelCase						      =   torch.device(f'''cuda:{gpu_id}'''		)

     _lowerCAmelCase						      =   [
         self.unet,
         self.movq,
     ]
     for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
             cpu_offload(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase=0		)     ->				Tuple:
     if is_accelerate_available() and is_accelerate_version(">="      ,  "0.17.0.dev0"		):
         from accelerate import cpu_offload_with_hook
     else:
         raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."		)

     _lowerCAmelCase						      =   torch.device(f'''cuda:{gpu_id}'''		)

     if self.device.type != "cpu":
         self.to("cpu"      ,  silence_dtype_warnings=_lowerCAmelCase		)
         torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

     _lowerCAmelCase						      =   None
     for cpu_offloaded_model in [self.unet, self.movq]:
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   cpu_offload_with_hook(_lowerCAmelCase      ,  _lowerCAmelCase      ,  prev_module_hook=_lowerCAmelCase		)

     # We'll offload the last model manually.
     _lowerCAmelCase						      =   hook




 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 				_snake_case (						self		)     ->				int:
     if not hasattr(self.unet      ,  "_hf_hook"		):
         return self.device
     for module in self.unet.modules():
         if (
             hasattr(_lowerCAmelCase      ,  "_hf_hook"		)
             and hasattr(module._hf_hook      ,  "execution_device"		)
             and module._hf_hook.execution_device is not None
         ):
             return torch.device(module._hf_hook.execution_device		)
     return self.device





 @torch.no_grad()
 @replace_example_docstring(_lowerCAmelCase		)
 def __call__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = 512      ,  _lowerCAmelCase = 512      ,  _lowerCAmelCase = 100      ,  _lowerCAmelCase = 4.0      ,  _lowerCAmelCase = 1      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = "pil"      ,  _lowerCAmelCase = True      ,  )     ->				List[str]:
     _lowerCAmelCase						      =   self._execution_device

     _lowerCAmelCase						      =   guidance_scale > 1.0

     if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		):
         _lowerCAmelCase						      =   torch.cat(_lowerCAmelCase      ,  dim=0		)
     if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		):
         _lowerCAmelCase						      =   torch.cat(_lowerCAmelCase      ,  dim=0		)
     if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		):
         _lowerCAmelCase						      =   torch.cat(_lowerCAmelCase      ,  dim=0		)

     _lowerCAmelCase						      =   image_embeds.shape[0] * num_images_per_prompt

     if do_classifier_free_guidance:
         _lowerCAmelCase						      =   image_embeds.repeat_interleave(_lowerCAmelCase      ,  dim=0		)
         _lowerCAmelCase						      =   negative_image_embeds.repeat_interleave(_lowerCAmelCase      ,  dim=0		)
         _lowerCAmelCase						      =   hint.repeat_interleave(_lowerCAmelCase      ,  dim=0		)

     _lowerCAmelCase						      =   torch.cat([negative_image_embeds, image_embeds]      ,  dim=0		).to(dtype=self.unet.dtype      ,  device=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.cat([hint, hint]      ,  dim=0		).to(dtype=self.unet.dtype      ,  device=_lowerCAmelCase		)
     self.scheduler.set_timesteps(_lowerCAmelCase      ,  device=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.scheduler.timesteps

     _lowerCAmelCase						      =   self.movq.config.latent_channels

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   downscale_height_and_width(_lowerCAmelCase      ,  _lowerCAmelCase      ,  self.movq_scale_factor		)

     # create initial latent
     _lowerCAmelCase						      =   self.prepare_latents(
         (batch_size, num_channels_latents, height, width)      ,  image_embeds.dtype      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  self.scheduler      ,  )

     for i, t in enumerate(self.progress_bar(_lowerCAmelCase		)		):
         # expand the latents if we are doing classifier free guidance
         _lowerCAmelCase						      =   torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

         _lowerCAmelCase						      =   {"image_embeds": image_embeds, "hint": hint}
         _lowerCAmelCase						      =   self.unet(
             sample=_lowerCAmelCase      ,  timestep=_lowerCAmelCase      ,  encoder_hidden_states=_lowerCAmelCase      ,  added_cond_kwargs=_lowerCAmelCase      ,  return_dict=_lowerCAmelCase      ,  )[0]

         if do_classifier_free_guidance:
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   noise_pred.split(latents.shape[1]      ,  dim=1		)
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   noise_pred.chunk(2		)
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   variance_pred.chunk(2		)
             _lowerCAmelCase						      =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             _lowerCAmelCase						      =   torch.cat([noise_pred, variance_pred_text]      ,  dim=1		)

         if not (
             hasattr(self.scheduler.config      ,  "variance_type"		)
             and self.scheduler.config.variance_type in ["learned", "learned_range"]
         ):
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   noise_pred.split(latents.shape[1]      ,  dim=1		)

         # compute the previous noisy sample x_t -> x_t-1
         _lowerCAmelCase						      =   self.scheduler.step(
             _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  )[0]
     # post-processing
     _lowerCAmelCase						      =   self.movq.decode(_lowerCAmelCase      ,  force_not_quantize=_lowerCAmelCase		)["sample"]

     if output_type not in ["pt", "np", "pil"]:
         raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''		)

     if output_type in ["np", "pil"]:
         _lowerCAmelCase						      =   image * 0.5 + 0.5
         _lowerCAmelCase						      =   image.clamp(0      ,  1		)
         _lowerCAmelCase						      =   image.cpu().permute(0      ,  2      ,  3      ,  1		).float().numpy()

     if output_type == "pil":
         _lowerCAmelCase						      =   self.numpy_to_pil(_lowerCAmelCase		)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_lowerCAmelCase		)




"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def       A_  (				__lowercase	):
 # 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_E_0_0 and cp <= 0X9_F_F_F)
     or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F)  #
     or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F)  #
     or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F)  #
     or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F)  #
     or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F)  #
     or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
     or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F)  #
 ):  #
  return True

 return False




def       A_  (				__lowercase	):
 # word like '180' or '身高' or '神'
 for char in word:
  UpperCamelCase_     :					Union[str, Any]   	=ord(__lowercase	)
  if not _is_chinese_char(__lowercase	):
   return 0
 return 1




def       A_  (				__lowercase	):
 UpperCamelCase_     :					List[str]   	=set()

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




def       A_  (				__lowercase						,	__lowercase	):
 if not chinese_word_set:
  return bert_tokens
 UpperCamelCase_     :					List[str]   	=max([len(__lowercase	) for w in chinese_word_set]	)

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




def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Tuple   	=[]

 for i in range(0						,	len(__lowercase	)						,	1_00	):
  UpperCamelCase_     :					Union[str, Any]   	=ltp_tokenizer.seg(lines[i : i + 1_00]	)[0]
  UpperCamelCase_     :					int   	=[get_chinese_word(__lowercase	) for r in res]
  ltp_res.extend(__lowercase	)
 assert len(__lowercase	) == len(__lowercase	)

 UpperCamelCase_     :					Dict   	=[]
 for i in range(0						,	len(__lowercase	)						,	1_00	):
  UpperCamelCase_     :					int   	=bert_tokenizer(lines[i : i + 1_00]						,	add_special_tokens=__lowercase						,	truncation=__lowercase						,	max_length=5_12	)
  bert_res.extend(res['input_ids']	)
 assert len(__lowercase	) == len(__lowercase	)

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

 assert len(__lowercase	) == len(__lowercase	)

 return ref_ids




def       A_  (				__lowercase	):
 # 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_     :					Tuple   	=f.readlines()
 UpperCamelCase_     :					Optional[int]   	=[line.strip() for line in data if len(__lowercase	) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
 UpperCamelCase_     :					Optional[Any]   	=LTP(args.ltp	)  # faster in GPU device
 UpperCamelCase_     :					Dict   	=BertTokenizer.from_pretrained(args.bert	)

 UpperCamelCase_     :					int   	=prepare_ref(__lowercase						,	__lowercase						,	__lowercase	)

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


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

 __SCREAMING_SNAKE_CASE							=     parser.parse_args()
 main(args)
