


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,
)


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

_snake_case             =     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
'''






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=8	):


  '''simple docstring'''

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







class 	UpperCAmelCase_					(					lowercase__    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A						,				):






     """simple docstring"""





     super().__init__()

     self.register_modules(
         unet=_a						,				scheduler=_a						,				movq=_a						,				)
     lowerCamelCase :					Dict    =				2 ** (len(self.movq.config.block_out_channels ) - 1)






   def _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A ):






     """simple docstring"""





     if latents is None:
       lowerCamelCase :					Optional[int]    =				randn_tensor(_a						,				generator=_a						,				device=_a						,				dtype=_a )
     else:
       if latents.shape != shape:
         raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
       lowerCamelCase :					Tuple    =				latents.to(_a )

     lowerCamelCase :					str    =				latents * scheduler.init_noise_sigma
     return latents






   def _snake_case    (		self						,				__A=0 ):






     """simple docstring"""





     if is_accelerate_available():
       from accelerate import cpu_offload
     else:
       raise ImportError("Please install accelerate via `pip install accelerate`" )

     lowerCamelCase :					int    =				torch.device(F"""cuda:{gpu_id}""" )

     lowerCamelCase :					Union[str, Any]    =				[
         self.unet,
         self.movq,
     ]
     for cpu_offloaded_model in models:
       if cpu_offloaded_model is not None:
         cpu_offload(_a						,				_a )






   def _snake_case    (		self						,				__A=0 ):






     """simple docstring"""





     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 :					Union[str, Any]    =				torch.device(F"""cuda:{gpu_id}""" )

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

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

     # We'll offload the last model manually.
     lowerCamelCase :					Dict    =				hook






   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def _snake_case    (		self ):






     """simple docstring"""





     if not hasattr(self.unet						,				"_hf_hook" ):
       return self.device
     for module in self.unet.modules():
       if (
           hasattr(_a						,				"_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(_a )
   def __call__(		self						,				__A						,				__A						,				__A = 512						,				__A = 512						,				__A = 100						,				__A = 4.0						,				__A = 1						,				__A = None						,				__A = None						,				__A = "pil"						,				__A = True						,				):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self._execution_device

     lowerCamelCase :					List[Any]    =				guidance_scale > 1.0

     if isinstance(_a						,				_a ):
       lowerCamelCase :					str    =				torch.cat(_a						,				dim=0 )
     lowerCamelCase :					Optional[Any]    =				image_embeds.shape[0] * num_images_per_prompt
     if isinstance(_a						,				_a ):
       lowerCamelCase :					Optional[Any]    =				torch.cat(_a						,				dim=0 )

     if do_classifier_free_guidance:
       lowerCamelCase :					List[str]    =				image_embeds.repeat_interleave(_a						,				dim=0 )
       lowerCamelCase :					List[Any]    =				negative_image_embeds.repeat_interleave(_a						,				dim=0 )

     lowerCamelCase :					Tuple    =				torch.cat([negative_image_embeds, image_embeds]						,				dim=0 ).to(dtype=self.unet.dtype						,				device=_a )

     self.scheduler.set_timesteps(_a						,				device=_a )
     lowerCamelCase :					Any    =				self.scheduler.timesteps

     lowerCamelCase :					List[Any]    =				self.unet.config.in_channels

     lowerCamelCase :					Any    =				downscale_height_and_width(_a						,				_a						,				self.movq_scale_factor )

     # create initial latent
     lowerCamelCase :					List[str]    =				self.prepare_latents(
         (batch_size, num_channels_latents, height, width)						,				image_embeds.dtype						,				_a						,				_a						,				_a						,				self.scheduler						,				)

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

       lowerCamelCase :					Dict    =				{'image_embeds': image_embeds}
       lowerCamelCase :					Any    =				self.unet(
           sample=_a						,				timestep=_a						,				encoder_hidden_states=_a						,				added_cond_kwargs=_a						,				return_dict=_a						,				)[0]

       if do_classifier_free_guidance:
         lowerCamelCase :					str    =				noise_pred.split(latents.shape[1]						,				dim=1 )
         lowerCamelCase :					str    =				noise_pred.chunk(2 )
         lowerCamelCase :					Any    =				variance_pred.chunk(2 )
         lowerCamelCase :					Union[str, Any]    =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
         lowerCamelCase :					Optional[Any]    =				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 :					Dict    =				noise_pred.split(latents.shape[1]						,				dim=1 )

       # compute the previous noisy sample x_t -> x_t-1
       lowerCamelCase :					int    =				self.scheduler.step(
           _a						,				_a						,				_a						,				generator=_a						,				)[0]
     # post-processing
     lowerCamelCase :					int    =				self.movq.decode(_a						,				force_not_quantize=_a )['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 :					str    =				image * 0.5 + 0.5
       lowerCamelCase :					Union[str, Any]    =				image.clamp(0						,				1 )
       lowerCamelCase :					List[str]    =				image.cpu().permute(0						,				2						,				3						,				1 ).float().numpy()

     if output_type == "pil":
       lowerCamelCase :					Tuple    =				self.numpy_to_pil(_a )

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=_a )





'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def 					UpperCAmelCase_		(__a      :    List[Any] ):






   """simple docstring"""

   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 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   for char in word:
      _a				:	Union[str, Any]			=   ord(__a )
      if not _is_chinese_char(__a ):
         return 0
   return 1





def 					UpperCAmelCase_		(__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   set()

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





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    set() ):






   """simple docstring"""

   if not chinese_word_set:
      return bert_tokens
   _a				:	Optional[Any]			=   max([len(__a ) for w in chinese_word_set] )

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





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    LTP ,	__a      :    BertTokenizer ):






   """simple docstring"""

   _a				:	int			=   []

   for i in range(0 ,	len(__a ) ,	1_0_0 ):
      _a				:	Union[str, Any]			=   ltp_tokenizer.seg(lines[i : i + 1_0_0] )[0]
      _a				:	Optional[Any]			=   [get_chinese_word(__a ) for r in res]
      ltp_res.extend(__a )
   assert len(__a ) == len(__a )

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

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

   assert len(__a ) == len(__a )

   return ref_ids





def 					UpperCAmelCase_		(__a      :    Optional[Any] ):






   """simple docstring"""

   with open(args.file_name ,	'r' ,	encoding='utf-8' ) as f:
      _a				:	Dict			=   f.readlines()
   _a				:	int			=   [line.strip() for line in data if len(__a ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   _a				:	int			=   LTP(args.ltp )  # faster in GPU device
   _a				:	Tuple			=   BertTokenizer.from_pretrained(args.bert )

   _a				:	int			=   prepare_ref(__a ,	__a ,	__a )

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


if __name__ == "__main__":
  __lowerCAmelCase         =    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""")

  __lowerCAmelCase         =    parser.parse_args()
  main(args)



