





"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


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

UpperCamelCase_									="""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase=8       ):
						_UpperCamelCase      : Dict         =   height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						_UpperCamelCase      : List[Any]         =   width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor





def 	a_    (							_lowercase	,						_lowercase=512	,						_lowercase=512       ):
						_UpperCamelCase      : List[str]         =   pil_image.resize((w, h)	,						resample=Image.BICUBIC	,						reducing_gap=1       )
						_UpperCamelCase      : Tuple         =   np.array(pil_image.convert('''RGB'''       )       )
						_UpperCamelCase      : Dict         =   arr.astype(np.floataa       ) / 127.5 - 1
						_UpperCamelCase      : Optional[int]         =   np.transpose(_lowercase	,						[2, 0, 1]       )
						_UpperCamelCase      : List[Any]         =   torch.from_numpy(_lowercase       ).unsqueeze(0       )
						return image






class 			_a						(				_lowerCAmelCase    ):






				def __init__(			self		:   int,			lowerCAmelCase__		:   UNetaDConditionModel,			lowerCAmelCase__		:   DDPMScheduler,			lowerCAmelCase__		:   VQModel,			)     ->				Union[str, Any]:




										'''simple docstring'''



										super().__init__()

										self.register_modules(
										    unet=lowerCAmelCase__,			scheduler=lowerCAmelCase__,			movq=lowerCAmelCase__,			)
										_UpperCamelCase      : List[str]         =   2 ** (len(self.movq.config.block_out_channels							) - 1)






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   List[Any],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Union[str, Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   min(int(num_inference_steps * strength							),			lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   max(num_inference_steps - init_timestep,			0							)
										_UpperCamelCase      : int         =   self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   Dict,			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   Tuple=None							)     ->				Optional[Any]:




										'''simple docstring'''



										if not isinstance(lowerCAmelCase__,			(torch.Tensor, PIL.Image.Image, list)							):
																raise ValueError(
																    f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase__							)}"""							)

										_UpperCamelCase      : int         =   image.to(device=lowerCAmelCase__,			dtype=lowerCAmelCase__							)

										_UpperCamelCase      : str         =   batch_size * num_images_per_prompt

										if image.shape[1] == 4:
																_UpperCamelCase      : Any         =   image

										else:
																if isinstance(lowerCAmelCase__,			lowerCAmelCase__							) and len(lowerCAmelCase__							) != batch_size:
																						raise ValueError(
																						    f"""You have passed a list of generators of length {len(lowerCAmelCase__							)}, but requested an effective batch"""
																						    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

																elif isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																						_UpperCamelCase      : Optional[Any]         =   [
																						    self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(lowerCAmelCase__							)
																						]
																						_UpperCamelCase      : List[str]         =   torch.cat(lowerCAmelCase__,			dim=0							)
																else:
																						_UpperCamelCase      : Dict         =   self.movq.encode(lowerCAmelCase__							).latent_dist.sample(lowerCAmelCase__							)

																_UpperCamelCase      : List[str]         =   self.movq.config.scaling_factor * init_latents

										_UpperCamelCase      : Any         =   torch.cat([init_latents],			dim=0							)

										_UpperCamelCase      : int         =   init_latents.shape
										_UpperCamelCase      : Any         =   randn_tensor(lowerCAmelCase__,			generator=lowerCAmelCase__,			device=lowerCAmelCase__,			dtype=lowerCAmelCase__							)

										# get latents
										_UpperCamelCase      : Any         =   self.scheduler.add_noise(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							)

										_UpperCamelCase      : int         =   init_latents

										return latents






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   str=0							)     ->				Dict:




										'''simple docstring'''



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

										_UpperCamelCase      : int         =   torch.device(f"""cuda:{gpu_id}"""							)

										_UpperCamelCase      : Tuple         =   [
										    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		:   List[str],			lowerCAmelCase__		:   Tuple=0							)     ->				Union[str, Any]:




										'''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.'''							)

										_UpperCamelCase      : int         =   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)

										_UpperCamelCase      : int         =   None
										for cpu_offloaded_model in [self.unet, self.movq]:
																_UpperCamelCase       ,							_UpperCamelCase      : List[Any]         =   cpu_offload_with_hook(lowerCAmelCase__,			lowerCAmelCase__,			prev_module_hook=lowerCAmelCase__							)

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






				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def     snake_case     (			self		:   Optional[Any]							)     ->				Tuple:




										'''simple docstring'''



										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		:   int,			lowerCAmelCase__		:   Union[torch.FloatTensor, List[torch.FloatTensor]],			lowerCAmelCase__		:   Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],			lowerCAmelCase__		:   Union[torch.FloatTensor, List[torch.FloatTensor]],			lowerCAmelCase__		:   int = 5_1_2,			lowerCAmelCase__		:   int = 5_1_2,			lowerCAmelCase__		:   int = 1_0_0,			lowerCAmelCase__		:   float = 4.0,			lowerCAmelCase__		:   float = 0.3,			lowerCAmelCase__		:   int = 1,			lowerCAmelCase__		:   Optional[Union[torch.Generator, List[torch.Generator]]] = None,			lowerCAmelCase__		:   Optional[str] = "pil",			lowerCAmelCase__		:   bool = True,			)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   self._execution_device

										_UpperCamelCase      : Union[str, Any]         =   guidance_scale > 1.0

										if isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																_UpperCamelCase      : int         =   torch.cat(lowerCAmelCase__,			dim=0							)
										_UpperCamelCase      : List[Any]         =   image_embeds.shape[0]
										if isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																_UpperCamelCase      : Optional[int]         =   torch.cat(lowerCAmelCase__,			dim=0							)

										if do_classifier_free_guidance:
																_UpperCamelCase      : Tuple         =   image_embeds.repeat_interleave(lowerCAmelCase__,			dim=0							)
																_UpperCamelCase      : int         =   negative_image_embeds.repeat_interleave(lowerCAmelCase__,			dim=0							)

										_UpperCamelCase      : Dict         =   torch.cat([negative_image_embeds, image_embeds],			dim=0							).to(dtype=self.unet.dtype,			device=lowerCAmelCase__							)

										if not isinstance(lowerCAmelCase__,			lowerCAmelCase__							):
																_UpperCamelCase      : Tuple         =   [image]
										if not all(isinstance(lowerCAmelCase__,			(PIL.Image.Image, torch.Tensor)							) for i in image							):
																raise ValueError(
																    f"""Input is in incorrect format: {[type(lowerCAmelCase__							) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""							)

										_UpperCamelCase      : Union[str, Any]         =   torch.cat([prepare_image(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							) for i in image],			dim=0							)
										_UpperCamelCase      : str         =   image.to(dtype=image_embeds.dtype,			device=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   self.movq.encode(lowerCAmelCase__							)['''latents''']
										_UpperCamelCase      : Optional[Any]         =   latents.repeat_interleave(lowerCAmelCase__,			dim=0							)
										self.scheduler.set_timesteps(lowerCAmelCase__,			device=lowerCAmelCase__							)
										_UpperCamelCase       ,							_UpperCamelCase      : List[Any]         =   self.get_timesteps(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__							)
										_UpperCamelCase      : str         =   timesteps[:1].repeat(batch_size * num_images_per_prompt							)
										_UpperCamelCase       ,							_UpperCamelCase      : Tuple         =   downscale_height_and_width(lowerCAmelCase__,			lowerCAmelCase__,			self.movq_scale_factor							)
										_UpperCamelCase      : Optional[Any]         =   self.prepare_latents(
										    lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			image_embeds.dtype,			lowerCAmelCase__,			lowerCAmelCase__							)
										for i, t in enumerate(self.progress_bar(lowerCAmelCase__							)							):
																# expand the latents if we are doing classifier free guidance
																_UpperCamelCase      : int         =   torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

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

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

																# compute the previous noisy sample x_t -> x_t-1
																_UpperCamelCase      : List[Any]         =   self.scheduler.step(
																    lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			generator=lowerCAmelCase__,			)[0]

										# post-processing
										_UpperCamelCase      : int         =   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"]:
																_UpperCamelCase      : List[Any]         =   image * 0.5 + 0.5
																_UpperCamelCase      : Dict         =   image.clamp(0,			1							)
																_UpperCamelCase      : Tuple         =   image.cpu().permute(0,			2,			3,			1							).float().numpy()

										if output_type == "pil":
																_UpperCamelCase      : Any         =   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.models.bert.tokenization_bert 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 >= 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_    (							_lowercase       ):
						# word like '180' or '身高' or '神'
						for char in word:
												_UpperCamelCase      : Dict         =   ord(_lowercase       )
												if not _is_chinese_char(_lowercase       ):
																		return 0
						return 1





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   set()

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





def 	a_    (							_lowercase	,						_lowercase       ):
						if not chinese_word_set:
												return bert_tokens
						_UpperCamelCase      : Tuple         =   max([len(_lowercase       ) for w in chinese_word_set]       )

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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   []

						for i in range(0	,						len(_lowercase       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   ltp_tokenizer.pipeline(lines[i : i + 100]	,						tasks=['''cws''']       ).cws
												_UpperCamelCase      : Optional[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       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   bert_tokenizer(lines[i : i + 100]	,						add_special_tokens=_lowercase	,						truncation=_lowercase	,						max_length=512       )
												bert_res.extend(res['''input_ids''']       )
						assert len(_lowercase       ) == len(_lowercase       )

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

						_UpperCamelCase      : List[str]         =   prepare_ref(_lowercase	,						_lowercase	,						_lowercase       )

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


if __name__ == "__main__":
						UpperCamelCase_									=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""",
						)

						UpperCamelCase_									=parser.parse_args()
						main(args)



