





'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def  _lowerCamelCase				( lowercase						:      List[str]						)	->	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 >= 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  _lowerCamelCase				( lowercase						:      str						)	->	Tuple:
				# word like '180' or '身高' or '神'
				for char in word:
								_a								= ord(lowercase						)
								if not _is_chinese_char(lowercase						):
												return 0
				return 1






def  _lowerCamelCase				( lowercase						:      List[str]						)	->	List[Any]:
				_a								= set()

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






def  _lowerCamelCase				( lowercase						:      List[str]    ,							lowercase						:      set()						)	->	Dict:
				if not chinese_word_set:
								return bert_tokens
				_a								= max([len(lowercase						) for w in chinese_word_set]						)

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






def  _lowerCamelCase				( lowercase						:      List[str]    ,							lowercase						:      LTP    ,							lowercase						:      BertTokenizer						)	->	int:
				_a								= []

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

				_a								= []
				for i in range(0    ,							len(lowercase						)    ,							100						):
								_a								= 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						)

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

				_a								= prepare_ref(lowercase    ,							lowercase    ,							lowercase						)

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


if __name__ == "__main__":
					lowerCAmelCase_					:    Tuple      	=			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_					:    Tuple      	=			parser.parse_args()
					main(args)






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

_snake_case										=       "\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"







@dataclass
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						42







class 						lowercase							(   UpperCamelCase__		):

     def __init__(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     )						->							List[Any]:
           super().__init__()

           self.register_modules(
               prior=_a		,     image_encoder=_a		,     image_processor=_a		,     scheduler=_a		,     renderer=_a		,     )

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							str:
           if latents is None:
                 _A							:     str			=						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}'''				)
                 _A							:     Union[str, Any]			=						latents.to(_a				)

           _A							:     int			=						latents * scheduler.init_noise_sigma
           return latents

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

           _A							:     str			=						torch.device(F'''cuda:{gpu_id}'''				)

           _A							:     Any			=						[self.image_encoder, self.prior]
           for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                       cpu_offload(_a		,     _a				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           if self.device != torch.device("""meta"""				) or not hasattr(self.image_encoder		,     """_hf_hook"""				):
                 return self.device
           for module in self.image_encoder.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

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     )						->							Tuple:
           if isinstance(_a		,     _a				) and isinstance(image[0]		,     torch.Tensor				):
                 _A							:     int			=						torch.cat(_a		,     axis=0				) if image[0].ndim == 4 else torch.stack(_a		,     axis=0				)

           if not isinstance(_a		,     torch.Tensor				):
                 _A							:     Dict			=						self.image_processor(_a		,     return_tensors="""pt"""				).pixel_values[0].unsqueeze(0				)

           _A							:     int			=						image.to(dtype=self.image_encoder.dtype		,     device=_a				)

           _A							:     List[Any]			=						self.image_encoder(_a				)["""last_hidden_state"""]
           _A							:     List[Any]			=						image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

           _A							:     Dict			=						image_embeds.repeat_interleave(_a		,     dim=0				)

           if do_classifier_free_guidance:
                 _A							:     str			=						torch.zeros_like(_a				)

                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 _A							:     List[str]			=						torch.cat([negative_image_embeds, image_embeds]				)

           return image_embeds



     @torch.no_grad()
     @replace_example_docstring(_a				)
     def __call__(  self		,     _a		,     _a = 1		,     _a = 25		,     _a = None		,     _a = None		,     _a = 4.0		,     _a = 64		,     _a = "pil"		,     _a = True		,     )						->							Union[str, Any]:

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

           _A							:     Optional[int]			=						self._execution_device

           _A							:     Tuple			=						batch_size * num_images_per_prompt

           _A							:     List[Any]			=						guidance_scale > 1.0
           _A							:     Optional[Any]			=						self._encode_image(_a		,     _a		,     _a		,     _a				)

           # prior

           self.scheduler.set_timesteps(_a		,     device=_a				)
           _A							:     Optional[int]			=						self.scheduler.timesteps

           _A							:     List[str]			=						self.prior.config.num_embeddings
           _A							:     int			=						self.prior.config.embedding_dim

           _A							:     Optional[Any]			=						self.prepare_latents(
               (batch_size, num_embeddings * embedding_dim)		,     image_embeds.dtype		,     _a		,     _a		,     _a		,     self.scheduler		,     )

           # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
           _A							:     List[Any]			=						latents.reshape(latents.shape[0]		,     _a		,     _a				)

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

                 _A							:     Tuple			=						self.prior(
                     _a		,     timestep=_a		,     proj_embedding=_a		,     ).predicted_image_embedding

                 # remove the variance
                 _A			,   _A							:     Optional[Any]			=						noise_pred.split(
                     scaled_model_input.shape[2]		,     dim=2				)  # batch_size, num_embeddings, embedding_dim

                 if do_classifier_free_guidance is not None:
                       _A			,   _A							:     Dict			=						noise_pred.chunk(2				)
                       _A							:     Tuple			=						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                 _A							:     int			=						self.scheduler.step(
                     _a		,     timestep=_a		,     sample=_a		,     ).prev_sample

           if output_type == "latent":
                 return ShapEPipelineOutput(images=_a				)

           _A							:     List[str]			=						[]
           for i, latent in enumerate(_a				):
                 print()
                 _A							:     List[str]			=						self.renderer.decode(
                     latent[None, :]		,     _a		,     size=_a		,     ray_batch_size=4096		,     n_coarse_samples=64		,     n_fine_samples=128		,     )

                 images.append(_a				)

           _A							:     List[Any]			=						torch.stack(_a				)

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

           _A							:     List[str]			=						images.cpu().numpy()

           if output_type == "pil":
                 _A							:     List[Any]			=						[self.numpy_to_pil(_a				) for image in images]

           # Offload last model to CPU
           if hasattr(self		,     """final_offload_hook"""				) and self.final_offload_hook is not None:
                 self.final_offload_hook.offload()

           if not return_dict:
                 return (images,)

           return ShapEPipelineOutput(images=_a				)

