


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def    __lowerCamelCase   (      A__						: Dict					)     ->    Tuple:


   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    __lowerCamelCase   (      A__						: Union[str, Any]					)     ->    Any:


   for char in word:
      lowerCamelCase_						:   Optional[Any]		= ord(a__					)
      if not _is_chinese_char(a__					):
         return 0
   return 1


def    __lowerCamelCase   (      A__						: Dict					)     ->    Dict:
   lowerCamelCase_						:   List[str]		= set()

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


def    __lowerCamelCase   (      A__						: Optional[int]						,						A__						: Optional[int]					)     ->    int:


   if not chinese_word_set:
      return bert_tokens
   lowerCamelCase_						:   Any		= max([len(a__					) for w in chinese_word_set]					)

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


def    __lowerCamelCase   (      A__						: Dict						,						A__						: Tuple						,						A__						: Union[str, Any]					)     ->    Any:
   lowerCamelCase_						:   int		= []

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

   lowerCamelCase_						:   List[Any]		= []
   for i in range(0						,						len(a__					)						,						100					):
      lowerCamelCase_						:   Dict		= bert_tokenizer(lines[i : i + 100]						,						add_special_tokens=a__						,						truncation=a__						,						max_length=512					)
      bert_res.extend(res["""input_ids"""]					)
   assert len(a__					) == len(a__					)

   lowerCamelCase_						:   str		= []
   for input_ids, chinese_word in zip(a__						,						a__					):
      lowerCamelCase_						:   Any		= []
      for id in input_ids:
         lowerCamelCase_						:   int		= bert_tokenizer._convert_id_to_token(a__					)
         input_tokens.append(a__					)
      lowerCamelCase_						:   Optional[int]		= add_sub_symbol(a__						,						a__					)
      lowerCamelCase_						:   Union[str, Any]		= []
      # 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] == "##":
            lowerCamelCase_						:   Dict		= 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    __lowerCamelCase   (      A__						: Dict					)     ->    Optional[int]:


   with open(args.file_name						,						"""r"""						,						encoding="""utf-8"""					) as f:
      lowerCamelCase_						:   Any		= f.readlines()
   lowerCamelCase_						:   str		= [line.strip() for line in data if len(a__					) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   lowerCamelCase_						:   Union[str, Any]		= LTP(args.ltp					)  # faster in GPU device
   lowerCamelCase_						:   Optional[Any]		= BertTokenizer.from_pretrained(args.bert					)

   lowerCamelCase_						:   str		= prepare_ref(a__						,						a__						,						a__					)

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


if __name__ == "__main__":
      snake_case__				:	List[Any]					       =					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__				:	Union[str, Any]					       =					parser.parse_args()
      main(args)








'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference







class 		lowerCAmelCase__  ( a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = ShapEImgaImgPipeline
 lowerCAmelCase__       = ["image"]
 lowerCAmelCase__       = ["image"]
 lowerCAmelCase__       = [
     "num_images_per_prompt",
     "num_inference_steps",
     "generator",
     "latents",
     "guidance_scale",
     "frame_size",
     "output_type",
     "return_dict",
 ]
 lowerCAmelCase__       = False
 @property
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Tuple:


      """simple docstring"""




      return 32
 @property
 def  UpperCAmelCase__						(						self   :			str     )    ->					int:


      """simple docstring"""




      return 32
 @property
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					Union[str, Any]:


      """simple docstring"""




      return self.time_input_dim * 4
 @property
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      return 8
 @property
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Optional[Any]:


      """simple docstring"""




      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size   ,				image_size=64   ,				projection_dim=self.text_embedder_hidden_size   ,				intermediate_size=37   ,				num_attention_heads=4   ,				num_channels=3   ,				num_hidden_layers=5   ,				patch_size=1   ,				)

      __SCREAMING_SNAKE_CASE      = CLIPVisionModel(__SCREAMING_SNAKE_CASE     )
      return model
 @property
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = CLIPImageProcessor(
          crop_size=224   ,				do_center_crop=__SCREAMING_SNAKE_CASE   ,				do_normalize=__SCREAMING_SNAKE_CASE   ,				do_resize=__SCREAMING_SNAKE_CASE   ,				image_mean=[0.48145466, 0.4578275, 0.40821073]   ,				image_std=[0.26862954, 0.26130258, 0.27577711]   ,				resample=3   ,				size=224   ,				)

      return image_processor
 @property
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Tuple:


      """simple docstring"""




      torch.manual_seed(0     )

      __SCREAMING_SNAKE_CASE      = {
          """num_attention_heads""": 2,
          """attention_head_dim""": 16,
          """embedding_dim""": self.time_input_dim,
          """num_embeddings""": 32,
          """embedding_proj_dim""": self.text_embedder_hidden_size,
          """time_embed_dim""": self.time_embed_dim,
          """num_layers""": 1,
          """clip_embed_dim""": self.time_input_dim * 2,
          """additional_embeddings""": 0,
          """time_embed_act_fn""": """gelu""",
          """norm_in_type""": """layer""",
          """embedding_proj_norm_type""": """layer""",
          """encoder_hid_proj_type""": None,
          """added_emb_type""": None,
      }

      __SCREAMING_SNAKE_CASE      = PriorTransformer(**__SCREAMING_SNAKE_CASE     )
      return model
 @property
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """simple docstring"""




      torch.manual_seed(0     )

      __SCREAMING_SNAKE_CASE      = {
          """param_shapes""": (
              (self.renderer_dim, 93),
              (self.renderer_dim, 8),
              (self.renderer_dim, 8),
              (self.renderer_dim, 8),
          ),
          """d_latent""": self.time_input_dim,
          """d_hidden""": self.renderer_dim,
          """n_output""": 12,
          """background""": (
              0.1,
              0.1,
              0.1,
          ),
      }
      __SCREAMING_SNAKE_CASE      = ShapERenderer(**__SCREAMING_SNAKE_CASE     )
      return model
 def  UpperCAmelCase__						(						self   :			str     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.dummy_prior
      __SCREAMING_SNAKE_CASE      = self.dummy_image_encoder
      __SCREAMING_SNAKE_CASE      = self.dummy_image_processor
      __SCREAMING_SNAKE_CASE      = self.dummy_renderer

      __SCREAMING_SNAKE_CASE      = HeunDiscreteScheduler(
          beta_schedule="""exp"""   ,				num_train_timesteps=1_024   ,				prediction_type="""sample"""   ,				use_karras_sigmas=__SCREAMING_SNAKE_CASE   ,				clip_sample=__SCREAMING_SNAKE_CASE   ,				clip_sample_range=1.0   ,				)
      __SCREAMING_SNAKE_CASE      = {
          """prior""": prior,
          """image_encoder""": image_encoder,
          """image_processor""": image_processor,
          """renderer""": renderer,
          """scheduler""": scheduler,
      }

      return components
 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			str=0     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 64, 64)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )

      if str(__SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
           __SCREAMING_SNAKE_CASE      = torch.manual_seed(__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = {
          """image""": input_image,
          """generator""": generator,
          """num_inference_steps""": 1,
          """frame_size""": 32,
          """output_type""": """np""",
      }
      return inputs
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """cpu"""

      __SCREAMING_SNAKE_CASE      = self.get_dummy_components()

      __SCREAMING_SNAKE_CASE      = self.pipeline_class(**__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = pipe.to(__SCREAMING_SNAKE_CASE     )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE     )     )
      __SCREAMING_SNAKE_CASE      = output.images[0]
      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]

      assert image.shape == (20, 32, 32, 3)

      __SCREAMING_SNAKE_CASE      = np.array(
          [
              0.00039216,
              0.00039216,
              0.00039216,
              0.00039216,
              0.00039216,
              0.00039216,
              0.00039216,
              0.00039216,
              0.00039216,
          ]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
 def  UpperCAmelCase__						(						self   :			str     )    ->					Dict:


      """simple docstring"""




      self._test_inference_batch_consistent(batch_sizes=[1, 2]     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = torch_device == """cpu"""
      __SCREAMING_SNAKE_CASE      = True
      self._test_inference_batch_single_identical(
          batch_size=2   ,				test_max_difference=__SCREAMING_SNAKE_CASE   ,				relax_max_difference=__SCREAMING_SNAKE_CASE   ,				)


 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_dummy_components()
      __SCREAMING_SNAKE_CASE      = self.pipeline_class(**__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = 1
      __SCREAMING_SNAKE_CASE      = 2

      __SCREAMING_SNAKE_CASE      = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE     )

      for key in inputs.keys():
           if key in self.batch_params:
                __SCREAMING_SNAKE_CASE      = batch_size * [inputs[key]]

      __SCREAMING_SNAKE_CASE      = pipe(**__SCREAMING_SNAKE_CASE   ,				num_images_per_prompt=__SCREAMING_SNAKE_CASE     )[0]

      assert images.shape[0] == batch_size * num_images_per_prompt







@slow
@require_torch_gpu
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			int     )    ->					Dict:


      """simple docstring"""




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""     )
      __SCREAMING_SNAKE_CASE      = load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/shap_e/test_shap_e_img2img_out.npy"""     )
      __SCREAMING_SNAKE_CASE      = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""     )
      __SCREAMING_SNAKE_CASE      = pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(0     )

      __SCREAMING_SNAKE_CASE      = pipe(
          __SCREAMING_SNAKE_CASE   ,				generator=__SCREAMING_SNAKE_CASE   ,				guidance_scale=3.0   ,				num_inference_steps=64   ,				frame_size=64   ,				output_type="""np"""   ,				).images[0]

      assert images.shape == (20, 64, 64, 3)

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )






