

'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[int]:
       # 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       UpperCAmelCase__    (      UpperCAmelCase__				)			->			List[Any]:
       # word like '180' or '身高' or '神'
       for char in word:
              A_						=      ord(UpperCAmelCase__				)
              if not _is_chinese_char(UpperCAmelCase__				):
                     return 0
       return 1
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      set()

       for token in tokens:
              A_						=      len(UpperCAmelCase__				) > 1 and is_chinese(UpperCAmelCase__				)
              if chinese_word:
                     word_set.add(UpperCAmelCase__				)
       A_						=      list(UpperCAmelCase__				)
       return word_list
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       if not chinese_word_set:
              return bert_tokens
       A_						=      max([len(UpperCAmelCase__				) for w in chinese_word_set]				)

       A_						=      bert_tokens
       A_					,			A_						=      0, len(UpperCAmelCase__				)
       while start < end:
              A_						=      True
              if is_chinese(bert_word[start]				):
                     A_						=      min(end - start,       UpperCAmelCase__				)
                     for i in range(UpperCAmelCase__,       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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       A_						=      []

       for i in range(0,       len(UpperCAmelCase__				),       1_00				):
              A_						=      ltp_tokenizer.pipeline(lines[i : i + 1_00],       tasks=["""cws"""]				).cws
              A_						=      [get_chinese_word(UpperCAmelCase__				) for r in res]
              ltp_res.extend(UpperCAmelCase__				)
       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       A_						=      []
       for i in range(0,       len(UpperCAmelCase__				),       1_00				):
              A_						=      bert_tokenizer(lines[i : i + 1_00],       add_special_tokens=UpperCAmelCase__,       truncation=UpperCAmelCase__,       max_length=5_12				)
              bert_res.extend(res["""input_ids"""]				)
       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

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

       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       return ref_ids
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[Any]:
       # 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(UpperCAmelCase__				) > 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(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

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


if __name__ == "__main__":
      __lowerCamelCase		       =    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		       =    parser.parse_args()
      main(args)



'''simple docstring'''







import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 					A__      (      _snake_case						,						unittest.TestCase			):
    lowercase						=		KandinskyVaaPriorPipeline
    lowercase						=		["prompt"]
    lowercase						=		["prompt", "negative_prompt"]
    lowercase						=		[
        "num_images_per_prompt",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    lowercase						=		False



    @property
    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           return 32



    @property
    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           return 32



    @property
    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           return self.time_input_dim



    @property
    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           return self.time_input_dim * 4



    @property
    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           return 100



    @property
    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
           return tokenizer



    @property
    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           torch.manual_seed(0       )
           A_						=      CLIPTextConfig(
               bos_token_id=0		,				eos_token_id=2		,				hidden_size=self.text_embedder_hidden_size		,				projection_dim=self.text_embedder_hidden_size		,				intermediate_size=37		,				layer_norm_eps=1e-0_5		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=1000		,				)
           return CLIPTextModelWithProjection(UpperCamelCase__       )



    @property
    def 					snake_case_			(					self       )						->      Tuple:





           '''simple docstring'''
           torch.manual_seed(0       )

           A_						=      {
               """num_attention_heads""": 2,
               """attention_head_dim""": 12,
               """embedding_dim""": self.text_embedder_hidden_size,
               """num_layers""": 1,
           }

           A_						=      PriorTransformer(**UpperCamelCase__       )
           # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
           A_						=      nn.Parameter(torch.ones(model.clip_std.shape       )       )
           return model



    @property
    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           torch.manual_seed(0       )
           A_						=      CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size		,				image_size=224		,				projection_dim=self.text_embedder_hidden_size		,				intermediate_size=37		,				num_attention_heads=4		,				num_channels=3		,				num_hidden_layers=5		,				patch_size=14		,				)

           A_						=      CLIPVisionModelWithProjection(UpperCamelCase__       )
           return model



    @property
    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      CLIPImageProcessor(
               crop_size=224		,				do_center_crop=UpperCamelCase__		,				do_normalize=UpperCamelCase__		,				do_resize=UpperCamelCase__		,				image_mean=[0.48145466, 0.4578275, 0.40821073]		,				image_std=[0.26862954, 0.26130258, 0.27577711]		,				resample=3		,				size=224		,				)

           return image_processor



    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      self.dummy_prior
           A_						=      self.dummy_image_encoder
           A_						=      self.dummy_text_encoder
           A_						=      self.dummy_tokenizer
           A_						=      self.dummy_image_processor

           A_						=      UnCLIPScheduler(
               variance_type="""fixed_small_log"""		,				prediction_type="""sample"""		,				num_train_timesteps=1000		,				clip_sample=UpperCamelCase__		,				clip_sample_range=10.0		,				)

           A_						=      {
               """prior""": prior,
               """image_encoder""": image_encoder,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """scheduler""": scheduler,
               """image_processor""": image_processor,
           }

           return components



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=0       )						->      Optional[int]:





           '''simple docstring'''
           if str(UpperCamelCase__       ).startswith("""mps"""       ):
                  A_						=      torch.manual_seed(UpperCamelCase__       )
           else:
                  A_						=      torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
           A_						=      {
               """prompt""": """horse""",
               """generator""": generator,
               """guidance_scale""": 4.0,
               """num_inference_steps""": 2,
               """output_type""": """np""",
           }
           return inputs



    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      """cpu"""

           A_						=      self.get_dummy_components()

           A_						=      self.pipeline_class(**UpperCamelCase__       )
           A_						=      pipe.to(UpperCamelCase__       )

           pipe.set_progress_bar_config(disable=UpperCamelCase__       )

           A_						=      pipe(**self.get_dummy_inputs(UpperCamelCase__       )       )
           A_						=      output.image_embeds

           A_						=      pipe(
               **self.get_dummy_inputs(UpperCamelCase__       )		,				return_dict=UpperCamelCase__		,				)[0]

           A_						=      image[0, -10:]
           A_						=      image_from_tuple[0, -10:]

           assert image.shape == (1, 32)

           A_						=      np.array(
               [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2



    @skip_mps
    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      torch_device == """cpu"""
           A_						=      True
           A_						=      False

           self._test_inference_batch_single_identical(
               test_max_difference=UpperCamelCase__		,				relax_max_difference=UpperCamelCase__		,				test_mean_pixel_difference=UpperCamelCase__		,				)







    @skip_mps
    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      torch_device == """cpu"""
           A_						=      False

           self._test_attention_slicing_forward_pass(
               test_max_difference=UpperCamelCase__		,				test_mean_pixel_difference=UpperCamelCase__		,				)
