

"""simple docstring"""




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def 	UpperCAmelCase__   (				A__   )      ->      List[Any]:




   """simple docstring"""
   # 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 >= 0X2_0000 and cp <= 0X2_A6DF)  #
       or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
       or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
       or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
       or (cp >= 0XF900 and cp <= 0XFAFF)
       or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
   ):  #
      return True

   return False



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




   """simple docstring"""
   # word like '180' or '身高' or '神'
   for char in word:
      lowerCamelCase__						=			ord(A__   )
      if not _is_chinese_char(A__   ):
         return 0
   return 1



def 	UpperCAmelCase__   (				A__   )      ->      Optional[int]:




   """simple docstring"""
   lowerCamelCase__						=			set()

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



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




   """simple docstring"""
   if not chinese_word_set:
      return bert_tokens
   lowerCamelCase__						=			max([len(A__   ) for w in chinese_word_set]   )

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



def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Tuple:




   """simple docstring"""
   lowerCamelCase__						=			[]

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

   lowerCamelCase__						=			[]
   for i in range(0	,    len(A__   )	,    100   ):
      lowerCamelCase__						=			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__						=			[]
   for input_ids, chinese_word in zip(A__	,    A__   ):
      lowerCamelCase__						=			[]
      for id in input_ids:
         lowerCamelCase__						=			bert_tokenizer._convert_id_to_token(A__   )
         input_tokens.append(A__   )
      lowerCamelCase__						=			add_sub_symbol(A__	,    A__   )
      lowerCamelCase__						=			[]
      # 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__						=			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__   )      ->      List[str]:




   """simple docstring"""
   # 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:
      lowerCamelCase__						=			f.readlines()
   lowerCamelCase__						=			[line.strip() for line in data if len(A__   ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   lowerCamelCase__						=			LTP(args.ltp   )  # faster in GPU device
   lowerCamelCase__						=			BertTokenizer.from_pretrained(args.bert   )

   lowerCamelCase__						=			prepare_ref(A__	,    A__	,    A__   )

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


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			Dict	  =			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''',
     )

     SCREAMING_SNAKE_CASE_     :			Dict	  =			parser.parse_args()
     main(args)






"""simple docstring"""




import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard


@slow
@require_flax
class 		_A       (			unittest.TestCase					):





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"bird"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

         lowerCamelCase__						=			load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"		)
         lowerCamelCase__						=			pipe.prepare_image_inputs([canny_image] * num_samples		)

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"Chef in the kitchen"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

         lowerCamelCase__						=			load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"		)
         lowerCamelCase__						=			pipe.prepare_image_inputs([pose_image] * num_samples		)

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2




