



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def      lowerCAmelCase__		(   lowerCamelCase_		:		Any):





				'''simple docstring'''

				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__		(   lowerCamelCase_		:		str):





				'''simple docstring'''

				for char in word:
								lowerCAmelCase__					:    List[str]												=					ord(lowerCamelCase_)
								if not _is_chinese_char(lowerCamelCase_):
												return 0
				return 1






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]):





				'''simple docstring'''

				lowerCAmelCase__					:    List[str]												=					set()

				for token in tokens:
								lowerCAmelCase__					:    Union[str, Any]												=					len(lowerCamelCase_) > 1 and is_chinese(lowerCamelCase_)
								if chinese_word:
												word_set.add(lowerCamelCase_)
				lowerCAmelCase__					:    int												=					list(lowerCamelCase_)
				return word_list






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		set()):





				'''simple docstring'''

				if not chinese_word_set:
								return bert_tokens
				lowerCAmelCase__					:    Optional[int]												=					max([len(lowerCamelCase_) for w in chinese_word_set])

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		LTP	,lowerCamelCase_		:		BertTokenizer):





				'''simple docstring'''

				lowerCAmelCase__					:    Dict												=					[]

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

				lowerCAmelCase__					:    Tuple												=					[]
				for i in range(0	,len(lowerCamelCase_)	,100):
								lowerCAmelCase__					:    int												=					bert_tokenizer(lines[i : i + 100]	,add_special_tokens=lowerCamelCase_	,truncation=lowerCamelCase_	,max_length=512)
								bert_res.extend(res['''input_ids'''])
				assert len(lowerCamelCase_) == len(lowerCamelCase_)

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

				assert len(lowerCamelCase_) == len(lowerCamelCase_)

				return ref_ids






def      lowerCAmelCase__		(   lowerCamelCase_		:		Tuple):





				'''simple docstring'''

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

				lowerCAmelCase__					:    Union[str, Any]												=					prepare_ref(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_)

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


if __name__ == "__main__":
							__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',
							)

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






import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class 	lowerCamelCase__      (							lowerCamelCase__				,    lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =IFInpaintingSuperResolutionPipeline
		snake_case_     =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
		snake_case_     =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""})
		snake_case_     =PipelineTesterMixin.required_optional_params - {"""latents"""}



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

						"""simple docstring"""






						return self._get_superresolution_dummy_components()



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

						"""simple docstring"""






						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    Union[str, Any]												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    int												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					floats_tensor((1, 3, 16, 16)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					{
						    '''prompt''': '''A painting of a squirrel eating a burger''',
						    '''image''': image,
						    '''original_image''': original_image,
						    '''mask_image''': mask_image,
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''numpy''',
						}

						return inputs



		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()		,reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,)
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != '''cuda'''		,reason='''float16 requires CUDA'''			)
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						super().test_save_load_floataa(expected_max_diff=1e-1			)



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						self._test_save_load_local()



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						self._test_inference_batch_single_identical(
						    expected_max_diff=1e-2		,)

