



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							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 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							for char in word:
														_lowerCAmelCase     :							int    =    ord(_lowerCamelCase  )
														if not _is_chinese_char(_lowerCamelCase  ):
																					return 0
							return 1






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    set()

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






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




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

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






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    []

							for i in range(0				, len(_lowerCamelCase  )				, 100  ):
														_lowerCAmelCase     :							List[Any]    =    ltp_tokenizer.seg(lines[i : i + 100]  )[0]
														_lowerCAmelCase     :							Tuple    =    [get_chinese_word(_lowerCamelCase  ) for r in res]
														ltp_res.extend(_lowerCamelCase  )
							assert len(_lowerCamelCase  ) == len(_lowerCamelCase  )

							_lowerCAmelCase     :							str    =    []
							for i in range(0				, len(_lowerCamelCase  )				, 100  ):
														_lowerCAmelCase     :							List[Any]    =    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     :							Any    =    []
							for input_ids, chinese_word in zip(_lowerCamelCase				, _lowerCamelCase  ):
														_lowerCAmelCase     :							Union[str, Any]    =    []
														for id in input_ids:
																					_lowerCAmelCase     :							List[Any]    =    bert_tokenizer._convert_id_to_token(_lowerCamelCase  )
																					input_tokens.append(_lowerCamelCase  )
														_lowerCAmelCase     :							Optional[int]    =    add_sub_symbol(_lowerCamelCase				, _lowerCamelCase  )
														_lowerCAmelCase     :							List[str]    =    []
														# 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     :							Optional[Any]    =    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 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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

							_lowerCAmelCase     :							Optional[Any]    =    prepare_ref(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )

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


if __name__ == "__main__":
						_snake_case		=  argparse.ArgumentParser(description="prepare_chinese_ref")
						parser.add_argument(
						    "--file_name",
						    type=str,
						    default="./resources/chinese-demo.txt",
						    help="file need process, same as training data in lm",
						)
						parser.add_argument(
						    "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
						)
						parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
						parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")

						_snake_case		=  parser.parse_args()
						main(args)




import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
						from PIL import Image
else:







						class  snake_case__:







												'''simple docstring'''




												@staticmethod
												def   lowercase_       ( *__lowercase      ,  **__lowercase							)    ->			Union[str, Any]:
																pass







@is_pipeline_test
@require_vision
@require_torch
class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Union[str, Any]    =							MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			List[str]:
										lowerCAmelCase_       :					Union[str, Any]			 = pipeline(
										    '''zero-shot-object-detection'''      ,  model='''hf-internal-testing/tiny-random-owlvit-object-detection'''							)

										lowerCAmelCase_       :					str			 = [
										    {
										        '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
										        '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
										    }
										]
										return object_detector, examples




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase							)    ->			str:
										lowerCAmelCase_       :					Tuple			 = object_detector(examples[0]      ,  threshold=0.0							)

										lowerCAmelCase_       :					Dict			 = len(__lowercase							)
										self.assertGreater(__lowercase      ,  0							)
										self.assertEqual(
										    __lowercase      ,  [
										        {
										            '''score''': ANY(__lowercase							),
										            '''label''': ANY(__lowercase							),
										            '''box''': {'''xmin''': ANY(__lowercase							), '''ymin''': ANY(__lowercase							), '''xmax''': ANY(__lowercase							), '''ymax''': ANY(__lowercase							)},
										        }
										        for i in range(__lowercase							)
										    ]      ,  )




						@require_tf
						@unittest.skip('''Zero Shot Object Detection not implemented in TF'''							)
						def   lowercase_       ( self							)    ->			List[str]:
										pass




						@require_torch
						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					Union[str, Any]			 = pipeline(
										    '''zero-shot-object-detection'''      ,  model='''hf-internal-testing/tiny-random-owlvit-object-detection'''							)

										lowerCAmelCase_       :					Union[str, Any]			 = object_detector(
										    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''      ,  candidate_labels=['''cat''', '''remote''', '''couch''']      ,  threshold=0.64      ,  )

										self.assertEqual(
										    nested_simplify(__lowercase      ,  decimals=4							)      ,  [
										        {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										        {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										        {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										        {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										        {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										        {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										        {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
										        {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
										        {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
										    ]      ,  )

										lowerCAmelCase_       :					Union[str, Any]			 = object_detector(
										    [
										        {
										            '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
										            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
										        }
										    ]      ,  threshold=0.64      ,  )

										self.assertEqual(
										    nested_simplify(__lowercase      ,  decimals=4							)      ,  [
										        [
										            {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										            {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										            {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
										            {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										            {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										            {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
										            {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
										            {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
										            {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
										        ]
										    ]      ,  )




						@require_torch
						@slow
						def   lowercase_       ( self							)    ->			Union[str, Any]:
										lowerCAmelCase_       :					Any			 = pipeline('''zero-shot-object-detection'''							)

										lowerCAmelCase_       :					Dict			 = object_detector(
										    '''http://images.cocodataset.org/val2017/000000039769.jpg'''      ,  candidate_labels=['''cat''', '''remote''', '''couch''']      ,  )
										self.assertEqual(
										    nested_simplify(__lowercase      ,  decimals=4							)      ,  [
										        {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
										        {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
										        {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
										        {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
										        {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
										    ]      ,  )

										lowerCAmelCase_       :					Tuple			 = object_detector(
										    [
										        {
										            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
										            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
										        },
										        {
										            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
										            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
										        },
										    ]      ,  )
										self.assertEqual(
										    nested_simplify(__lowercase      ,  decimals=4							)      ,  [
										        [
										            {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
										            {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
										            {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
										            {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
										            {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
										        ],
										        [
										            {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
										            {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
										            {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
										            {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
										            {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
										        ],
										    ]      ,  )




						@require_tf
						@unittest.skip('''Zero Shot Object Detection not implemented in TF'''							)
						def   lowercase_       ( self							)    ->			List[str]:
										pass




						@require_torch
						@slow
						def   lowercase_       ( self							)    ->			Optional[int]:
										lowerCAmelCase_       :					Any			 = 0.2
										lowerCAmelCase_       :					List[Any]			 = pipeline('''zero-shot-object-detection'''							)

										lowerCAmelCase_       :					Optional[Any]			 = object_detector(
										    '''http://images.cocodataset.org/val2017/000000039769.jpg'''      ,  candidate_labels=['''cat''', '''remote''', '''couch''']      ,  threshold=__lowercase      ,  )
										self.assertEqual(
										    nested_simplify(__lowercase      ,  decimals=4							)      ,  [
										        {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
										        {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
										        {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
										    ]      ,  )




						@require_torch
						@slow
						def   lowercase_       ( self							)    ->			Optional[int]:
										lowerCAmelCase_       :					Dict			 = 2
										lowerCAmelCase_       :					Union[str, Any]			 = pipeline('''zero-shot-object-detection'''							)

										lowerCAmelCase_       :					Optional[Any]			 = object_detector(
										    '''http://images.cocodataset.org/val2017/000000039769.jpg'''      ,  candidate_labels=['''cat''', '''remote''', '''couch''']      ,  top_k=__lowercase      ,  )
										self.assertEqual(
										    nested_simplify(__lowercase      ,  decimals=4							)      ,  [
										        {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
										        {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
										    ]      ,  )