



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def 							A			(  _UpperCAmelCase       :     int					)       ->				str:







			'''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			(  _UpperCAmelCase       :     int					)       ->				Union[str, Any]:







			'''simple docstring'''



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





def 							A			(  _UpperCAmelCase       :     List[Any]					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase	 =     set()

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





def 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Any					)       ->				Union[str, Any]:







			'''simple docstring'''



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

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





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     List[str]					)       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     []

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

			_UpperCAmelCase	 =     []
			for i in range(0							,     len(_lowerCamelCase					)							,     100					):
						_UpperCAmelCase	 =     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					)

			_UpperCAmelCase	 =     []
			for input_ids, chinese_word in zip(_lowerCamelCase							,     _lowerCamelCase					):
						_UpperCAmelCase	 =     []
						for id in input_ids:
									_UpperCAmelCase	 =     bert_tokenizer._convert_id_to_token(_lowerCamelCase					)
									input_tokens.append(_lowerCamelCase					)
						_UpperCAmelCase	 =     add_sub_symbol(_lowerCamelCase							,     _lowerCamelCase					)
						_UpperCAmelCase	 =     []
						# 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] == "##":
												_UpperCAmelCase	 =     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			(  _UpperCAmelCase       :     Union[str, Any]					)       ->				List[str]:







			'''simple docstring'''



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

			_UpperCAmelCase	 =     prepare_ref(_lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase					)

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


if __name__ == "__main__":
							UpperCAmelCase__           = 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")

							UpperCAmelCase__           = parser.parse_args()
							main(args)





import unittest

from transformers import is_vision_available
from transformers.pipelines import 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     __lowerCAmelCase					:



										@staticmethod
										def 					_lowerCamelCase (		*A     :						Union[str, Any]				,	**A     :						List[Any])     ->					Union[str, Any]:



													"""simple docstring"""






													pass







@is_pipeline_test
@require_vision
class     __lowerCAmelCase					(    unittest.TestCase		):



			@require_torch
			def 					_lowerCamelCase (		self     :						List[str])     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     pipeline(
						    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'				,	)
						_UpperCAmelCase	 =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						_UpperCAmelCase	 =     image_classifier(A				,	candidate_labels=['a', 'b', 'c'])

						# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
						# python and torch versions.
						self.assertIn(
						    nested_simplify(A)				,	[
						        [{'score': 0.3_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'b'}, {'score': 0.3_3_3, 'label': 'c'}],
						        [{'score': 0.3_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'c'}, {'score': 0.3_3_3, 'label': 'b'}],
						    ]				,	)

						_UpperCAmelCase	 =     image_classifier([image] * 5				,	candidate_labels=['A', 'B', 'C']				,	batch_size=2)
						self.assertEqual(
						    nested_simplify(A)				,	[
						        [
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						        ],
						        [
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						        ],
						        [
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						        ],
						        [
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						        ],
						        [
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						        ],
						    ]				,	)



			@require_tf
			def 					_lowerCamelCase (		self     :						str)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     pipeline(
						    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'				,	framework='tf')
						_UpperCAmelCase	 =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						_UpperCAmelCase	 =     image_classifier(A				,	candidate_labels=['a', 'b', 'c'])

						self.assertEqual(
						    nested_simplify(A)				,	[{'score': 0.3_3_3, 'label': 'a'}, {'score': 0.3_3_3, 'label': 'b'}, {'score': 0.3_3_3, 'label': 'c'}]				,	)

						_UpperCAmelCase	 =     image_classifier([image] * 5				,	candidate_labels=['A', 'B', 'C']				,	batch_size=2)
						self.assertEqual(
						    nested_simplify(A)				,	[
						        [
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						        ],
						        [
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						        ],
						        [
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						        ],
						        [
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						        ],
						        [
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						            {'score': 0.3_3_3, 'label': ANY(A)},
						        ],
						    ]				,	)



			@slow
			@require_torch
			def 					_lowerCamelCase (		self     :						Tuple)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     pipeline(
						    task='zero-shot-image-classification'				,	model='openai/clip-vit-base-patch32'				,	)
						# This is an image of 2 cats with remotes and no planes
						_UpperCAmelCase	 =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						_UpperCAmelCase	 =     image_classifier(A				,	candidate_labels=['cat', 'plane', 'remote'])

						self.assertEqual(
						    nested_simplify(A)				,	[
						        {'score': 0.5_1_1, 'label': 'remote'},
						        {'score': 0.4_8_5, 'label': 'cat'},
						        {'score': 0.0_0_4, 'label': 'plane'},
						    ]				,	)

						_UpperCAmelCase	 =     image_classifier([image] * 5				,	candidate_labels=['cat', 'plane', 'remote']				,	batch_size=2)
						self.assertEqual(
						    nested_simplify(A)				,	[
						        [
						            {'score': 0.5_1_1, 'label': 'remote'},
						            {'score': 0.4_8_5, 'label': 'cat'},
						            {'score': 0.0_0_4, 'label': 'plane'},
						        ],
						    ]
						    * 5				,	)



			@slow
			@require_tf
			def 					_lowerCamelCase (		self     :						List[str])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     pipeline(
						    task='zero-shot-image-classification'				,	model='openai/clip-vit-base-patch32'				,	framework='tf')
						# This is an image of 2 cats with remotes and no planes
						_UpperCAmelCase	 =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						_UpperCAmelCase	 =     image_classifier(A				,	candidate_labels=['cat', 'plane', 'remote'])
						self.assertEqual(
						    nested_simplify(A)				,	[
						        {'score': 0.5_1_1, 'label': 'remote'},
						        {'score': 0.4_8_5, 'label': 'cat'},
						        {'score': 0.0_0_4, 'label': 'plane'},
						    ]				,	)

						_UpperCAmelCase	 =     image_classifier([image] * 5				,	candidate_labels=['cat', 'plane', 'remote']				,	batch_size=2)
						self.assertEqual(
						    nested_simplify(A)				,	[
						        [
						            {'score': 0.5_1_1, 'label': 'remote'},
						            {'score': 0.4_8_5, 'label': 'cat'},
						            {'score': 0.0_0_4, 'label': 'plane'},
						        ],
						    ]
						    * 5				,	)
