
"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor






@require_vision
class        _UpperCAmelCase(       unittest.TestCase      ):



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






							'''simple docstring'''
							_UpperCamelCase									=    tempfile.mkdtemp()

							_UpperCamelCase									=    [
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''[PAD]''',
							    '''[MASK]''',
							    '''的''',
							    '''价''',
							    '''格''',
							    '''是''',
							    '''15''',
							    '''便''',
							    '''alex''',
							    '''##andra''',
							    '''，''',
							    '''。''',
							    '''-''',
							    '''t''',
							    '''shirt''',
							]
							_UpperCamelCase									=    os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''vocab_file'''])
							with open(self.vocab_file							,						'''w'''							,						encoding='''utf-8''') as vocab_writer:
												vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))

							_UpperCamelCase									=    {
							    '''do_resize''': True,
							    '''size''': {'''height''': 2_24, '''width''': 2_24},
							    '''do_center_crop''': True,
							    '''crop_size''': {'''height''': 18, '''width''': 18},
							    '''do_normalize''': True,
							    '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
							    '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
							    '''do_convert_rgb''': True,
							}
							_UpperCamelCase									=    os.path.join(self.tmpdirname							,						__a)
							with open(self.image_processor_file							,						'''w'''							,						encoding='''utf-8''') as fp:
												json.dump(__a							,						__a)



		def 		UpperCAmelCase				(   self							,						**__a)			->			List[Any]:






							'''simple docstring'''
							return BertTokenizer.from_pretrained(self.tmpdirname							,						**__a)



		def 		UpperCAmelCase				(   self							,						**__a)			->			Any:






							'''simple docstring'''
							return BertTokenizerFast.from_pretrained(self.tmpdirname							,						**__a)



		def 		UpperCAmelCase				(   self							,						**__a)			->			List[str]:






							'''simple docstring'''
							return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname							,						**__a)



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






							'''simple docstring'''
							shutil.rmtree(self.tmpdirname)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    [np.random.randint(2_55							,						size=(3, 30, 4_00)							,						dtype=np.uinta)]

							_UpperCamelCase									=    [Image.fromarray(np.moveaxis(__a							,						0							,						-1)) for x in image_inputs]

							return image_inputs



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






							'''simple docstring'''
							_UpperCamelCase									=    self.get_tokenizer()
							_UpperCamelCase									=    self.get_rust_tokenizer()
							_UpperCamelCase									=    self.get_image_processor()

							_UpperCamelCase									=    ChineseCLIPProcessor(tokenizer=__a							,						image_processor=__a)
							processor_slow.save_pretrained(self.tmpdirname)
							_UpperCamelCase									=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname							,						use_fast=__a)

							_UpperCamelCase									=    ChineseCLIPProcessor(tokenizer=__a							,						image_processor=__a)
							processor_fast.save_pretrained(self.tmpdirname)
							_UpperCamelCase									=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname)

							self.assertEqual(processor_slow.tokenizer.get_vocab()							,						tokenizer_slow.get_vocab())
							self.assertEqual(processor_fast.tokenizer.get_vocab()							,						tokenizer_fast.get_vocab())
							self.assertEqual(tokenizer_slow.get_vocab()							,						tokenizer_fast.get_vocab())
							self.assertIsInstance(processor_slow.tokenizer							,						__a)
							self.assertIsInstance(processor_fast.tokenizer							,						__a)

							self.assertEqual(processor_slow.image_processor.to_json_string()							,						image_processor.to_json_string())
							self.assertEqual(processor_fast.image_processor.to_json_string()							,						image_processor.to_json_string())
							self.assertIsInstance(processor_slow.image_processor							,						__a)
							self.assertIsInstance(processor_fast.image_processor							,						__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()							,						image_processor=self.get_image_processor())
							processor.save_pretrained(self.tmpdirname)

							_UpperCamelCase									=    self.get_tokenizer(cls_token='''(CLS)'''							,						sep_token='''(SEP)''')
							_UpperCamelCase									=    self.get_image_processor(do_normalize=__a)

							_UpperCamelCase									=    ChineseCLIPProcessor.from_pretrained(
							    self.tmpdirname							,						cls_token='''(CLS)'''							,						sep_token='''(SEP)'''							,						do_normalize=__a)

							self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer_add_kwargs.get_vocab())
							self.assertIsInstance(processor.tokenizer							,						__a)

							self.assertEqual(processor.image_processor.to_json_string()							,						image_processor_add_kwargs.to_json_string())
							self.assertIsInstance(processor.image_processor							,						__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.get_image_processor()
							_UpperCamelCase									=    self.get_tokenizer()

							_UpperCamelCase									=    ChineseCLIPProcessor(tokenizer=__a							,						image_processor=__a)

							_UpperCamelCase									=    self.prepare_image_inputs()

							_UpperCamelCase									=    image_processor(__a							,						return_tensors='''np''')
							_UpperCamelCase									=    processor(images=__a							,						return_tensors='''np''')

							for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()							,						input_processor[key].sum()							,						delta=1e-2)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_image_processor()
							_UpperCamelCase									=    self.get_tokenizer()

							_UpperCamelCase									=    ChineseCLIPProcessor(tokenizer=__a							,						image_processor=__a)

							_UpperCamelCase									=    '''Alexandra，T-shirt的价格是15便士。'''

							_UpperCamelCase									=    processor(text=__a)

							_UpperCamelCase									=    tokenizer(__a)

							for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]							,						encoded_processor[key])



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_image_processor()
							_UpperCamelCase									=    self.get_tokenizer()

							_UpperCamelCase									=    ChineseCLIPProcessor(tokenizer=__a							,						image_processor=__a)

							_UpperCamelCase									=    '''Alexandra，T-shirt的价格是15便士。'''
							_UpperCamelCase									=    self.prepare_image_inputs()

							_UpperCamelCase									=    processor(text=__a							,						images=__a)

							self.assertListEqual(list(inputs.keys())							,						['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''])

							# test if it raises when no input is passed
							with pytest.raises(__a):
												processor()



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_image_processor()
							_UpperCamelCase									=    self.get_tokenizer()

							_UpperCamelCase									=    ChineseCLIPProcessor(tokenizer=__a							,						image_processor=__a)

							_UpperCamelCase									=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							_UpperCamelCase									=    processor.batch_decode(__a)
							_UpperCamelCase									=    tokenizer.batch_decode(__a)

							self.assertListEqual(__a							,						__a)




		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_image_processor()
							_UpperCamelCase									=    self.get_tokenizer()

							_UpperCamelCase									=    ChineseCLIPProcessor(tokenizer=__a							,						image_processor=__a)

							_UpperCamelCase									=    '''Alexandra，T-shirt的价格是15便士。'''
							_UpperCamelCase									=    self.prepare_image_inputs()

							_UpperCamelCase									=    processor(text=__a							,						images=__a)

							self.assertListEqual(list(inputs.keys())							,						processor.model_input_names)








"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					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  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					for char in word:
										_UpperCamelCase									=    ord(__snake_case      )
										if not _is_chinese_char(__snake_case      ):
															return 0
					return 1



def  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    set()

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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





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

					_UpperCamelCase									=    bert_tokens
					_UpperCamelCase					,    _UpperCamelCase									=    0, len(__snake_case      )
					while start < end:
										_UpperCamelCase									=    True
										if is_chinese(bert_word[start]      ):
															_UpperCamelCase									=    min(end - start,	__snake_case      )
															for i in range(__snake_case,	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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []

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

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

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

					assert len(__snake_case      ) == len(__snake_case      )

					return ref_ids



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""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(__snake_case      ) > 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(__snake_case,	__snake_case,	__snake_case      )

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


if __name__ == "__main__":
				_a								=						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""",
				)

				_a								=						parser.parse_args()
				main(args)






