



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def A    (							__UpperCamelCase       )    ->   List[str]:
			# 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 >= 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 A    (							__UpperCamelCase       )    ->   str:
			# word like '180' or '身高' or '神'
			for char in word:
						A__	  =     ord(__UpperCamelCase       )
						if not _is_chinese_char(__UpperCamelCase       ):
									return 0
			return 1


def A    (							__UpperCamelCase       )    ->   str:
			A__	  =     set()

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


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   str:
			if not chinese_word_set:
						return bert_tokens
			A__	  =     max([len(__UpperCamelCase       ) for w in chinese_word_set]       )

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


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     []

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

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

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

			assert len(__UpperCamelCase       ) == len(__UpperCamelCase       )

			return ref_ids


def A    (							__UpperCamelCase       )    ->   List[str]:
			# 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:
						A__	  =     f.readlines()
			A__	  =     [line.strip() for line in data if len(__UpperCamelCase       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			A__	  =     LTP(args.ltp       )  # faster in GPU device
			A__	  =     BertTokenizer.from_pretrained(args.bert       )

			A__	  =     prepare_ref(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   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__   			=   parser.parse_args()
							main(args)










'''simple docstring'''


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor

class      A       (						unittest.TestCase       ):







							def __init__(    self			:    Tuple				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    List[str]=7				,     lowerCAmelCase_			:    Dict=3				,     lowerCAmelCase_			:    List[Any]=18				,     lowerCAmelCase_			:    Any=30				,     lowerCAmelCase_			:    Optional[int]=4_00				,     lowerCAmelCase_			:    Union[str, Any]=True				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    List[str]=True				,     )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							size if size is not None else {'''height''': 18, '''width''': 18}
														_a      	=							parent
														_a      	=							batch_size
														_a      	=							num_channels
														_a      	=							image_size
														_a      	=							min_resolution
														_a      	=							max_resolution
														_a      	=							do_resize
														_a      	=							size
														_a      	=							do_normalize







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
														            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
														        ]   ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}



@require_torch
@require_vision
class      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							ImageGPTImageProcessor if is_vision_available() else None







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				str:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessingTester(self   )







							@property
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				int:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''clusters'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_resize'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''size'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_normalize'''   )   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict   )
														self.assertEqual(image_processor.size				,     {'''height''': 18, '''width''': 18}   )

														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict				,     size=42   )
														self.assertEqual(image_processor.size				,     {'''height''': 42, '''width''': 42}   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				str:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														_a      	=							json.loads(image_processor.to_json_string()   )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     obj[key]   )   )
																					else:
																												self.assertEqual(obj[key]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a      	=							os.path.join(lowerCAmelCase_				,     '''image_processor.json'''   )
																					image_processor_first.to_json_file(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_json_file(lowerCAmelCase_   ).to_dict()

														_a      	=							image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     image_processor_second[key]   )   )
																					else:
																												self.assertEqual(image_processor_first[key]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_pretrained(lowerCAmelCase_   ).to_dict()

														_a      	=							image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     image_processor_second[key]   )   )
																					else:
																												self.assertEqual(image_processor_first[key]				,     lowerCAmelCase_   )







							@unittest.skip('''ImageGPT requires clusters at initialization'''   )
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														pass



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							load_dataset('''hf-internal-testing/fixtures_image_utils'''					,		split='''test''' )

							_a      	=							Image.open(dataset[4]['''file'''] )
							_a      	=							Image.open(dataset[5]['''file'''] )

							_a      	=							[imagea, imagea]

							return images

@require_vision
@require_torch
class      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''   )

														_a      	=							prepare_images()

														# test non-batched
														_a      	=							image_processing(images[0]				,     return_tensors='''pt'''   )

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (1, 10_24)   )

														_a      	=							[3_06, 1_91, 1_91]
														self.assertEqual(encoding.input_ids[0, :3].tolist()				,     lowerCAmelCase_   )

														# test batched
														_a      	=							image_processing(lowerCAmelCase_				,     return_tensors='''pt'''   )

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (2, 10_24)   )

														_a      	=							[3_03, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()				,     lowerCAmelCase_   )

