





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 						lowerCamelCase__     (     UpperCamelCase__			:       str						)			-> List[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 						lowerCamelCase__     (     UpperCamelCase__			:       str						)			-> Dict:



					'''simple docstring'''




					for char in word:
										_snake_case									=				ord(UpperCamelCase__						)
										if not _is_chinese_char(UpperCamelCase__						):
															return 0
					return 1





def 						lowerCamelCase__     (     UpperCamelCase__			:       List[str]						)			-> Optional[Any]:



					'''simple docstring'''




					_snake_case									=				set()

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       set()						)			-> List[str]:



					'''simple docstring'''




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

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       List[str]       ,    UpperCamelCase__			:       LTP       ,    UpperCamelCase__			:       BertTokenizer						)			-> Optional[int]:



					'''simple docstring'''




					_snake_case									=				[]

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

					_snake_case									=				[]
					for i in range(0       ,    len(UpperCamelCase__						)       ,    100						):
										_snake_case									=				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__						)

					_snake_case									=				[]
					for input_ids, chinese_word in zip(UpperCamelCase__       ,    UpperCamelCase__						):
										_snake_case									=				[]
										for id in input_ids:
															_snake_case									=				bert_tokenizer._convert_id_to_token(UpperCamelCase__						)
															input_tokens.append(UpperCamelCase__						)
										_snake_case									=				add_sub_symbol(UpperCamelCase__       ,    UpperCamelCase__						)
										_snake_case									=				[]
										# 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] == "##":
																				_snake_case									=				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 						lowerCamelCase__     (     UpperCamelCase__			:       Union[str, Any]						)			-> Tuple:



					'''simple docstring'''




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

					_snake_case									=				prepare_ref(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__						)

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


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

					UpperCAmelCase_      							=     parser.parse_args()
					main(args)










import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


UpperCAmelCase_      							=     {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class 			UpperCamelCase_		(  nn.Module					):




					def __init__(		self				,						lowerCAmelCase_  )		->							int:
										super().__init__()
										_snake_case									=				torchvision.models.resnetaaa(pretrained=lowerCAmelCase_  )
										_snake_case									=				list(model.children()  )[:-2]
										_snake_case									=				nn.Sequential(*lowerCAmelCase_  )
										_snake_case									=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							str:
										# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
										_snake_case									=				self.pool(self.model(lowerCAmelCase_  )  )
										_snake_case									=				torch.flatten(lowerCAmelCase_				,						start_dim=2  )
										_snake_case									=				out.transpose(1				,						2  ).contiguous()
										return out  # BxNx2048





class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[Any]:
										_snake_case									=				[json.loads(lowerCAmelCase_  ) for l in open(lowerCAmelCase_  )]
										_snake_case									=				os.path.dirname(lowerCAmelCase_  )
										_snake_case									=				tokenizer
										_snake_case									=				labels
										_snake_case									=				len(lowerCAmelCase_  )
										_snake_case									=				max_seq_length

										_snake_case									=				transforms




					def __len__(		self  )		->							Any:
										return len(self.data  )




					def __getitem__(		self				,						lowerCAmelCase_  )		->							Optional[int]:
										_snake_case									=				torch.LongTensor(self.tokenizer.encode(self.data[index]['text']				,						add_special_tokens=lowerCAmelCase_  )  )
										_snake_case							,     _snake_case							,     _snake_case									=				sentence[0], sentence[1:-1], sentence[-1]
										_snake_case									=				sentence[: self.max_seq_length]

										_snake_case									=				torch.zeros(self.n_classes  )
										_snake_case									=				1

										_snake_case									=				Image.open(os.path.join(self.data_dir				,						self.data[index]['img']  )  ).convert('RGB'  )
										_snake_case									=				self.transforms(lowerCAmelCase_  )

										return {
										    "image_start_token": start_token,
										    "image_end_token": end_token,
										    "sentence": sentence,
										    "image": image,
										    "label": label,
										}






					def 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				Counter()
										for row in self.data:
															label_freqs.update(row['label']  )
										return label_freqs





def 						lowerCamelCase__     (     UpperCamelCase__			:       str						)			-> Dict:



					'''simple docstring'''




					_snake_case									=				[len(row['sentence']						) for row in batch]
					_snake_case							,     _snake_case									=				len(UpperCamelCase__						), max(UpperCamelCase__						)

					_snake_case									=				torch.zeros(UpperCamelCase__       ,    UpperCamelCase__       ,    dtype=torch.long						)
					_snake_case									=				torch.zeros(UpperCamelCase__       ,    UpperCamelCase__       ,    dtype=torch.long						)

					for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__       ,    UpperCamelCase__						)						):
										_snake_case									=				input_row['sentence']
										_snake_case									=				1

					_snake_case									=				torch.stack([row['image'] for row in batch]						)
					_snake_case									=				torch.stack([row['label'] for row in batch]						)
					_snake_case									=				torch.stack([row['image_start_token'] for row in batch]						)
					_snake_case									=				torch.stack([row['image_end_token'] for row in batch]						)

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def 						lowerCamelCase__     (     )			-> str:



					'''simple docstring'''




					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]





def 						lowerCamelCase__     (     )			-> Tuple:



					'''simple docstring'''




					return transforms.Compose(
					    [
					        transforms.Resize(256						),
					        transforms.CenterCrop(224						),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]       ,    std=[0.1222_1994, 0.1214_5835, 0.1438_0469]       ,    ),
					    ]						)



