


"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       Optional[Any]:

							'''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							)		->       Optional[int]:

							'''simple docstring'''



							for char in word:
														__UpperCAmelCase   :						Dict      							=					ord(_UpperCamelCase							)
														if not _is_chinese_char(_UpperCamelCase							):
																					return 0
							return 1
def      lowerCamelCase       (     _UpperCamelCase		:      List[str]							)		->       Optional[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Dict      							=					set()

							for token in tokens:
														__UpperCAmelCase   :						List[str]      							=					len(_UpperCamelCase							) > 1 and is_chinese(_UpperCamelCase							)
														if chinese_word:
																					word_set.add(_UpperCamelCase							)
							__UpperCAmelCase   :						Any      							=					list(_UpperCamelCase							)
							return word_list
def      lowerCamelCase       (     _UpperCamelCase		:      List[str]       ,			_UpperCamelCase		:      set()							)		->       Any:

							'''simple docstring'''



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

							__UpperCAmelCase   :						str      							=					bert_tokens
							__UpperCAmelCase							,__UpperCAmelCase   :						Any      							=					0, len(_UpperCamelCase							)
							while start < end:
														__UpperCAmelCase   :						Tuple      							=					True
														if is_chinese(bert_word[start]							):
																					__UpperCAmelCase   :						Union[str, Any]      							=					min(end - start       ,			_UpperCamelCase							)
																					for i in range(_UpperCamelCase       ,			1       ,			-1							):
																												__UpperCAmelCase   :						Dict      							=					"""""".join(bert_word[start : start + i]							)
																												if whole_word in chinese_word_set:
																																			for j in range(start + 1       ,			start + i							):
																																										__UpperCAmelCase   :						Any      							=					"""##""" + bert_word[j]
																																			__UpperCAmelCase   :						int      							=					start + i
																																			__UpperCAmelCase   :						Union[str, Any]      							=					False
																																			break
														if single_word:
																					start += 1
							return bert_word
def      lowerCamelCase       (     _UpperCamelCase		:      List[str]       ,			_UpperCamelCase		:      LTP       ,			_UpperCamelCase		:      BertTokenizer							)		->       List[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Any      							=					[]

							for i in range(0       ,			len(_UpperCamelCase							)       ,			1_0_0							):
														__UpperCAmelCase   :						List[Any]      							=					ltp_tokenizer.pipeline(lines[i : i + 1_0_0]       ,			tasks=["""cws"""]							).cws
														__UpperCAmelCase   :						List[str]      							=					[get_chinese_word(_UpperCamelCase							) for r in res]
														ltp_res.extend(_UpperCamelCase							)
							assert len(_UpperCamelCase							) == len(_UpperCamelCase							)

							__UpperCAmelCase   :						Dict      							=					[]
							for i in range(0       ,			len(_UpperCamelCase							)       ,			1_0_0							):
														__UpperCAmelCase   :						List[Any]      							=					bert_tokenizer(lines[i : i + 1_0_0]       ,			add_special_tokens=_UpperCamelCase       ,			truncation=_UpperCamelCase       ,			max_length=5_1_2							)
														bert_res.extend(res["""input_ids"""]							)
							assert len(_UpperCamelCase							) == len(_UpperCamelCase							)

							__UpperCAmelCase   :						Union[str, Any]      							=					[]
							for input_ids, chinese_word in zip(_UpperCamelCase       ,			_UpperCamelCase							):
														__UpperCAmelCase   :						int      							=					[]
														for id in input_ids:
																					__UpperCAmelCase   :						Dict      							=					bert_tokenizer._convert_id_to_token(_UpperCamelCase							)
																					input_tokens.append(_UpperCamelCase							)
														__UpperCAmelCase   :						str      							=					add_sub_symbol(_UpperCamelCase       ,			_UpperCamelCase							)
														__UpperCAmelCase   :						Optional[Any]      							=					[]
														# 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] == "##":
																												__UpperCAmelCase   :						Tuple      							=					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		:      Dict							)		->       Any:

							'''simple docstring'''



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

							__UpperCAmelCase   :						Optional[Any]      							=					prepare_ref(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase							)

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


if __name__ == "__main__":
							UpperCAmelCase    :     Optional[Any]   						=		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    :     Union[str, Any]   						=		parser.parse_args()
							main(args)




"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase    :     Dict   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Union[str, Any]   						=		torch.device('cpu')
def      lowerCamelCase       (     )		->       str:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[int]      							=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							__UpperCAmelCase   :						Optional[Any]      							=					Image.open(requests.get(_UpperCamelCase       ,			stream=_UpperCamelCase							).raw							)
							return im
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       Union[str, Any]:

							'''simple docstring'''



							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]							)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]							)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]							)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]							)
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]       ,			_UpperCamelCase		:      Optional[int]       ,			_UpperCamelCase		:      List[Any]							)		->       Union[str, Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Any      							=					dct.pop(_UpperCamelCase							)
							__UpperCAmelCase   :						List[str]      							=					val
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]							)		->       List[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Tuple      							=					[]
							for k in state_dict.keys():
														__UpperCAmelCase   :						int      							=					k
														if ".pwconv" in k:
																					__UpperCAmelCase   :						Optional[int]      							=					k_new.replace(""".pwconv"""       ,			""".point_wise_conv"""							)
														if ".dwconv" in k:
																					__UpperCAmelCase   :						List[Any]      							=					k_new.replace(""".dwconv"""       ,			""".depth_wise_conv"""							)
														if ".Proj." in k:
																					__UpperCAmelCase   :						str      							=					k_new.replace(""".Proj."""       ,			""".proj."""							)
														if "patch_embed" in k_new:
																					__UpperCAmelCase   :						int      							=					k_new.replace("""patch_embed"""       ,			"""swiftformer.patch_embed.patch_embedding"""							)
														if "network" in k_new:
																					__UpperCAmelCase   :						str      							=					k_new.split("""."""							)
																					if ls[2].isdigit():
																												__UpperCAmelCase   :						Dict      							=					"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]							)
																					else:
																												__UpperCAmelCase   :						List[str]      							=					k_new.replace("""network"""       ,			"""swiftformer.encoder.network"""							)
														rename_keys.append((k, k_new)							)
							return rename_keys
@torch.no_grad()
def      lowerCamelCase       (     _UpperCamelCase		:      int       ,			_UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      int							)		->       Union[str, Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[int]      							=					SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							__UpperCAmelCase   :						Optional[int]      							=					1_0_0_0
							__UpperCAmelCase   :						Optional[int]      							=					"""huggingface/label-files"""
							__UpperCAmelCase   :						str      							=					"""imagenet-1k-id2label.json"""
							__UpperCAmelCase   :						List[Any]      							=					json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type="""dataset"""							)       ,			"""r"""							)							)
							__UpperCAmelCase   :						Any      							=					{int(_UpperCamelCase							): v for k, v in idalabel.items()}
							__UpperCAmelCase   :						Optional[Any]      							=					idalabel
							__UpperCAmelCase   :						Optional[int]      							=					{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														__UpperCAmelCase   :						Tuple      							=					[3, 3, 6, 4]
														__UpperCAmelCase   :						List[Any]      							=					[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														__UpperCAmelCase   :						Optional[int]      							=					[3, 3, 9, 6]
														__UpperCAmelCase   :						Tuple      							=					[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														__UpperCAmelCase   :						Dict      							=					[4, 3, 1_0, 5]
														__UpperCAmelCase   :						Optional[Any]      							=					[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														__UpperCAmelCase   :						str      							=					[4, 4, 1_2, 6]
														__UpperCAmelCase   :						str      							=					[6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith("""https"""							):
																					__UpperCAmelCase   :						str      							=					torch.hub.load_state_dict_from_url(_UpperCamelCase       ,			map_location="""cpu"""       ,			check_hash=_UpperCamelCase							)
														else:
																					__UpperCAmelCase   :						Dict      							=					torch.load(_UpperCamelCase       ,			map_location="""cpu"""							)
							__UpperCAmelCase   :						str      							=					checkpoint

							__UpperCAmelCase   :						Union[str, Any]      							=					create_rename_keys(_UpperCamelCase							)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase							)

							# load HuggingFace model
							__UpperCAmelCase   :						int      							=					SwiftFormerForImageClassification(_UpperCamelCase							).eval()
							hf_model.load_state_dict(_UpperCamelCase							)

							# prepare test inputs
							__UpperCAmelCase   :						List[Any]      							=					prepare_img()
							__UpperCAmelCase   :						int      							=					ViTImageProcessor.from_pretrained("""preprocessor_config"""							)
							__UpperCAmelCase   :						Optional[Any]      							=					processor(images=_UpperCamelCase       ,			return_tensors="""pt"""							)

							# compare outputs from both models
							__UpperCAmelCase   :						Tuple      							=					get_expected_output(_UpperCamelCase							)
							__UpperCAmelCase   :						Dict      							=					hf_model(inputs["""pixel_values"""]							).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]							)
							assert torch.allclose(hf_logits[0, 0:5]       ,			_UpperCamelCase       ,			atol=1E-3							)

							Path(_UpperCamelCase							).mkdir(exist_ok=_UpperCamelCase							)
							print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''							)
							hf_model.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
							UpperCAmelCase    :     Optional[Any]   						=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--swiftformer_name',
							    default='swiftformer_xs',
							    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
							    type=str,
							    help='Name of the SwiftFormer model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='./converted_outputs/',
							    type=str,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

							UpperCAmelCase    :     Tuple   						=		parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
