



'''simple docstring'''





import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[str]							      =					SwinvaConfig()
    lowerCAmelCase__ :  int							      =					swinva_name.split("""_"""			)

    lowerCAmelCase__ :  Dict							      =					name_split[1]
    if "to" in name_split[3]:
        lowerCAmelCase__ :  List[Any]							      =					int(name_split[3][-3:]			)
    else:
        lowerCAmelCase__ :  List[str]							      =					int(name_split[3]			)
    if "to" in name_split[2]:
        lowerCAmelCase__ :  Dict							      =					int(name_split[2][-2:]			)
    else:
        lowerCAmelCase__ :  Union[str, Any]							      =					int(name_split[2][6:]			)

    if model_size == "tiny":
        lowerCAmelCase__ :  str							      =					96
        lowerCAmelCase__ :  Optional[Any]							      =					(2, 2, 6, 2)
        lowerCAmelCase__ :  Any							      =					(3, 6, 12, 24)
    elif model_size == "small":
        lowerCAmelCase__ :  List[Any]							      =					96
        lowerCAmelCase__ :  Tuple							      =					(2, 2, 18, 2)
        lowerCAmelCase__ :  Any							      =					(3, 6, 12, 24)
    elif model_size == "base":
        lowerCAmelCase__ :  Tuple							      =					128
        lowerCAmelCase__ :  Dict							      =					(2, 2, 18, 2)
        lowerCAmelCase__ :  List[Any]							      =					(4, 8, 16, 32)
    else:
        lowerCAmelCase__ :  Optional[int]							      =					192
        lowerCAmelCase__ :  Optional[Any]							      =					(2, 2, 18, 2)
        lowerCAmelCase__ :  Union[str, Any]							      =					(6, 12, 24, 48)

    if "to" in swinva_name:
        lowerCAmelCase__ :  int							      =					(12, 12, 12, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        lowerCAmelCase__ :  Union[str, Any]							      =					21841
        lowerCAmelCase__ :  Union[str, Any]							      =					'''huggingface/label-files'''
        lowerCAmelCase__ :  Optional[int]							      =					'''imagenet-22k-id2label.json'''
        lowerCAmelCase__ :  str							      =					json.load(open(hf_hub_download(snake_case_							,    snake_case_							,    repo_type="""dataset"""			)							,    """r"""			)			)
        lowerCAmelCase__ :  Union[str, Any]							      =					{int(snake_case_			): v for k, v in idalabel.items()}
        lowerCAmelCase__ :  List[Any]							      =					idalabel
        lowerCAmelCase__ :  Union[str, Any]							      =					{v: k for k, v in idalabel.items()}

    else:
        lowerCAmelCase__ :  int							      =					1000
        lowerCAmelCase__ :  Union[str, Any]							      =					'''huggingface/label-files'''
        lowerCAmelCase__ :  Union[str, Any]							      =					'''imagenet-1k-id2label.json'''
        lowerCAmelCase__ :  int							      =					json.load(open(hf_hub_download(snake_case_							,    snake_case_							,    repo_type="""dataset"""			)							,    """r"""			)			)
        lowerCAmelCase__ :  Tuple							      =					{int(snake_case_			): v for k, v in idalabel.items()}
        lowerCAmelCase__ :  Union[str, Any]							      =					idalabel
        lowerCAmelCase__ :  str							      =					{v: k for k, v in idalabel.items()}

    lowerCAmelCase__ :  List[Any]							      =					img_size
    lowerCAmelCase__ :  Union[str, Any]							      =					num_classes
    lowerCAmelCase__ :  List[Any]							      =					embed_dim
    lowerCAmelCase__ :  Dict							      =					depths
    lowerCAmelCase__ :  int							      =					num_heads
    lowerCAmelCase__ :  Any							      =					window_size

    return config
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    if "patch_embed.proj" in name:
        lowerCAmelCase__ :  Optional[int]							      =					name.replace("""patch_embed.proj"""							,    """embeddings.patch_embeddings.projection"""			)
    if "patch_embed.norm" in name:
        lowerCAmelCase__ :  Optional[Any]							      =					name.replace("""patch_embed.norm"""							,    """embeddings.norm"""			)
    if "layers" in name:
        lowerCAmelCase__ :  Tuple							      =					'''encoder.''' + name
    if "attn.proj" in name:
        lowerCAmelCase__ :  Union[str, Any]							      =					name.replace("""attn.proj"""							,    """attention.output.dense"""			)
    if "attn" in name:
        lowerCAmelCase__ :  str							      =					name.replace("""attn"""							,    """attention.self"""			)
    if "norm1" in name:
        lowerCAmelCase__ :  Tuple							      =					name.replace("""norm1"""							,    """layernorm_before"""			)
    if "norm2" in name:
        lowerCAmelCase__ :  Tuple							      =					name.replace("""norm2"""							,    """layernorm_after"""			)
    if "mlp.fc1" in name:
        lowerCAmelCase__ :  Dict							      =					name.replace("""mlp.fc1"""							,    """intermediate.dense"""			)
    if "mlp.fc2" in name:
        lowerCAmelCase__ :  Any							      =					name.replace("""mlp.fc2"""							,    """output.dense"""			)
    if "q_bias" in name:
        lowerCAmelCase__ :  Optional[Any]							      =					name.replace("""q_bias"""							,    """query.bias"""			)
    if "k_bias" in name:
        lowerCAmelCase__ :  Tuple							      =					name.replace("""k_bias"""							,    """key.bias"""			)
    if "v_bias" in name:
        lowerCAmelCase__ :  Dict							      =					name.replace("""v_bias"""							,    """value.bias"""			)
    if "cpb_mlp" in name:
        lowerCAmelCase__ :  str							      =					name.replace("""cpb_mlp"""							,    """continuous_position_bias_mlp"""			)
    if name == "norm.weight":
        lowerCAmelCase__ :  Optional[int]							      =					'''layernorm.weight'''
    if name == "norm.bias":
        lowerCAmelCase__ :  str							      =					'''layernorm.bias'''

    if "head" in name:
        lowerCAmelCase__ :  int							      =					name.replace("""head"""							,    """classifier"""			)
    else:
        lowerCAmelCase__ :  Union[str, Any]							      =					'''swinv2.''' + name

    return name
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        lowerCAmelCase__ :  int							      =					orig_state_dict.pop(snake_case_			)

        if "mask" in key:
            continue
        elif "qkv" in key:
            lowerCAmelCase__ :  str							      =					key.split("""."""			)
            lowerCAmelCase__ :  int							      =					int(key_split[1]			)
            lowerCAmelCase__ :  Optional[int]							      =					int(key_split[3]			)
            lowerCAmelCase__ :  Dict							      =					model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                lowerCAmelCase__ :  Dict							      =					val[:dim, :]
                lowerCAmelCase__ :  Optional[Any]							      =					val[dim : dim * 2, :]
                lowerCAmelCase__ :  Optional[Any]							      =					val[-dim:, :]
            else:
                lowerCAmelCase__ :  Any							      =					val[:dim]
                lowerCAmelCase__ :  List[str]							      =					val[
                    dim : dim * 2
                ]
                lowerCAmelCase__ :  Tuple							      =					val[-dim:]
        else:
            lowerCAmelCase__ :  Optional[Any]							      =					val

    return orig_state_dict
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[int]							      =					timm.create_model(snake_case_							,    pretrained=snake_case_			)
    timm_model.eval()

    lowerCAmelCase__ :  Any							      =					get_swinva_config(snake_case_			)
    lowerCAmelCase__ :  str							      =					SwinvaForImageClassification(snake_case_			)
    model.eval()

    lowerCAmelCase__ :  int							      =					convert_state_dict(timm_model.state_dict()							,    snake_case_			)
    model.load_state_dict(snake_case_			)

    lowerCAmelCase__ :  Union[str, Any]							      =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''

    lowerCAmelCase__ :  Optional[Any]							      =					AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""							,    """-"""			)			)			)
    lowerCAmelCase__ :  Union[str, Any]							      =					Image.open(requests.get(snake_case_							,    stream=snake_case_			).raw			)
    lowerCAmelCase__ :  List[Any]							      =					image_processor(images=snake_case_							,    return_tensors="""pt"""			)

    lowerCAmelCase__ :  Optional[int]							      =					timm_model(inputs["""pixel_values"""]			)
    lowerCAmelCase__ :  List[Any]							      =					model(**snake_case_			).logits

    assert torch.allclose(snake_case_							,    snake_case_							,    atol=1e-3			)

    print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""			)
    model.save_pretrained(snake_case_			)

    print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
    image_processor.save_pretrained(snake_case_			)

    model.push_to_hub(
        repo_path_or_name=Path(snake_case_							,    snake_case_			)							,    organization="""nandwalritik"""							,    commit_message="""Add model"""							,    )


if __name__ == "__main__":
 _lowerCAmelCase      						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--swinv2_name''',
     default='''swinv2_tiny_patch4_window8_256''',
     type=str,
     help='''Name of the Swinv2 timm model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )

 _lowerCAmelCase      						=	parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









"""simple docstring"""



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def 		__lowercase     (   snake_case_  :  int						)   ->Tuple:



    '''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 		__lowercase     (   snake_case_  :  str						)   ->Dict:



    '''simple docstring'''







    for char in word:
        __A    :						int						 =     ord(snake_case_						)
        if not _is_chinese_char(snake_case_						):
            return 0
    return 1







def 		__lowercase     (   snake_case_  :  List[str]						)   ->List[Any]:



    '''simple docstring'''







    __A    :						str						 =     set()

    for token in tokens:
        __A    :						List[Any]						 =     len(snake_case_						) > 1 and is_chinese(snake_case_						)
        if chinese_word:
            word_set.add(snake_case_						)
    __A    :						Any						 =     list(snake_case_						)
    return word_list







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  set()						)   ->Any:



    '''simple docstring'''







    if not chinese_word_set:
        return bert_tokens
    __A    :						List[Any]						 =     max([len(snake_case_						) for w in chinese_word_set]						)

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







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  LTP				,snake_case_  :  BertTokenizer						)   ->Dict:



    '''simple docstring'''







    __A    :						Optional[Any]						 =     []

    for i in range(0				,len(snake_case_						)				,100						):
        __A    :						int						 =     ltp_tokenizer.seg(lines[i : i + 100]						)[0]
        __A    :						List[Any]						 =     [get_chinese_word(snake_case_						) for r in res]
        ltp_res.extend(snake_case_						)
    assert len(snake_case_						) == len(snake_case_						)

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

    __A    :						Optional[int]						 =     []
    for input_ids, chinese_word in zip(snake_case_				,snake_case_						):
        __A    :						List[str]						 =     []
        for id in input_ids:
            __A    :						Tuple						 =     bert_tokenizer._convert_id_to_token(snake_case_						)
            input_tokens.append(snake_case_						)
        __A    :						Optional[int]						 =     add_sub_symbol(snake_case_				,snake_case_						)
        __A    :						Optional[Any]						 =     []
        # 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] == "##":
                __A    :						Optional[Any]						 =     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 		__lowercase     (   snake_case_  :  int						)   ->List[Any]:



    '''simple docstring'''







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

    __A    :						Optional[Any]						 =     prepare_ref(snake_case_				,snake_case_				,snake_case_						)

    with open(args.save_path				,'''w'''				,encoding='''utf-8'''						) as f:
        __A    :						int						 =     [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""",
           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""")

       a_ =			parser.parse_args()
       main(args)




