



"""simple docstring"""







import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Any:
		lowercase__   :    Optional[int]				=			[]
		embed.append(
		    (
		        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
		        f"""stage{idx}.patch_embed.proj.weight""",
		    )	)
		embed.append(
		    (
		        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
		        f"""stage{idx}.patch_embed.proj.bias""",
		    )	)
		embed.append(
		    (
		        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
		        f"""stage{idx}.patch_embed.norm.weight""",
		    )	)
		embed.append(
		    (
		        f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
		        f"""stage{idx}.patch_embed.norm.bias""",
		    )	)
		return embed



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Dict:
		lowercase__   :    str				=			[]
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
		        f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
		    )	)
		attention_weights.append(
		    (
		        f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
		        f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
		    )	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")	)
		attention_weights.append(
		    (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")	)
		return attention_weights



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Tuple:
		lowercase__   :    List[str]				=			[]
		token.append((f"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''')	)
		return token



def      __UpperCAmelCase      (    )							->      Optional[int]:
		lowercase__   :    List[str]				=			[]
		head.append(('''layernorm.weight''', '''norm.weight''')	)
		head.append(('''layernorm.bias''', '''norm.bias''')	)
		head.append(('''classifier.weight''', '''head.weight''')	)
		head.append(('''classifier.bias''', '''head.bias''')	)
		return head



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      int:
		lowercase__   :    List[Any]				=			'''imagenet-1k-id2label.json'''
		lowercase__   :    Optional[Any]				=			10_00

		lowercase__   :    Optional[Any]				=			'''huggingface/label-files'''
		lowercase__   :    Dict				=			num_labels
		lowercase__   :    Union[str, Any]				=			json.load(open(cached_download(hf_hub_url(__lowerCamelCase						,     __lowerCamelCase						,     repo_type='''dataset'''	)	)						,     '''r'''	)	)
		lowercase__   :    int				=			{int(__lowerCamelCase	): v for k, v in idalabel.items()}

		lowercase__   :    Optional[Any]				=			idalabel
		lowercase__   :    str				=			{v: k for k, v in idalabel.items()}

		lowercase__   :    Any				=			CvtConfig(num_labels=__lowerCamelCase						,     idalabel=__lowerCamelCase						,     labelaid=__lowerCamelCase	)

		# For depth size 13 (13 = 1+2+10)
		if cvt_model.rsplit('''/'''						,     1	)[-1][4:6] == "13":
				lowercase__   :    int				=			[1, 2, 10]

		# For depth size 21 (21 = 1+4+16)
		elif cvt_model.rsplit('''/'''						,     1	)[-1][4:6] == "21":
				lowercase__   :    int				=			[1, 4, 16]

		# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
		else:
				lowercase__   :    List[Any]				=			[2, 2, 20]
				lowercase__   :    Any				=			[3, 12, 16]
				lowercase__   :    Tuple				=			[1_92, 7_68, 10_24]

		lowercase__   :    List[Any]				=			CvtForImageClassification(__lowerCamelCase	)
		lowercase__   :    str				=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	)
		lowercase__   :    List[str]				=			image_size
		lowercase__   :    Union[str, Any]				=			torch.load(__lowerCamelCase						,     map_location=torch.device('''cpu'''	)	)

		lowercase__   :    int				=			OrderedDict()
		lowercase__   :    List[Any]				=			[]

		for idx in range(len(config.depth	)	):
				if config.cls_token[idx]:
						lowercase__   :    Any				=			list_of_state_dict + cls_token(__lowerCamelCase	)
				lowercase__   :    Any				=			list_of_state_dict + embeddings(__lowerCamelCase	)
				for cnt in range(config.depth[idx]	):
						lowercase__   :    Tuple				=			list_of_state_dict + attention(__lowerCamelCase						,     __lowerCamelCase	)

		lowercase__   :    List[Any]				=			list_of_state_dict + final()
		for gg in list_of_state_dict:
				print(__lowerCamelCase	)
		for i in range(len(__lowerCamelCase	)	):
				lowercase__   :    Optional[Any]				=			original_weights[list_of_state_dict[i][1]]

		model.load_state_dict(__lowerCamelCase	)
		model.save_pretrained(__lowerCamelCase	)
		image_processor.save_pretrained(__lowerCamelCase	)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							parser.add_argument(
							    '--cvt_model',
							    default='cvt-w24',
							    type=str,
							    help='Name of the cvt model you\'d like to convert.',
							)
							parser.add_argument(
							    '--image_size',
							    default=384,
							    type=int,
							    help='Input Image Size',
							)
							parser.add_argument(
							    '--cvt_file_name',
							    default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
							    type=str,
							    help='Input Image Size',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)

							lowerCAmelCase_   						=   parser.parse_args()
							convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_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)




