



'''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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  str											= []
    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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Tuple											= []
    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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= []
    token.append((f"""cvt.encoder.stages.{idx}.cls_token""", """stage2.cls_token""")      )
    return token
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  Any											= []
    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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= 'imagenet-1k-id2label.json'
    lowerCAmelCase__			:  Dict											= 1000

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

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

    lowerCAmelCase__			:  Dict											= CvtConfig(num_labels=_UpperCAmelCase							,   idalabel=_UpperCAmelCase							,   labelaid=_UpperCAmelCase      )

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

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

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        lowerCAmelCase__			:  Dict											= [2, 2, 20]
        lowerCAmelCase__			:  Any											= [3, 12, 16]
        lowerCAmelCase__			:  Tuple											= [192, 768, 1024]

    lowerCAmelCase__			:  str											= CvtForImageClassification(_UpperCAmelCase      )
    lowerCAmelCase__			:  List[Any]											= AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""      )
    lowerCAmelCase__			:  int											= image_size
    lowerCAmelCase__			:  int											= torch.load(_UpperCAmelCase							,   map_location=torch.device("""cpu"""      )      )

    lowerCAmelCase__			:  List[Any]											= OrderedDict()
    lowerCAmelCase__			:  Union[str, Any]											= []

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

    lowerCAmelCase__			:  str											= list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase      )
    for i in range(len(_UpperCAmelCase      )      ):
        lowerCAmelCase__			:  List[str]											= original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_UpperCAmelCase      )
    model.save_pretrained(_UpperCAmelCase      )
    image_processor.save_pretrained(_UpperCAmelCase      )


# 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'''





def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    if any(not isinstance(UpperCamelCase							,   UpperCamelCase      ) or x < 0 for x in sequence      ):
        raise TypeError("""Sequence must be list of non-negative integers"""      )
    for _ in range(len(UpperCamelCase      )      ):
        for i, (rod_upper, rod_lower) in enumerate(zip(UpperCamelCase							,   sequence[1:]      )      ):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
 assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
 assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]


