




'''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 						snake_case_							(_a	:							str     ):
    UpperCAmelCase			       =		[]
    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 						snake_case_							(_a	:							Union[str, Any]    ,   _a	:							str     ):
    UpperCAmelCase			       =		[]
    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 						snake_case_							(_a	:							Union[str, Any]     ):
    UpperCAmelCase			       =		[]
    token.append((F"cvt.encoder.stages.{idx}.cls_token", '''stage2.cls_token''')     )
    return token





def 						snake_case_							():
    UpperCAmelCase			       =		[]
    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 						snake_case_							(_a	:							Optional[Any]    ,   _a	:							str    ,   _a	:							Dict    ,   _a	:							Optional[Any]     ):
    UpperCAmelCase			       =		'''imagenet-1k-id2label.json'''
    UpperCAmelCase			       =		1_0_0_0

    UpperCAmelCase			       =		'''huggingface/label-files'''
    UpperCAmelCase			       =		num_labels
    UpperCAmelCase			       =		json.load(open(cached_download(hf_hub_url(_a    ,   _a    ,   repo_type='''dataset'''     )     )    ,   '''r'''     )     )
    UpperCAmelCase			       =		{int(_a     ): v for k, v in idalabel.items()}

    UpperCAmelCase			       =		idalabel
    UpperCAmelCase			       =		{v: k for k, v in idalabel.items()}

    UpperCAmelCase			       =		UpperCAmelCase			       =		CvtConfig(num_labels=_a    ,   idalabel=_a    ,   labelaid=_a     )

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit('''/'''    ,   1     )[-1][4:6] == "13":
        UpperCAmelCase			       =		[1, 2, 1_0]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('''/'''    ,   1     )[-1][4:6] == "21":
        UpperCAmelCase			       =		[1, 4, 1_6]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        UpperCAmelCase			       =		[2, 2, 2_0]
        UpperCAmelCase			       =		[3, 1_2, 1_6]
        UpperCAmelCase			       =		[1_9_2, 7_6_8, 1_0_2_4]

    UpperCAmelCase			       =		CvtForImageClassification(_a     )
    UpperCAmelCase			       =		AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''     )
    UpperCAmelCase			       =		image_size
    UpperCAmelCase			       =		torch.load(_a    ,   map_location=torch.device('''cpu'''     )     )

    UpperCAmelCase			       =		OrderedDict()
    UpperCAmelCase			       =		[]

    for idx in range(len(config.depth     )     ):
        if config.cls_token[idx]:
            UpperCAmelCase			       =		list_of_state_dict + cls_token(_a     )
        UpperCAmelCase			       =		list_of_state_dict + embeddings(_a     )
        for cnt in range(config.depth[idx]     ):
            UpperCAmelCase			       =		list_of_state_dict + attention(_a    ,   _a     )

    UpperCAmelCase			       =		list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_a     )
    for i in range(len(_a     )     ):
        UpperCAmelCase			       =		original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_a     )
    model.save_pretrained(_a     )
    image_processor.save_pretrained(_a     )


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

if __name__ == "__main__":
   A     =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=3_84,
       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.'
   )

   A     =parser.parse_args()
   convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









"""simple docstring"""
import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_lowerCAmelCase    :str     =					object()

# For specifying empty leaf dict `{}`
_lowerCAmelCase    :str     =					object()





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  Dict											=    tuple((re.compile(x + '''$'''							) for x in qs)							)
    for i in range(len(UpperCamelCase__							) - len(UpperCamelCase__							) + 1							):
        _UpperCAmelCase							:  str											=    [x.match(UpperCamelCase__							) for x, y in zip(UpperCamelCase__     ,       ks[i:]							)]
        if matches and all(UpperCamelCase__							):
            return True
    return False





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]							):
    def replace(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Tuple							):
        for rule, replacement in rules:
            if _match(UpperCamelCase__     ,       UpperCamelCase__							):
                return replacement
        return val

    return replace





def  lowerCamelCase_						():
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        (("transformer", "wte", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("attention", "out_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("mlp", "c_fc", "bias"), P('''mp'''							)),
        (("mlp", "c_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("mlp", "c_proj", "bias"), None),
        # layer norms
        ((r"ln_\d+", "bias"), None),
        ((r"\d+", r"ln_\d+", "scale"), None),
        (("ln_f", "bias"), None),
        (("ln_f", "scale"), None),
    ]





def  lowerCamelCase_						(UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[str]											=    _get_partition_rules()
    _UpperCAmelCase							:  List[str]											=    _replacement_rules(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    {k: _unmatched for k in flatten_dict(UpperCamelCase__							)}
    _UpperCAmelCase							:  int											=    {k: replace(UpperCamelCase__     ,       UpperCamelCase__							) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(UpperCamelCase__							)							)


