




'''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'''







from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


A     =logging.get_logger(__name__)


A     =TypeVar('DatasetType', Dataset, IterableDataset)





def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[List[float]] = None    ,   _a	:							Optional[int] = None    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,   ):
    from .arrow_dataset import Dataset
    from .iterable_dataset import IterableDataset

    if not datasets:
        raise ValueError('''Unable to interleave an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
        raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."     )
    if dataset_type is Dataset:
        return _interleave_map_style_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )
    else:
        return _interleave_iterable_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )







def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							int = 0    ,   ):

    if not dsets:
        raise ValueError('''Unable to concatenate an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if dataset_type is Dataset:
        return _concatenate_map_style_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )
    else:
        return _concatenate_iterable_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )




