





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCAmelCase							:			Dict        =							logging.get_logger(__name__)

_UpperCAmelCase							:			Tuple        =							{
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}

class   lowerCAmelCase				(   __UpperCamelCase,    __UpperCamelCase     ):
 UpperCAmelCase__						=						"""resnet"""
 UpperCAmelCase__						=						["""basic""", """bottleneck"""]
 def __init__( self    : Tuple     ,	UpperCAmelCase    : int=3     ,	UpperCAmelCase    : List[Any]=64     ,	UpperCAmelCase    : Optional[int]=[256, 512, 1024, 2048]     ,	UpperCAmelCase    : Optional[Any]=[3, 4, 6, 3]     ,	UpperCAmelCase    : List[str]="bottleneck"     ,	UpperCAmelCase    : List[str]="relu"     ,	UpperCAmelCase    : Optional[Any]=False     ,	UpperCAmelCase    : str=None     ,	UpperCAmelCase    : str=None     ,	**UpperCAmelCase    : str     ,	)     ->      Tuple:
     super().__init__(**UpperCAmelCase      )
     if layer_type not in self.layer_types:
         raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types      )}"""      )
     lowerCamelCase__       : int   					=						num_channels
     lowerCamelCase__       : Optional[Any]   					=						embedding_size
     lowerCamelCase__       : Optional[int]   					=						hidden_sizes
     lowerCamelCase__       : Optional[Any]   					=						depths
     lowerCamelCase__       : Optional[Any]   					=						layer_type
     lowerCamelCase__       : str   					=						hidden_act
     lowerCamelCase__       : Union[str, Any]   					=						downsample_in_first_stage
     lowerCamelCase__       : Optional[Any]   					=						['stem'] + [F"""stage{idx}""" for idx in range(1     ,	len(UpperCAmelCase      ) + 1      )]
     lowerCamelCase__					,				lowerCamelCase__       : List[str]   					=						get_aligned_output_features_output_indices(
         out_features=UpperCAmelCase     ,	out_indices=UpperCAmelCase     ,	stage_names=self.stage_names      )


class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						version.parse("""1.11"""     )
 @property
 def 	A_      ( self    : List[Any]      )     ->      Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]      )







 @property
 def 	A_      ( self    : Any      )     ->      float:
     return 1e-3












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					)     ->	Dict:
    lowerCamelCase__       : 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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	List[str]:
    lowerCamelCase__       : Optional[int]   					=						[]
    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					)     ->	str:
    lowerCamelCase__       : Union[str, Any]   					=						[]
    token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')					)
    return token


def 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    lowerCamelCase__       : Tuple   					=						[]
    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					)     ->	Optional[int]:
    lowerCamelCase__       : Union[str, Any]   					=						'imagenet-1k-id2label.json'
    lowerCamelCase__       : Tuple   					=						1000

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

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

    lowerCamelCase__       : int   					=						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__       : Dict   					=						[1, 2, 10]

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

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

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

    lowerCamelCase__       : Dict   					=						OrderedDict()
    lowerCamelCase__       : Union[str, Any]   					=						[]

    for idx in range(len(config.depth					)					):
        if config.cls_token[idx]:
            lowerCamelCase__       : str   					=						list_of_state_dict + cls_token(_UpperCAmelCase					)
        lowerCamelCase__       : Tuple   					=						list_of_state_dict + embeddings(_UpperCAmelCase					)
        for cnt in range(config.depth[idx]					):
            lowerCamelCase__       : List[str]   					=						list_of_state_dict + attention(_UpperCAmelCase		,      _UpperCAmelCase					)

    lowerCamelCase__       : Optional[Any]   					=						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__":
    _UpperCAmelCase							:			int        =							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."""
    )

    _UpperCAmelCase							:			Optional[int]        =							parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





