




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 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Any								=[]
				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 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	List[str]								=[]
				token.append((F"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")   )
				return token




def 		lowerCAmelCase_   (	):
				__magic_name__	:	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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Optional[int]								="""imagenet-1k-id2label.json"""
				__magic_name__	:	List[Any]								=1000

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

				__magic_name__	:	List[str]								=idalabel
				__magic_name__	:	Tuple								={v: k for k, v in idalabel.items()}

				__magic_name__	:	List[str]								=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":
								__magic_name__	:	List[str]								=[1, 2, 10]

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

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

				__magic_name__	:	Optional[Any]								=CvtForImageClassification(lowerCamelCase   )
				__magic_name__	:	int								=AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""   )
				__magic_name__	:	Dict								=image_size
				__magic_name__	:	Optional[Any]								=torch.load(lowerCamelCase	,     map_location=torch.device("""cpu"""   )   )

				__magic_name__	:	List[Any]								=OrderedDict()
				__magic_name__	:	Tuple								=[]

				for idx in range(len(config.depth   )   ):
								if config.cls_token[idx]:
												__magic_name__	:	List[Any]								=list_of_state_dict + cls_token(lowerCamelCase   )
								__magic_name__	:	List[Any]								=list_of_state_dict + embeddings(lowerCamelCase   )
								for cnt in range(config.depth[idx]   ):
												__magic_name__	:	Union[str, Any]								=list_of_state_dict + attention(lowerCamelCase	,     lowerCamelCase   )

				__magic_name__	:	List[str]								=list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(lowerCamelCase   )
				for i in range(len(lowerCamelCase   )   ):
								__magic_name__	:	Dict								=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__":
							UpperCAmelCase_		:				Optional[Any]									=     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."
							)

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










import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version




def 		lowerCAmelCase_   (	*lowerCamelCase	,     lowerCamelCase = None	,     lowerCamelCase=True	,     lowerCamelCase=2   ):
				from .. import __version__

				__magic_name__	:	Optional[int]								=take_from
				__magic_name__	:	Tuple								=()
				if not isinstance(args[0]	,     lowerCamelCase   ):
								__magic_name__	:	List[Any]								=(args,)

				for attribute, version_name, message in args:
								if version.parse(version.parse(lowerCamelCase   ).base_version   ) >= version.parse(lowerCamelCase   ):
												raise ValueError(
												    F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
												    F" version {__version__} is >= {version_name}"   )

								__magic_name__	:	List[str]								=None
								if isinstance(lowerCamelCase	,     lowerCamelCase   ) and attribute in deprecated_kwargs:
												values += (deprecated_kwargs.pop(lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
								elif hasattr(lowerCamelCase	,     lowerCamelCase   ):
												values += (getattr(lowerCamelCase	,     lowerCamelCase   ),)
												__magic_name__	:	List[str]								=F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
								elif deprecated_kwargs is None:
												__magic_name__	:	List[str]								=F"`{attribute}` is deprecated and will be removed in version {version_name}."

								if warning is not None:
												__magic_name__	:	List[Any]								=warning + """ """ if standard_warn else """"""
												warnings.warn(warning + message	,     lowerCamelCase	,     stacklevel=lowerCamelCase   )

				if isinstance(lowerCamelCase	,     lowerCamelCase   ) and len(lowerCamelCase   ) > 0:
								__magic_name__	:	List[Any]								=inspect.getouterframes(inspect.currentframe()   )[1]
								__magic_name__	:	Optional[int]								=call_frame.filename
								__magic_name__	:	Tuple								=call_frame.lineno
								__magic_name__	:	str								=call_frame.function
								__magic_name__				, __magic_name__	:	Optional[int]								=next(iter(deprecated_kwargs.items()   )   )
								raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"   )

				if len(lowerCamelCase   ) == 0:
								return
				elif len(lowerCamelCase   ) == 1:
								return values[0]
				return values




