



"""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       _lowerCamelCase				(							UpperCAmelCase_ :       str     )	-> int:






	"""simple docstring"""





	A__											=					[]
	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				(							UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       Union[str, Any]     )	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					[]
	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				(							UpperCAmelCase_ :       Tuple     )	-> int:






	"""simple docstring"""





	A__											=					[]
	token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")     )
	return token

def       _lowerCamelCase				(							)	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					[]
	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				(							UpperCAmelCase_ :       Tuple,	UpperCAmelCase_ :       int,	UpperCAmelCase_ :       List[str],	UpperCAmelCase_ :       Tuple     )	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					"imagenet-1k-id2label.json"
	A__											=					1000

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

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

	A__											=					A__											=					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":
		A__											=					[1, 2, 10]

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

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

	A__											=					CvtForImageClassification(UpperCAmelCase_     )
	A__											=					AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"     )
	A__											=					image_size
	A__											=					torch.load(UpperCAmelCase_,	map_location=torch.device("cpu"     )     )

	A__											=					OrderedDict()
	A__											=					[]

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

	A__											=					list_of_state_dict + final()
	for gg in list_of_state_dict:
		print(UpperCAmelCase_     )
	for i in range(len(UpperCAmelCase_     )     ):
		A__											=					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     =							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     =							parser.parse_args()
			convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)








def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = hex_num.strip()
			if not hex_num:
						raise ValueError('''No value was passed to the function'''	)

			__lowercase			       = hex_num[0] == '''-'''
			if is_negative:
						__lowercase			       = hex_num[1:]

			try:
						__lowercase			       = int(_UpperCamelCase							,      16	)
			except ValueError:
						raise ValueError('''Invalid value was passed to the function'''	)

			__lowercase			       = ''''''
			while int_num > 0:
						__lowercase			       = str(int_num % 2	) + bin_str
						int_num >>= 1

			return int(('''-''' + bin_str) if is_negative else bin_str	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


