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





def      lowerCamelCase_					(							):
				lowerCamelCase_         =     []
				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__       ):
				lowerCamelCase_         =     "imagenet-1k-id2label.json"
				lowerCamelCase_         =     1_0_0_0

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

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

				lowerCamelCase_         =     lowerCamelCase_         =     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":
								lowerCamelCase_         =     [1, 2, 1_0]

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

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

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

				lowerCamelCase_         =     OrderedDict()
				lowerCamelCase_         =     []

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

				lowerCamelCase_         =     list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(lowerCamelCase__       )
				for i in range(len(lowerCamelCase__       )       ):
								lowerCamelCase_         =     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__":
			__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_8_4,
			    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)




import re





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]"      ,					str_       )]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     split_input(str_       )
				return "".join(
				    ["".join([char.capitalize() for char in sub_str]       ) for sub_str in string_split]       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     split_input(lowerCamelCase__       )
								if upper:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.upper() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								else:
												lowerCamelCase_         =     "".join(
												    [
												        separator.join([char.lower() for char in sub_str]       )
												        for sub_str in string_split
												    ]       )
								return res_str
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__       ):
				return to_simple_case(lowerCamelCase__       )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				try:
								lowerCamelCase_         =     to_simple_case(lowerCamelCase__       )
								return res_str[0].lower() + res_str[1:]
				except IndexError:
								return "not valid string"





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"_"       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				return to_complex_case(lowerCamelCase__      ,					lowerCamelCase__      ,					"-"       )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


