




"""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      UpperCamelCase__   (						lowercase__     :   Optional[Any]      ):
		snake_case						:  List[Any]    	= []
		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      UpperCamelCase__   (						lowercase__     :   List[str]				,   lowercase__     :   int      ):
		snake_case						:  Dict    	= []
		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      UpperCamelCase__   (						lowercase__     :   Optional[int]      ):
		snake_case						:  List[str]    	= []
		token.append((F'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token")      )
		return token



def      UpperCamelCase__   (						):
		snake_case						:  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      UpperCamelCase__   (						lowercase__     :   List[Any]				,   lowercase__     :   str				,   lowercase__     :   Optional[Any]				,   lowercase__     :   Union[str, Any]      ):
		snake_case						:  Union[str, Any]    	= '''imagenet-1k-id2label.json'''
		snake_case						:  int    	= 1000

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

		snake_case						:  Union[str, Any]    	= idalabel
		snake_case						:  int    	= {v: k for k, v in idalabel.items()}

		snake_case						:  List[Any]    	= CvtConfig(num_labels=lowercase__				,   idalabel=lowercase__				,   labelaid=lowercase__      )

		# For depth size 13 (13 = 1+2+10)
		if cvt_model.rsplit("/"				,   1      )[-1][4:6] == "13":
				snake_case						:  Tuple    	= [1, 2, 10]

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

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

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

		snake_case						:  Tuple    	= OrderedDict()
		snake_case						:  Union[str, Any]    	= []

		for idx in range(len(config.depth      )      ):
				if config.cls_token[idx]:
						snake_case						:  Union[str, Any]    	= list_of_state_dict + cls_token(lowercase__      )
				snake_case						:  Optional[Any]    	= list_of_state_dict + embeddings(lowercase__      )
				for cnt in range(config.depth[idx]      ):
						snake_case						:  Tuple    	= list_of_state_dict + attention(lowercase__				,   lowercase__      )

		snake_case						:  Dict    	= list_of_state_dict + final()
		for gg in list_of_state_dict:
				print(lowercase__      )
		for i in range(len(lowercase__      )      ):
				snake_case						:  List[Any]    	= original_weights[list_of_state_dict[i][1]]

		model.load_state_dict(lowercase__      )
		model.save_pretrained(lowercase__      )
		image_processor.save_pretrained(lowercase__      )


# 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=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."
							)

							__A       						=	parser.parse_args()
							convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase								=	{
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""MobileViTFeatureExtractor"""]
     UpperCamelCase								=	["""MobileViTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileViTForImageClassification""",
         """MobileViTForSemanticSegmentation""",
         """MobileViTModel""",
         """MobileViTPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFMobileViTForImageClassification""",
         """TFMobileViTForSemanticSegmentation""",
         """TFMobileViTModel""",
         """TFMobileViTPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilevit import MobileViTFeatureExtractor
          from .image_processing_mobilevit import MobileViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevit import (
              MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTForImageClassification,
              MobileViTForSemanticSegmentation,
              MobileViTModel,
              MobileViTPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilevit import (
              TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileViTForImageClassification,
              TFMobileViTForSemanticSegmentation,
              TFMobileViTModel,
              TFMobileViTPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




