
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   (	UpperCamelCase:     List[Any]  ):






 """simple docstring"""


 __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 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     int  ):






 """simple docstring"""


 __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 		_UpperCAmelCase   (	UpperCamelCase:     Tuple  ):






 """simple docstring"""


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






def 		_UpperCAmelCase   (	):






 """simple docstring"""


 __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 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     List[Any]  ):






 """simple docstring"""


 __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(__UpperCamelCase			,							__UpperCamelCase			,							repo_type="dataset"  )  )			,							"r"  )  )
 __lowerCAmelCase         =							{int(__UpperCamelCase  ): v for k, v in idalabel.items()}

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

 __lowerCAmelCase         =							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         =							[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(__UpperCamelCase  )
 __lowerCAmelCase         =							AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"  )
 __lowerCAmelCase         =							image_size
 __lowerCAmelCase         =							torch.load(__UpperCamelCase			,							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(__UpperCamelCase  )
  __lowerCAmelCase         =							list_of_state_dict + embeddings(__UpperCamelCase  )
  for cnt in range(config.depth[idx]  ):
   __lowerCAmelCase         =							list_of_state_dict + attention(__UpperCamelCase			,							__UpperCamelCase  )

 __lowerCAmelCase         =							list_of_state_dict + final()
 for gg in list_of_state_dict:
  print(__UpperCamelCase  )
 for i in range(len(__UpperCamelCase  )  ):
  __lowerCAmelCase         =							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=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."
     )

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








import warnings
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


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'segformer'







							def __init__(						self  :  List[str]      ,				snake_case__  :  Any=3      ,				snake_case__  :  int=4      ,				snake_case__  :  Tuple=[2, 2, 2, 2]      ,				snake_case__  :  Optional[int]=[8, 4, 2, 1]      ,				snake_case__  :  Union[str, Any]=[32, 64, 160, 256]      ,				snake_case__  :  str=[7, 3, 3, 3]      ,				snake_case__  :  List[Any]=[4, 2, 2, 2]      ,				snake_case__  :  Tuple=[1, 2, 5, 8]      ,				snake_case__  :  List[str]=[4, 4, 4, 4]      ,				snake_case__  :  Optional[Any]="gelu"      ,				snake_case__  :  Optional[Any]=0.0      ,				snake_case__  :  Any=0.0      ,				snake_case__  :  str=0.1      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Any=0.1      ,				snake_case__  :  List[Any]=1E-6      ,				snake_case__  :  Any=256      ,				snake_case__  :  Optional[Any]=255      ,				**snake_case__  :  Union[str, Any]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__ )

								if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
									warnings.warn(
									    "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
									    " removed, as the behaviour will default to that of reshape_last_stage = True."      ,				snake_case__      ,				)

								__lowerCAmelCase         =							num_channels
								__lowerCAmelCase         =							num_encoder_blocks
								__lowerCAmelCase         =							depths
								__lowerCAmelCase         =							sr_ratios
								__lowerCAmelCase         =							hidden_sizes
								__lowerCAmelCase         =							patch_sizes
								__lowerCAmelCase         =							strides
								__lowerCAmelCase         =							mlp_ratios
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							hidden_act
								__lowerCAmelCase         =							hidden_dropout_prob
								__lowerCAmelCase         =							attention_probs_dropout_prob
								__lowerCAmelCase         =							classifier_dropout_prob
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							drop_path_rate
								__lowerCAmelCase         =							layer_norm_eps
								__lowerCAmelCase         =							decoder_hidden_size
								__lowerCAmelCase         =							kwargs.get("reshape_last_stage"      ,				snake_case__ )
								__lowerCAmelCase         =							semantic_loss_ignore_index

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[str]   							=  version.parse('1.11'					)







							@property
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ] )







							@property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return 1E-4







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								return 12






