


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_		:						List[Any]		):




      """simple docstring"""





      a		:str									=			[]
      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_		:						int					,    UpperCAmelCase_		:						Any		):




      """simple docstring"""





      a		:int									=			[]
      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_		:						Union[str, Any]		):




      """simple docstring"""





      a		:Dict									=			[]
      token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')		)
      return token
def 							__lowerCamelCase		(					):




      """simple docstring"""





      a		:Union[str, Any]									=			[]
      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_		:						Union[str, Any]					,    UpperCAmelCase_		:						Tuple					,    UpperCAmelCase_		:						Optional[Any]					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      a		:Any									=			'''imagenet-1k-id2label.json'''
      a		:List[str]									=			1000

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

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

      a		:int									=			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		:Tuple									=			[1, 2, 10]

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

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

      a		:List[str]									=			CvtForImageClassification(UpperCAmelCase_		)
      a		:str									=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''		)
      a		:str									=			image_size
      a		:List[str]									=			torch.load(UpperCAmelCase_					,    map_location=torch.device('''cpu'''		)		)

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

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

      a		:int									=			list_of_state_dict + final()
      for gg in list_of_state_dict:
            print(UpperCAmelCase_		)
      for i in range(len(UpperCAmelCase_		)		):
            a		:List[str]									=			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__":
     snake_case							:       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=3_84,
         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.'''
     )

     snake_case							:       Any										=	parser.parse_args()
     convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
     from scipy.stats import pearsonr, spearmanr
     from sklearn.metrics import fa_score, matthews_corrcoef


snake_case							:       List[str]										=	(
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						Optional[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      return (preds == labels).mean()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:Dict									=			simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)
      a		:Any									=			fa_score(y_true=UpperCAmelCase_					,    y_pred=UpperCAmelCase_		)
      return {
          "acc": acc,
          "f1": fa,
          "acc_and_f1": (acc + fa) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:str									=			pearsonr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      a		:Union[str, Any]									=			spearmanr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
          "corr": (pearson_corr + spearman_corr) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						Dict		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      assert len(UpperCAmelCase_		) == len(UpperCAmelCase_		), F'''Predictions and labels have mismatched lengths {len(UpperCAmelCase_		)} and {len(UpperCAmelCase_		)}'''
      if task_name == "cola":
            return {"mcc": matthews_corrcoef(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "sst-2":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "mrpc":
            return acc_and_fa(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "sts-b":
            return pearson_and_spearman(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "qqp":
            return acc_and_fa(UpperCAmelCase_					,    UpperCAmelCase_		)
      elif task_name == "mnli":
            return {"mnli/acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "mnli-mm":
            return {"mnli-mm/acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "qnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "rte":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "wnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      elif task_name == "hans":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      else:
            raise KeyError(UpperCAmelCase_		)






def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      if len(UpperCAmelCase_		) != len(UpperCAmelCase_		):
            raise ValueError(F'''Predictions and labels have mismatched lengths {len(UpperCAmelCase_		)} and {len(UpperCAmelCase_		)}'''		)
      if task_name == "xnli":
            return {"acc": simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)}
      else:
            raise KeyError(UpperCAmelCase_		)





