





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:
       UpperCamelCase_          =  []
       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_					,		UpperCamelCase_  )     ->		Optional[Any]:
       UpperCamelCase_          =  []
       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]:
       UpperCamelCase_          =  []
       token.append((F'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token")  )
       return token




def  lowerCAmelCase_ (							)     ->		Optional[int]:
       UpperCamelCase_          =  []
       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_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Optional[int]:
       UpperCamelCase_          =  "imagenet-1k-id2label.json"
       UpperCamelCase_          =  1000

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

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

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

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

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

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

       UpperCamelCase_          =  OrderedDict()
       UpperCamelCase_          =  []

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

       UpperCamelCase_          =  list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(UpperCamelCase_  )
       for i in range(len(UpperCamelCase_  )  ):
              UpperCamelCase_          =  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 unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory




def  lowerCAmelCase_ (							)     ->		Dict:
       raise RuntimeError("CUDA out of memory."  )



class 							_UpperCamelCase					(	nn.Module     ):

       def __init__(		self: int		)       -> List[Any]:




              """simple docstring"""


              super().__init__()
              UpperCamelCase_          =  nn.Linear(3					,   4		)
              UpperCamelCase_          =  nn.BatchNormad(4		)
              UpperCamelCase_          =  nn.Linear(4					,   5		)



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[Any]:




              """simple docstring"""


              return self.lineara(self.batchnorm(self.lineara(_SCREAMING_SNAKE_CASE		)		)		)



class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: List[Any]		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: str		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()

              mock_training_loop_function()
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)

       def lowercase (		self: Optional[int]		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()
                     return batch_size, arga

              UpperCamelCase_       , UpperCamelCase_          =  mock_training_loop_function("hello"		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)
              self.assertListEqual([bs, arga]					,   [8, "hello"]		)

       def lowercase (		self: Any		)       -> Optional[int]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=0		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Union[str, Any]		):
                     pass

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("No executable batch size found, reached zero."					,   cm.exception.args[0]		)

       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Optional[int]		):
                     if batch_size > 0:
                            raise_fake_out_of_memory()
                     pass

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("No executable batch size found, reached zero."					,   cm.exception.args[0]		)

       def lowercase (		self: Optional[int]		)       -> Dict:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Tuple		):
                     if batch_size != 8:
                            raise raise_fake_out_of_memory()

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function(128					,   "hello"					,   "world"		)
                     self.assertIn("Batch size was passed into `f`"					,   cm.exception.args[0]		)
                     self.assertIn("`f(arg1='hello', arg2='world')"					,   cm.exception.args[0]		)

       def lowercase (		self: List[str]		)       -> Optional[Any]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[Any]		):
                     raise ValueError("Oops, we had an error!"		)

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("Oops, we had an error!"					,   cm.exception.args[0]		)



       @require_cuda
       def lowercase (		self: Optional[int]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  torch.cuda.memory_allocated()
              UpperCamelCase_          =  ModelForTest()
              model.cuda()
              self.assertGreater(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  release_memory(_SCREAMING_SNAKE_CASE		)
              self.assertEqual(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)

