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   __A		(			_A					):



 """simple docstring"""


 __a       =  []
 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   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  []
 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   __A		(			_A					):



 """simple docstring"""


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



def   __A		(			):



 """simple docstring"""


 __a       =  []
 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   __A		(			_A							,				_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  "imagenet-1k-id2label.json"
 __a       =  1000

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

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

 __a       =  __a       =  CvtConfig(num_labels=_A							,				idalabel=_A							,				labelaid=_A					)

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

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

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

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

 __a       =  OrderedDict()
 __a       =  []

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

 __a       =  list_of_state_dict + final()
 for gg in list_of_state_dict:
  print(_A					)
 for i in range(len(_A					)					):
  __a       =  original_weights[list_of_state_dict[i][1]]

 model.load_state_dict(_A					)
 model.save_pretrained(_A					)
 image_processor.save_pretrained(_A					)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Tuple							     =		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."""
  )

  SCREAMING_SNAKE_CASE :	Dict							     =		parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



# Function to print upper half of diamond (pyramid)



def   __A		(			_A					):



 """simple docstring"""


 for i in range(0							,				_A					):
  for _ in range(0							,				n - i - 1					):  # printing spaces
   print(" "							,				end=""					)
  for _ in range(0							,				i + 1					):  # printing stars
   print("* "							,				end=""					)
  print()



def   __A		(			_A					):



 """simple docstring"""


 for i in range(_A							,				0							,				-1					):
  for _ in range(_A							,				0							,				-1					):  # printing stars
   print("* "							,				end=""					)
  print()
  for _ in range(n - i + 1							,				0							,				-1					):  # printing spaces
   print(" "							,				end=""					)



def   __A		(			_A					):



 """simple docstring"""


 if n <= 0:
  print("       ...       ....        nothing printing :("					)
  return
 floyd(_A					)  # upper half
 reverse_floyd(_A					)  # lower half


if __name__ == "__main__":
  print(R"""| /\ | |- |  |-  |--| |\  /| |-""")
  print(R"""|/  \| |- |_ |_  |__| | \/ | |_""")
  SCREAMING_SNAKE_CASE :	Tuple							     =		1
  while K:
    SCREAMING_SNAKE_CASE :	Tuple							     =		int(input("""enter the number and , and see the magic : """))
    print()
    pretty_print(user_number)
    SCREAMING_SNAKE_CASE :	Optional[int]							     =		int(input("""press 0 to exit... and 1 to continue..."""))

  print("""Good Bye...""")


