


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[Any]				)->Tuple:
   '''simple docstring'''






   snake_case_            =       SwinvaConfig()
   snake_case_            =       swinva_name.split("_"				)

   snake_case_            =       name_split[1]
   if "to" in name_split[3]:
      snake_case_            =       int(name_split[3][-3:]				)
   else:
      snake_case_            =       int(name_split[3]				)
   if "to" in name_split[2]:
      snake_case_            =       int(name_split[2][-2:]				)
   else:
      snake_case_            =       int(name_split[2][6:]				)

   if model_size == "tiny":
      snake_case_            =       96
      snake_case_            =       (2, 2, 6, 2)
      snake_case_            =       (3, 6, 12, 24)
   elif model_size == "small":
      snake_case_            =       96
      snake_case_            =       (2, 2, 18, 2)
      snake_case_            =       (3, 6, 12, 24)
   elif model_size == "base":
      snake_case_            =       128
      snake_case_            =       (2, 2, 18, 2)
      snake_case_            =       (4, 8, 16, 32)
   else:
      snake_case_            =       192
      snake_case_            =       (2, 2, 18, 2)
      snake_case_            =       (6, 12, 24, 48)

   if "to" in swinva_name:
      snake_case_            =       (12, 12, 12, 6)

   if ("22k" in swinva_name) and ("to" not in swinva_name):
      snake_case_            =       21_841
      snake_case_            =       "huggingface/label-files"
      snake_case_            =       "imagenet-22k-id2label.json"
      snake_case_            =       json.load(open(hf_hub_download(lowerCAmelCase_		,    lowerCAmelCase_		,    repo_type="dataset"				)		,    "r"				)				)
      snake_case_            =       {int(lowerCAmelCase_				): v for k, v in idalabel.items()}
      snake_case_            =       idalabel
      snake_case_            =       {v: k for k, v in idalabel.items()}

   else:
      snake_case_            =       1_000
      snake_case_            =       "huggingface/label-files"
      snake_case_            =       "imagenet-1k-id2label.json"
      snake_case_            =       json.load(open(hf_hub_download(lowerCAmelCase_		,    lowerCAmelCase_		,    repo_type="dataset"				)		,    "r"				)				)
      snake_case_            =       {int(lowerCAmelCase_				): v for k, v in idalabel.items()}
      snake_case_            =       idalabel
      snake_case_            =       {v: k for k, v in idalabel.items()}

   snake_case_            =       img_size
   snake_case_            =       num_classes
   snake_case_            =       embed_dim
   snake_case_            =       depths
   snake_case_            =       num_heads
   snake_case_            =       window_size

   return config



def       _lowerCAmelCase    (				lowerCAmelCase_				:Any				)->List[str]:
   '''simple docstring'''






   if "patch_embed.proj" in name:
      snake_case_            =       name.replace("patch_embed.proj"		,    "embeddings.patch_embeddings.projection"				)
   if "patch_embed.norm" in name:
      snake_case_            =       name.replace("patch_embed.norm"		,    "embeddings.norm"				)
   if "layers" in name:
      snake_case_            =       "encoder." + name
   if "attn.proj" in name:
      snake_case_            =       name.replace("attn.proj"		,    "attention.output.dense"				)
   if "attn" in name:
      snake_case_            =       name.replace("attn"		,    "attention.self"				)
   if "norm1" in name:
      snake_case_            =       name.replace("norm1"		,    "layernorm_before"				)
   if "norm2" in name:
      snake_case_            =       name.replace("norm2"		,    "layernorm_after"				)
   if "mlp.fc1" in name:
      snake_case_            =       name.replace("mlp.fc1"		,    "intermediate.dense"				)
   if "mlp.fc2" in name:
      snake_case_            =       name.replace("mlp.fc2"		,    "output.dense"				)
   if "q_bias" in name:
      snake_case_            =       name.replace("q_bias"		,    "query.bias"				)
   if "k_bias" in name:
      snake_case_            =       name.replace("k_bias"		,    "key.bias"				)
   if "v_bias" in name:
      snake_case_            =       name.replace("v_bias"		,    "value.bias"				)
   if "cpb_mlp" in name:
      snake_case_            =       name.replace("cpb_mlp"		,    "continuous_position_bias_mlp"				)
   if name == "norm.weight":
      snake_case_            =       "layernorm.weight"
   if name == "norm.bias":
      snake_case_            =       "layernorm.bias"

   if "head" in name:
      snake_case_            =       name.replace("head"		,    "classifier"				)
   else:
      snake_case_            =       "swinv2." + name

   return name



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:Union[str, Any]				)->List[Any]:
   '''simple docstring'''






   for key in orig_state_dict.copy().keys():
      snake_case_            =       orig_state_dict.pop(lowerCAmelCase_				)

      if "mask" in key:
         continue
      elif "qkv" in key:
         snake_case_            =       key.split("."				)
         snake_case_            =       int(key_split[1]				)
         snake_case_            =       int(key_split[3]				)
         snake_case_            =       model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            snake_case_            =       val[:dim, :]
            snake_case_            =       val[dim : dim * 2, :]
            snake_case_            =       val[-dim:, :]
         else:
            snake_case_            =       val[:dim]
            snake_case_            =       val[
                dim : dim * 2
            ]
            snake_case_            =       val[-dim:]
      else:
         snake_case_            =       val

   return orig_state_dict



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:str				)->str:
   '''simple docstring'''






   snake_case_            =       timm.create_model(lowerCAmelCase_		,    pretrained=lowerCAmelCase_				)
   timm_model.eval()

   snake_case_            =       get_swinva_config(lowerCAmelCase_				)
   snake_case_            =       SwinvaForImageClassification(lowerCAmelCase_				)
   model.eval()

   snake_case_            =       convert_state_dict(timm_model.state_dict()		,    lowerCAmelCase_				)
   model.load_state_dict(lowerCAmelCase_				)

   snake_case_            =       "http://images.cocodataset.org/val2017/000000039769.jpg"

   snake_case_            =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"		,    "-"				)				)				)
   snake_case_            =       Image.open(requests.get(lowerCAmelCase_		,    stream=lowerCAmelCase_				).raw				)
   snake_case_            =       image_processor(images=lowerCAmelCase_		,    return_tensors="pt"				)

   snake_case_            =       timm_model(inputs["pixel_values"]				)
   snake_case_            =       model(**lowerCAmelCase_				).logits

   assert torch.allclose(lowerCAmelCase_		,    lowerCAmelCase_		,    atol=1e-3				)

   print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''				)
   model.save_pretrained(lowerCAmelCase_				)

   print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
   image_processor.save_pretrained(lowerCAmelCase_				)

   model.push_to_hub(
       repo_path_or_name=Path(lowerCAmelCase_		,    lowerCAmelCase_				)		,    organization="nandwalritik"		,    commit_message="Add model"		,    )


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:int			=  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--swinv2_name''',
       default='''swinv2_tiny_patch4_window8_256''',
       type=str,
       help='''Name of the Swinv2 timm model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
   )

   SCREAMING_SNAKE_CASE						:List[Any]			=  parser.parse_args()
   convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)








def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				)->str:
   '''simple docstring'''






   if not isinstance(lowerCAmelCase_		,    lowerCAmelCase_				):
      raise ValueError("iterations must be defined as integers"				)
   if not isinstance(lowerCAmelCase_		,    lowerCAmelCase_				) or not number >= 1:
      raise ValueError(
          "starting number must be\n                         and integer and be more than 0"				)
   if not iterations >= 1:
      raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

   snake_case_            =       ""
   while number <= iterations:
      if number % 3 == 0:
         out += "Fizz"
      if number % 5 == 0:
         out += "Buzz"
      if 0 not in (number % 3, number % 5):
         out += str(lowerCAmelCase_				)

      # print(out)
      number += 1
      out += " "
   return out


if __name__ == "__main__":
   import doctest

   doctest.testmod()




