






'''simple docstring'''
import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Tuple           =     SwinConfig()
   lowercase_	:    Optional[Any]           =     swin_name.split("_"							)

   lowercase_	:    int           =     name_split[1]
   lowercase_	:    List[Any]           =     int(name_split[4]							)
   lowercase_	:    Optional[int]           =     int(name_split[3][-1]							)

   if model_size == "tiny":
      lowercase_	:    List[Any]           =     96
      lowercase_	:    Dict           =     (2, 2, 6, 2)
      lowercase_	:    str           =     (3, 6, 12, 24)
   elif model_size == "small":
      lowercase_	:    Optional[Any]           =     96
      lowercase_	:    List[Any]           =     (2, 2, 18, 2)
      lowercase_	:    Any           =     (3, 6, 12, 24)
   elif model_size == "base":
      lowercase_	:    str           =     128
      lowercase_	:    int           =     (2, 2, 18, 2)
      lowercase_	:    int           =     (4, 8, 16, 32)
   else:
      lowercase_	:    Tuple           =     192
      lowercase_	:    Any           =     (2, 2, 18, 2)
      lowercase_	:    Optional[Any]           =     (6, 12, 24, 48)

   if "in22k" in swin_name:
      lowercase_	:    Optional[int]           =     2_1841
   else:
      lowercase_	:    List[Any]           =     1000
      lowercase_	:    str           =     "huggingface/label-files"
      lowercase_	:    Tuple           =     "imagenet-1k-id2label.json"
      lowercase_	:    Dict           =     json.load(open(hf_hub_download(_UpperCamelCase						,		_UpperCamelCase						,		repo_type="dataset"							)						,		"r"							)							)
      lowercase_	:    List[str]           =     {int(_UpperCamelCase							): v for k, v in idalabel.items()}
      lowercase_	:    Optional[Any]           =     idalabel
      lowercase_	:    Optional[int]           =     {v: k for k, v in idalabel.items()}

   lowercase_	:    Dict           =     img_size
   lowercase_	:    List[str]           =     num_classes
   lowercase_	:    str           =     embed_dim
   lowercase_	:    List[str]           =     depths
   lowercase_	:    List[str]           =     num_heads
   lowercase_	:    Optional[Any]           =     window_size

   return config




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   if "patch_embed.proj" in name:
      lowercase_	:    Optional[int]           =     name.replace("patch_embed.proj"						,		"embeddings.patch_embeddings.projection"							)
   if "patch_embed.norm" in name:
      lowercase_	:    int           =     name.replace("patch_embed.norm"						,		"embeddings.norm"							)
   if "layers" in name:
      lowercase_	:    List[Any]           =     "encoder." + name
   if "attn.proj" in name:
      lowercase_	:    List[str]           =     name.replace("attn.proj"						,		"attention.output.dense"							)
   if "attn" in name:
      lowercase_	:    Tuple           =     name.replace("attn"						,		"attention.self"							)
   if "norm1" in name:
      lowercase_	:    List[str]           =     name.replace("norm1"						,		"layernorm_before"							)
   if "norm2" in name:
      lowercase_	:    List[Any]           =     name.replace("norm2"						,		"layernorm_after"							)
   if "mlp.fc1" in name:
      lowercase_	:    Union[str, Any]           =     name.replace("mlp.fc1"						,		"intermediate.dense"							)
   if "mlp.fc2" in name:
      lowercase_	:    Optional[int]           =     name.replace("mlp.fc2"						,		"output.dense"							)

   if name == "norm.weight":
      lowercase_	:    List[str]           =     "layernorm.weight"
   if name == "norm.bias":
      lowercase_	:    Optional[int]           =     "layernorm.bias"

   if "head" in name:
      lowercase_	:    Optional[Any]           =     name.replace("head"						,		"classifier"							)
   else:
      lowercase_	:    int           =     "swin." + name

   return name




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   for key in orig_state_dict.copy().keys():
      lowercase_	:    Any           =     orig_state_dict.pop(_UpperCamelCase							)

      if "mask" in key:
         continue
      elif "qkv" in key:
         lowercase_	:    Optional[Any]           =     key.split("."							)
         lowercase_	:    str           =     int(key_split[1]							)
         lowercase_	:    Dict           =     int(key_split[3]							)
         lowercase_	:    int           =     model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            lowercase_	:    Union[str, Any]           =     val[:dim, :]
            lowercase_	:    int           =     val[
                dim : dim * 2, :
            ]
            lowercase_	:    Union[str, Any]           =     val[-dim:, :]
         else:
            lowercase_	:    Dict           =     val[
                :dim
            ]
            lowercase_	:    str           =     val[
                dim : dim * 2
            ]
            lowercase_	:    List[Any]           =     val[
                -dim:
            ]
      else:
         lowercase_	:    str           =     val

   return orig_state_dict




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     timm.create_model(_UpperCamelCase						,		pretrained=_UpperCamelCase							)
   timm_model.eval()

   lowercase_	:    List[Any]           =     get_swin_config(_UpperCamelCase							)
   lowercase_	:    str           =     SwinForImageClassification(_UpperCamelCase							)
   model.eval()

   lowercase_	:    Optional[int]           =     convert_state_dict(timm_model.state_dict()						,		_UpperCamelCase							)
   model.load_state_dict(_UpperCamelCase							)

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

   lowercase_	:    Union[str, Any]           =     AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"						,		"-"							)							)							)
   lowercase_	:    List[str]           =     Image.open(requests.get(_UpperCamelCase						,		stream=_UpperCamelCase							).raw							)
   lowercase_	:    int           =     image_processor(images=_UpperCamelCase						,		return_tensors="pt"							)

   lowercase_	:    Union[str, Any]           =     timm_model(inputs["pixel_values"]							)
   lowercase_	:    int           =     model(**_UpperCamelCase							).logits

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

   print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""							)
   model.save_pretrained(_UpperCamelCase							)

   print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
   image_processor.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
 UpperCamelCase__ =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--swin_name',
     default='swin_tiny_patch4_window7_224',
     type=str,
     help='Name of the Swin 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.'
 )

 UpperCamelCase__ =			parser.parse_args()
 convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   def update_area_of_max_square(_UpperCamelCase						,		_UpperCamelCase							) -> int:
      # BASE CASE
      if row >= rows or col >= cols:
         return 0

      lowercase_	:    List[str]           =     update_area_of_max_square(_UpperCamelCase						,		col + 1							)
      lowercase_	:    List[Any]           =     update_area_of_max_square(row + 1						,		col + 1							)
      lowercase_	:    Tuple           =     update_area_of_max_square(row + 1						,		_UpperCamelCase							)

      if mat[row][col]:
         lowercase_	:    Optional[int]           =     1 + min([right, diagonal, down]							)
         lowercase_	:    Any           =     max(largest_square_area[0]						,		_UpperCamelCase							)
         return sub_problem_sol
      else:
         return 0

   lowercase_	:    int           =     [0]
   update_area_of_max_square(0						,		0							)
   return largest_square_area[0]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   def update_area_of_max_square_using_dp_array(
       _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							) -> int:
      if row >= rows or col >= cols:
         return 0
      if dp_array[row][col] != -1:
         return dp_array[row][col]

      lowercase_	:    Dict           =     update_area_of_max_square_using_dp_array(_UpperCamelCase						,		col + 1						,		_UpperCamelCase							)
      lowercase_	:    str           =     update_area_of_max_square_using_dp_array(row + 1						,		col + 1						,		_UpperCamelCase							)
      lowercase_	:    Optional[Any]           =     update_area_of_max_square_using_dp_array(row + 1						,		_UpperCamelCase						,		_UpperCamelCase							)

      if mat[row][col]:
         lowercase_	:    Tuple           =     1 + min([right, diagonal, down]							)
         lowercase_	:    int           =     max(largest_square_area[0]						,		_UpperCamelCase							)
         lowercase_	:    Dict           =     sub_problem_sol
         return sub_problem_sol
      else:
         return 0

   lowercase_	:    Any           =     [0]
   lowercase_	:    Optional[int]           =     [[-1] * cols for _ in range(_UpperCamelCase							)]
   update_area_of_max_square_using_dp_array(0						,		0						,		_UpperCamelCase							)

   return largest_square_area[0]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    str           =     [[0] * (cols + 1) for _ in range(rows + 1							)]
   lowercase_	:    List[Any]           =     0
   for row in range(rows - 1						,		-1						,		-1							):
      for col in range(cols - 1						,		-1						,		-1							):
         lowercase_	:    Tuple           =     dp_array[row][col + 1]
         lowercase_	:    List[str]           =     dp_array[row + 1][col + 1]
         lowercase_	:    List[Any]           =     dp_array[row + 1][col]

         if mat[row][col] == 1:
            lowercase_	:    Any           =     1 + min(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
            lowercase_	:    Any           =     max(dp_array[row][col]						,		_UpperCamelCase							)
         else:
            lowercase_	:    int           =     0

   return largest_square_area




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     [0] * (cols + 1)
   lowercase_	:    Union[str, Any]           =     [0] * (cols + 1)
   lowercase_	:    int           =     0
   for row in range(rows - 1						,		-1						,		-1							):
      for col in range(cols - 1						,		-1						,		-1							):
         lowercase_	:    Dict           =     current_row[col + 1]
         lowercase_	:    List[Any]           =     next_row[col + 1]
         lowercase_	:    Tuple           =     next_row[col]

         if mat[row][col] == 1:
            lowercase_	:    Dict           =     1 + min(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
            lowercase_	:    int           =     max(current_row[col]						,		_UpperCamelCase							)
         else:
            lowercase_	:    Tuple           =     0
      lowercase_	:    Optional[Any]           =     current_row

   return largest_square_area


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))




