

"""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   lowercase_	(    _lowercase       :		Optional[Any]	):




     '''simple docstring'''






     UpperCAmelCase			:   str      =				SwinConfig()
     UpperCAmelCase			:   str      =				swin_name.split("_"	)

     UpperCAmelCase			:   Union[str, Any]      =				name_split[1]
     UpperCAmelCase			:   Union[str, Any]      =				int(name_split[4]	)
     UpperCAmelCase			:   List[str]      =				int(name_split[3][-1]	)

     if model_size == "tiny":
          UpperCAmelCase			:   Tuple      =				96
          UpperCAmelCase			:   str      =				(2, 2, 6, 2)
          UpperCAmelCase			:   Tuple      =				(3, 6, 12, 24)
     elif model_size == "small":
          UpperCAmelCase			:   Union[str, Any]      =				96
          UpperCAmelCase			:   Optional[Any]      =				(2, 2, 18, 2)
          UpperCAmelCase			:   Tuple      =				(3, 6, 12, 24)
     elif model_size == "base":
          UpperCAmelCase			:   Tuple      =				1_28
          UpperCAmelCase			:   Optional[int]      =				(2, 2, 18, 2)
          UpperCAmelCase			:   List[str]      =				(4, 8, 16, 32)
     else:
          UpperCAmelCase			:   List[Any]      =				1_92
          UpperCAmelCase			:   Union[str, Any]      =				(2, 2, 18, 2)
          UpperCAmelCase			:   List[str]      =				(6, 12, 24, 48)

     if "in22k" in swin_name:
          UpperCAmelCase			:   Tuple      =				2_18_41
     else:
          UpperCAmelCase			:   List[Any]      =				10_00
          UpperCAmelCase			:   List[Any]      =				"huggingface/label-files"
          UpperCAmelCase			:   List[Any]      =				"imagenet-1k-id2label.json"
          UpperCAmelCase			:   Optional[int]      =				json.load(open(hf_hub_download(_lowercase							,      _lowercase							,      repo_type="dataset"	)							,      "r"	)	)
          UpperCAmelCase			:   Any      =				{int(_lowercase	): v for k, v in idalabel.items()}
          UpperCAmelCase			:   Tuple      =				idalabel
          UpperCAmelCase			:   Tuple      =				{v: k for k, v in idalabel.items()}

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

     return config



def   lowercase_	(    _lowercase       :		List[str]	):




     '''simple docstring'''






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

     if name == "norm.weight":
          UpperCAmelCase			:   Optional[Any]      =				"layernorm.weight"
     if name == "norm.bias":
          UpperCAmelCase			:   Optional[int]      =				"layernorm.bias"

     if "head" in name:
          UpperCAmelCase			:   Dict      =				name.replace("head"							,      "classifier"	)
     else:
          UpperCAmelCase			:   List[str]      =				"swin." + name

     return name



def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		Any	):




     '''simple docstring'''






     for key in orig_state_dict.copy().keys():
          UpperCAmelCase			:   Union[str, Any]      =				orig_state_dict.pop(_lowercase	)

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

               if "weight" in key:
                    UpperCAmelCase			:   Optional[Any]      =				val[:dim, :]
                    UpperCAmelCase			:   str      =				val[
                        dim : dim * 2, :
                    ]
                    UpperCAmelCase			:   Any      =				val[-dim:, :]
               else:
                    UpperCAmelCase			:   str      =				val[
                        :dim
                    ]
                    UpperCAmelCase			:   List[str]      =				val[
                        dim : dim * 2
                    ]
                    UpperCAmelCase			:   List[Any]      =				val[
                        -dim:
                    ]
          else:
               UpperCAmelCase			:   Tuple      =				val

     return orig_state_dict



def   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		Tuple	):




     '''simple docstring'''






     UpperCAmelCase			:   Optional[int]      =				timm.create_model(_lowercase							,      pretrained=_lowercase	)
     timm_model.eval()

     UpperCAmelCase			:   List[str]      =				get_swin_config(_lowercase	)
     UpperCAmelCase			:   Dict      =				SwinForImageClassification(_lowercase	)
     model.eval()

     UpperCAmelCase			:   str      =				convert_state_dict(timm_model.state_dict()							,      _lowercase	)
     model.load_state_dict(_lowercase	)

     UpperCAmelCase			:   Union[str, Any]      =				"http://images.cocodataset.org/val2017/000000039769.jpg"

     UpperCAmelCase			:   Optional[int]      =				AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"							,      "-"	)	)	)
     UpperCAmelCase			:   Dict      =				Image.open(requests.get(_lowercase							,      stream=_lowercase	).raw	)
     UpperCAmelCase			:   Dict      =				image_processor(images=_lowercase							,      return_tensors="pt"	)

     UpperCAmelCase			:   str      =				timm_model(inputs["pixel_values"]	)
     UpperCAmelCase			:   str      =				model(**_lowercase	).logits

     assert torch.allclose(_lowercase							,      _lowercase							,      atol=1E-3	)

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

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


if __name__ == "__main__":
 snake_case_   :   Any        =       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."""
 )

 snake_case_   :   str        =       parser.parse_args()
 convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





"""simple docstring"""






from __future__ import annotations

snake_case_   :   str        =       list[list[int]]

# assigning initial values to the grid
snake_case_   :   Matrix        =       [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
snake_case_   :   Matrix        =       [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]



def   lowercase_	(    _lowercase       :		Matrix							,      _lowercase       :		int							,      _lowercase       :		int							,      _lowercase       :		int	):




					'''simple docstring'''






					for i in range(9	):
										if grid[row][i] == n or grid[i][column] == n:
															return False

					for i in range(3	):
										for j in range(3	):
															if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
																				return False

					return True



def   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					for i in range(9	):
										for j in range(9	):
															if grid[i][j] == 0:
																				return i, j
					return None



def   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					if location := find_empty_location(_lowercase	):
										UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				location
					else:
										# If the location is ``None``, then the grid is solved.
										return grid

					for digit in range(1							,      10	):
										if is_safe(_lowercase							,      _lowercase							,      _lowercase							,      _lowercase	):
															UpperCAmelCase			:   int      =				digit

															if sudoku(_lowercase	) is not None:
																				return grid

															UpperCAmelCase			:   Optional[Any]      =				0

					return None



def   lowercase_	(    _lowercase       :		Matrix	):




					'''simple docstring'''






					for row in grid:
										for cell in row:
															print(_lowercase							,      end=" "	)
										print()


if __name__ == "__main__":
	# make a copy of grid so that you can compare with the unmodified grid
	for example_grid in (initial_grid, no_solution):
		print("""\nExample grid:\n""" + """=""" * 2_0)
		print_solution(example_grid)
		print("""\nExample grid solution:""")
		snake_case_   :   Dict        =       sudoku(example_grid)
		if solution is not None:
			print_solution(solution)
		else:
			print("""Cannot find a solution.""")


