

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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str      )			->      Tuple:




				'''simple docstring'''





				A__        =			SwinConfig()
				A__        =			swin_name.split("_"      )

				A__        =			name_split[1]
				A__        =			int(name_split[4]      )
				A__        =			int(name_split[3][-1]      )

				if model_size == "tiny":
								A__        =			9_6
								A__        =			(2, 2, 6, 2)
								A__        =			(3, 6, 1_2, 2_4)
				elif model_size == "small":
								A__        =			9_6
								A__        =			(2, 2, 1_8, 2)
								A__        =			(3, 6, 1_2, 2_4)
				elif model_size == "base":
								A__        =			1_2_8
								A__        =			(2, 2, 1_8, 2)
								A__        =			(4, 8, 1_6, 3_2)
				else:
								A__        =			1_9_2
								A__        =			(2, 2, 1_8, 2)
								A__        =			(6, 1_2, 2_4, 4_8)

				if "in22k" in swin_name:
								A__        =			2_1_8_4_1
				else:
								A__        =			1_0_0_0
								A__        =			"huggingface/label-files"
								A__        =			"imagenet-1k-id2label.json"
								A__        =			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       repo_type="dataset"      )	,       "r"      )      )
								A__        =			{int(SCREAMING_SNAKE_CASE_      ): v for k, v in idalabel.items()}
								A__        =			idalabel
								A__        =			{v: k for k, v in idalabel.items()}

				A__        =			img_size
				A__        =			num_classes
				A__        =			embed_dim
				A__        =			depths
				A__        =			num_heads
				A__        =			window_size

				return config



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]      )			->      Optional[Any]:




				'''simple docstring'''





				if "patch_embed.proj" in name:
								A__        =			name.replace("patch_embed.proj"	,       "embeddings.patch_embeddings.projection"      )
				if "patch_embed.norm" in name:
								A__        =			name.replace("patch_embed.norm"	,       "embeddings.norm"      )
				if "layers" in name:
								A__        =			"encoder." + name
				if "attn.proj" in name:
								A__        =			name.replace("attn.proj"	,       "attention.output.dense"      )
				if "attn" in name:
								A__        =			name.replace("attn"	,       "attention.self"      )
				if "norm1" in name:
								A__        =			name.replace("norm1"	,       "layernorm_before"      )
				if "norm2" in name:
								A__        =			name.replace("norm2"	,       "layernorm_after"      )
				if "mlp.fc1" in name:
								A__        =			name.replace("mlp.fc1"	,       "intermediate.dense"      )
				if "mlp.fc2" in name:
								A__        =			name.replace("mlp.fc2"	,       "output.dense"      )

				if name == "norm.weight":
								A__        =			"layernorm.weight"
				if name == "norm.bias":
								A__        =			"layernorm.bias"

				if "head" in name:
								A__        =			name.replace("head"	,       "classifier"      )
				else:
								A__        =			"swin." + name

				return name



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							Tuple      )			->      List[str]:




				'''simple docstring'''





				for key in orig_state_dict.copy().keys():
								A__        =			orig_state_dict.pop(SCREAMING_SNAKE_CASE_      )

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

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

				return orig_state_dict



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[Any]	,       SCREAMING_SNAKE_CASE_:							List[str]      )			->      Optional[int]:




				'''simple docstring'''





				A__        =			timm.create_model(SCREAMING_SNAKE_CASE_	,       pretrained=SCREAMING_SNAKE_CASE_      )
				timm_model.eval()

				A__        =			get_swin_config(SCREAMING_SNAKE_CASE_      )
				A__        =			SwinForImageClassification(SCREAMING_SNAKE_CASE_      )
				model.eval()

				A__        =			convert_state_dict(timm_model.state_dict()	,       SCREAMING_SNAKE_CASE_      )
				model.load_state_dict(SCREAMING_SNAKE_CASE_      )

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

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

				A__        =			timm_model(inputs["pixel_values"]      )
				A__        =			model(**SCREAMING_SNAKE_CASE_      ).logits

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

				print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'      )
				model.save_pretrained(SCREAMING_SNAKE_CASE_      )

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


if __name__ == "__main__":
	lowerCAmelCase__	   = 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."""
	)

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




lowerCAmelCase__	   = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							bytes      )			->      bytes:




				'''simple docstring'''





				if not isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
								A__        =			F'a bytes-like object is required, not \'{data.__class__.__name__}\''
								raise TypeError(SCREAMING_SNAKE_CASE_      )

				A__        =			"".join(bin(SCREAMING_SNAKE_CASE_      )[2:].zfill(8      ) for byte in data      )

				A__        =			len(SCREAMING_SNAKE_CASE_      ) % 6 != 0

				if padding_needed:
								# The padding that will be added later
								A__        =			b"=" * ((6 - len(SCREAMING_SNAKE_CASE_      ) % 6) // 2)

								# Append binary_stream with arbitrary binary digits (0's by default) to make its
								# length a multiple of 6.
								binary_stream += "0" * (6 - len(SCREAMING_SNAKE_CASE_      ) % 6)
				else:
								A__        =			b""

				# Encode every 6 binary digits to their corresponding Base64 character
				return (
				    "".join(
				        B64_CHARSET[int(binary_stream[index : index + 6]	,       2      )]
				        for index in range(0	,       len(SCREAMING_SNAKE_CASE_      )	,       6      )      ).encode()
				    + padding
				)



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str      )			->      bytes:




				'''simple docstring'''





				if not isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ) and not isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
								A__        =			(
								    "argument should be a bytes-like object or ASCII string, "
								    F'not \'{encoded_data.__class__.__name__}\''
								)
								raise TypeError(SCREAMING_SNAKE_CASE_      )

				# In case encoded_data is a bytes-like object, make sure it contains only
				# ASCII characters so we convert it to a string object
				if isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      ):
								try:
												A__        =			encoded_data.decode("utf-8"      )
								except UnicodeDecodeError:
												raise ValueError("base64 encoded data should only contain ASCII characters"      )

				A__        =			encoded_data.count("="      )

				# Check if the encoded string contains non base64 characters
				if padding:
								assert all(
								    char in B64_CHARSET for char in encoded_data[:-padding]      ), "Invalid base64 character(s) found."
				else:
								assert all(
								    char in B64_CHARSET for char in encoded_data      ), "Invalid base64 character(s) found."

				# Check the padding
				assert len(SCREAMING_SNAKE_CASE_      ) % 4 == 0 and padding < 3, "Incorrect padding"

				if padding:
								# Remove padding if there is one
								A__        =			encoded_data[:-padding]

								A__        =			"".join(
								    bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE_      )      )[2:].zfill(6      ) for char in encoded_data      )[: -padding * 2]
				else:
								A__        =			"".join(
								    bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE_      )      )[2:].zfill(6      ) for char in encoded_data      )

				A__        =			[
				    int(binary_stream[index : index + 8]	,       2      )
				    for index in range(0	,       len(SCREAMING_SNAKE_CASE_      )	,       8      )
				]

				return bytes(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

