



"""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 	UpperCAmelCase     (		A	:						str       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		SwinConfig()
		_UpperCAmelCase							 =		swin_name.split('_'       )

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

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

		if "in22k" in swin_name:
				_UpperCAmelCase							 =		2_1841
		else:
				_UpperCAmelCase							 =		1000
				_UpperCAmelCase							 =		'huggingface/label-files'
				_UpperCAmelCase							 =		'imagenet-1k-id2label.json'
				_UpperCAmelCase							 =		json.load(open(hf_hub_download(A     ,				A     ,				repo_type='dataset'       )     ,				'r'       )       )
				_UpperCAmelCase							 =		{int(A       ): v for k, v in idalabel.items()}
				_UpperCAmelCase							 =		idalabel
				_UpperCAmelCase							 =		{v: k for k, v in idalabel.items()}

		_UpperCAmelCase							 =		img_size
		_UpperCAmelCase							 =		num_classes
		_UpperCAmelCase							 =		embed_dim
		_UpperCAmelCase							 =		depths
		_UpperCAmelCase							 =		num_heads
		_UpperCAmelCase							 =		window_size

		return config




def 	UpperCAmelCase     (		A	:						List[str]       ):

		'''simple docstring'''


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

		if name == "norm.weight":
				_UpperCAmelCase							 =		'layernorm.weight'
		if name == "norm.bias":
				_UpperCAmelCase							 =		'layernorm.bias'

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

		return name




def 	UpperCAmelCase     (		A	:						Dict     ,				A	:						Tuple       ):

		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				_UpperCAmelCase							 =		orig_state_dict.pop(A       )

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

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

		return orig_state_dict




def 	UpperCAmelCase     (		A	:						Optional[Any]     ,				A	:						List[str]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		timm.create_model(A     ,				pretrained=A       )
		timm_model.eval()

		_UpperCAmelCase							 =		get_swin_config(A       )
		_UpperCAmelCase							 =		SwinForImageClassification(A       )
		model.eval()

		_UpperCAmelCase							 =		convert_state_dict(timm_model.state_dict()     ,				A       )
		model.load_state_dict(A       )

		_UpperCAmelCase							 =		'http://images.cocodataset.org/val2017/000000039769.jpg'

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

		_UpperCAmelCase							 =		timm_model(inputs['pixel_values']       )
		_UpperCAmelCase							 =		model(**A       ).logits

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

		print(f'Saving model {swin_name} to {pytorch_dump_folder_path}'       )
		model.save_pretrained(A       )

		print(f'Saving image processor to {pytorch_dump_folder_path}'       )
		image_processor.save_pretrained(A       )


if __name__ == "__main__":
					lowercase  							=					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.'''
					)

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







"""simple docstring"""





import os

lowercase  							=					{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}




def 	UpperCAmelCase     (		A	:						str       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		0

		_UpperCAmelCase							 =		0
		while index < len(A       ) - 1:
				_UpperCAmelCase							 =		SYMBOLS[numerals[index]]
				_UpperCAmelCase							 =		SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value




def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		''

		_UpperCAmelCase							 =		num // 1000
		numerals += m_count * "M"
		num %= 1000

		_UpperCAmelCase							 =		num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		_UpperCAmelCase							 =		num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals




def 	UpperCAmelCase     (		A	:						str = "/p089_roman.txt"       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		0

		with open(os.path.dirname(A       ) + roman_numerals_filename       ) as filea:
				_UpperCAmelCase							 =		filea.readlines()

		for line in lines:
				_UpperCAmelCase							 =		line.strip()
				_UpperCAmelCase							 =		parse_roman_numerals(A       )
				_UpperCAmelCase							 =		generate_roman_numerals(A       )
				savings += len(A       ) - len(A       )

		return savings


if __name__ == "__main__":
					print(F'''{solution() = }''')


