




"""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       __A (     a_  :Tuple)						->						Union[str, Any]:
				__a	:  Tuple         =					SwinConfig()
				__a	:  Tuple         =					swin_name.split('''_''')

				__a	:  int         =					name_split[1]
				__a	:  Optional[Any]         =					int(name_split[4])
				__a	:  str         =					int(name_split[3][-1])

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

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

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

				return config

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

				if name == "norm.weight":
								__a	:  int         =					'''layernorm.weight'''
				if name == "norm.bias":
								__a	:  Any         =					'''layernorm.bias'''

				if "head" in name:
								__a	:  Union[str, Any]         =					name.replace('''head'''				,    '''classifier''')
				else:
								__a	:  Union[str, Any]         =					'''swin.''' + name

				return name

def       __A (     a_  :List[Any]				,    a_  :Optional[Any])						->						int:
				for key in orig_state_dict.copy().keys():
								__a	:  Union[str, Any]         =					orig_state_dict.pop(a_)

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

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

				return orig_state_dict

def       __A (     a_  :List[Any]				,    a_  :Optional[int])						->						List[str]:
				__a	:  Optional[Any]         =					timm.create_model(a_				,    pretrained=a_)
				timm_model.eval()

				__a	:  List[str]         =					get_swin_config(a_)
				__a	:  Union[str, Any]         =					SwinForImageClassification(a_)
				model.eval()

				__a	:  List[str]         =					convert_state_dict(timm_model.state_dict()				,    a_)
				model.load_state_dict(a_)

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

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

				__a	:  List[Any]         =					timm_model(inputs['''pixel_values'''])
				__a	:  Optional[Any]         =					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__":
		A	     =					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.'''
		)

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






"""simple docstring"""






import os
import string
import sys


A	     =					1 << 8

A	     =					{
    '''tab''': ord('''\t'''),
    '''newline''': ord('''\r'''),
    '''esc''': 27,
    '''up''': 65 + ARROW_KEY_FLAG,
    '''down''': 66 + ARROW_KEY_FLAG,
    '''right''': 67 + ARROW_KEY_FLAG,
    '''left''': 68 + ARROW_KEY_FLAG,
    '''mod_int''': 91,
    '''undefined''': sys.maxsize,
    '''interrupt''': 3,
    '''insert''': 50,
    '''delete''': 51,
    '''pg_up''': 53,
    '''pg_down''': 54,
}

A	     =					KEYMAP['''up''']
A	     =					KEYMAP['''left''']

if sys.platform == "win32":
		A	     =					[]
		A	     =					{
		    B'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
		    B'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
		    B'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
		    B'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
		    B'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
		    B'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
		    B'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
		    B'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
		}

for i in range(10):
		A	     =					ord(str(i))

def       __A (     )						->						Dict:
				if os.name == "nt":
								import msvcrt

								__a	:  Optional[Any]         =					'''mbcs'''
								# Flush the keyboard buffer
								while msvcrt.kbhit():
												msvcrt.getch()
								if len(a_) == 0:
												# Read the keystroke
												__a	:  Optional[Any]         =					msvcrt.getch()

												# If it is a prefix char, get second part
												if ch in (b"\x00", b"\xe0"):
																__a	:  Optional[Any]         =					ch + msvcrt.getch()
																# Translate actual Win chars to bullet char types
																try:
																				__a	:  Union[str, Any]         =					chr(WIN_KEYMAP[cha])
																				WIN_CH_BUFFER.append(chr(KEYMAP['''mod_int''']))
																				WIN_CH_BUFFER.append(a_)
																				if ord(a_) in (
																				    KEYMAP["insert"] - 1 << 9,
																				    KEYMAP["delete"] - 1 << 9,
																				    KEYMAP["pg_up"] - 1 << 9,
																				    KEYMAP["pg_down"] - 1 << 9,
																				):
																								WIN_CH_BUFFER.append(chr(1_26))
																				__a	:  str         =					chr(KEYMAP['''esc'''])
																except KeyError:
																				__a	:  str         =					cha[1]
												else:
																__a	:  Optional[Any]         =					ch.decode(a_)
								else:
												__a	:  Union[str, Any]         =					WIN_CH_BUFFER.pop(0)
				elif os.name == "posix":
								import termios
								import tty

								__a	:  Any         =					sys.stdin.fileno()
								__a	:  List[str]         =					termios.tcgetattr(a_)
								try:
												tty.setraw(a_)
												__a	:  int         =					sys.stdin.read(1)
								finally:
												termios.tcsetattr(a_				,    termios.TCSADRAIN				,    a_)
				return ch






def       __A (     )						->						str:
				__a	:  Any         =					get_raw_chars()
				if ord(a_) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
								return char

				elif ord(a_) == KEYMAP["esc"]:
								__a	:  str         =					get_raw_chars()
								if ord(a_) == KEYMAP["mod_int"]:
												__a	:  List[str]         =					get_raw_chars()
												if ord(a_) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(a_) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
																return chr(ord(a_) + ARROW_KEY_FLAG)
												else:
																return KEYMAP["undefined"]
								else:
												return get_raw_chars()

				else:
								if char in string.printable:
												return char
								else:
												return KEYMAP["undefined"]