"""simple docstring"""




import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase    :							List[Any]													=						logging.get_logger(__name__)

__lowerCAmelCase    :							Optional[Any]													=						torch.device("cpu")







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowerCAmelCase__        =   Image.open(requests.get(lowerCamelCase__			,  stream=lowerCamelCase__     ).raw     )
		return im







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		if swiftformer_name == "swiftformer_xs":
				return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]     )

		elif swiftformer_name == "swiftformer_s":
				return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]     )

		elif swiftformer_name == "swiftformer_l1":
				return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]     )

		elif swiftformer_name == "swiftformer_l3":
				return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   dct.pop(lowerCamelCase__     )
		lowerCAmelCase__        =   val







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   []
		for k in state_dict.keys():
				lowerCAmelCase__        =   k
				if ".pwconv" in k:
						lowerCAmelCase__        =   k_new.replace(""".pwconv"""			,  """.point_wise_conv"""     )
				if ".dwconv" in k:
						lowerCAmelCase__        =   k_new.replace(""".dwconv"""			,  """.depth_wise_conv"""     )
				if ".Proj." in k:
						lowerCAmelCase__        =   k_new.replace(""".Proj."""			,  """.proj."""     )
				if "patch_embed" in k_new:
						lowerCAmelCase__        =   k_new.replace("""patch_embed"""			,  """swiftformer.patch_embed.patch_embedding"""     )
				if "network" in k_new:
						lowerCAmelCase__        =   k_new.split("""."""     )
						if ls[2].isdigit():
								lowerCAmelCase__        =   """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]     )
						else:
								lowerCAmelCase__        =   k_new.replace("""network"""			,  """swiftformer.encoder.network"""     )
				rename_keys.append((k, k_new)     )
		return rename_keys







@torch.no_grad()
def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   SwiftFormerConfig()

		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		lowerCAmelCase__        =   1000
		lowerCAmelCase__        =   """huggingface/label-files"""
		lowerCAmelCase__        =   """imagenet-1k-id2label.json"""
		lowerCAmelCase__        =   json.load(open(hf_hub_download(lowerCamelCase__			,  lowerCamelCase__			,  repo_type="""dataset"""     )			,  """r"""     )     )
		lowerCAmelCase__        =   {int(lowerCamelCase__     ): v for k, v in idalabel.items()}
		lowerCAmelCase__        =   idalabel
		lowerCAmelCase__        =   {v: k for k, v in idalabel.items()}

		# size of the architecture
		if swiftformer_name == "swiftformer_xs":
				lowerCAmelCase__        =   [3, 3, 6, 4]
				lowerCAmelCase__        =   [48, 56, 112, 220]

		elif swiftformer_name == "swiftformer_s":
				lowerCAmelCase__        =   [3, 3, 9, 6]
				lowerCAmelCase__        =   [48, 64, 168, 224]

		elif swiftformer_name == "swiftformer_l1":
				lowerCAmelCase__        =   [4, 3, 10, 5]
				lowerCAmelCase__        =   [48, 96, 192, 384]

		elif swiftformer_name == "swiftformer_l3":
				lowerCAmelCase__        =   [4, 4, 12, 6]
				lowerCAmelCase__        =   [64, 128, 320, 512]

		# load state_dict of original model, remove and rename some keys
		if original_ckpt:
				if original_ckpt.startswith("""https"""     ):
						lowerCAmelCase__        =   torch.hub.load_state_dict_from_url(lowerCamelCase__			,  map_location="""cpu"""			,  check_hash=lowerCamelCase__     )
				else:
						lowerCAmelCase__        =   torch.load(lowerCamelCase__			,  map_location="""cpu"""     )
		lowerCAmelCase__        =   checkpoint

		lowerCAmelCase__        =   create_rename_keys(lowerCamelCase__     )
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

		# load HuggingFace model
		lowerCAmelCase__        =   SwiftFormerForImageClassification(lowerCamelCase__     ).eval()
		hf_model.load_state_dict(lowerCamelCase__     )

		# prepare test inputs
		lowerCAmelCase__        =   prepare_img()
		lowerCAmelCase__        =   ViTImageProcessor.from_pretrained("""preprocessor_config"""     )
		lowerCAmelCase__        =   processor(images=lowerCamelCase__			,  return_tensors="""pt"""     )

		# compare outputs from both models
		lowerCAmelCase__        =   get_expected_output(lowerCamelCase__     )
		lowerCAmelCase__        =   hf_model(inputs["""pixel_values"""]     ).logits

		assert hf_logits.shape == torch.Size([1, 1000]     )
		assert torch.allclose(hf_logits[0, 0:5]			,  lowerCamelCase__			,  atol=1e-3     )

		Path(lowerCamelCase__     ).mkdir(exist_ok=lowerCamelCase__     )
		print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""     )
		hf_model.save_pretrained(lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							Any													=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--swiftformer_name",
			    default="swiftformer_xs",
			    choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
			    type=str,
			    help="Name of the SwiftFormer model you'd like to convert.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default="./converted_outputs/",
			    type=str,
			    help="Path to the output PyTorch model directory.",
			)
			parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

			__lowerCAmelCase    :							Tuple													=						parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)





"""simple docstring"""




from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

__lowerCAmelCase    :							Any													=						{
    # 1536-bit
    5: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 2048-bit
    14: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 3072-bit
    15: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 4096-bit
    16: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
            + "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
            + "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
            + "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
            + "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
            + "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
            + "FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 6144-bit
    17: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
            + "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
            + "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
            + "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
            + "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
            + "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
            + "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
            + "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
            + "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
            + "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
            + "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
            + "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
            + "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
            + "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
            + "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
            + "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
            + "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
            + "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
            + "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
            + "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
            + "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
            + "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
            + "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
            + "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
            + "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
            + "6DCC4024FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 8192-bit
    18: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
            + "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
            + "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
            + "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
            + "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
            + "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
            + "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
            + "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
            + "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
            + "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
            + "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
            + "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
            + "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
            + "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
            + "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
            + "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
            + "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
            + "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
            + "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
            + "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
            + "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
            + "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
            + "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
            + "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
            + "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
            + "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
            + "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
            + "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
}

class       a_      :




					def __init__(	self						:     List[str]  ,						snake_case__						:     int = 14     ):
							if group not in primes:
									raise ValueError("""Unsupported Group"""     )
							lowerCAmelCase__        =   primes[group]["""prime"""]
							lowerCAmelCase__        =   primes[group]["""generator"""]

							lowerCAmelCase__        =   int(hexlify(urandom(32     )     )  ,						base=16     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							return hex(self.__private_key     )[2:]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   pow(self.generator  ,						self.__private_key  ,						self.prime     )
							return hex(snake_case__     )[2:]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     int     ):
							# check if the other public key is valid based on NIST SP800-56
							return (
							    2 <= key <= self.prime - 2
							    and pow(snake_case__  ,						(self.prime - 1) // 2  ,						self.prime     ) == 1
							)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     str     ):
							lowerCAmelCase__        =   int(snake_case__  ,						base=16     )
							if not self.is_valid_public_key(snake_case__     ):
									raise ValueError("""Invalid public key"""     )
							lowerCAmelCase__        =   pow(snake_case__  ,						self.__private_key  ,						self.prime     )
							return shaaaa(str(snake_case__     ).encode()     ).hexdigest()




					@staticmethod
					def 						_SCREAMING_SNAKE_CASE						(	snake_case__						:     int  ,						snake_case__						:     int     ):
							# check if the other public key is valid based on NIST SP800-56
							return (
							    2 <= remote_public_key_str <= prime - 2
							    and pow(snake_case__  ,						(prime - 1) // 2  ,						snake_case__     ) == 1
							)




					@staticmethod
					def 						_SCREAMING_SNAKE_CASE						(	snake_case__						:     str  ,						snake_case__						:     str  ,						snake_case__						:     int = 14     ):
							lowerCAmelCase__        =   int(snake_case__  ,						base=16     )
							lowerCAmelCase__        =   int(snake_case__  ,						base=16     )
							lowerCAmelCase__        =   primes[group]["""prime"""]
							if not DiffieHellman.is_valid_public_key_static(snake_case__  ,						snake_case__     ):
									raise ValueError("""Invalid public key"""     )
							lowerCAmelCase__        =   pow(snake_case__  ,						snake_case__  ,						snake_case__     )
							return shaaaa(str(snake_case__     ).encode()     ).hexdigest()


if __name__ == "__main__":
			import doctest

			doctest.testmod()




