



from collections.abc import Generator
from math import sin






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if len(_lowerCamelCase  ) != 32:
														raise ValueError("Input must be of length 32"  )

							_lowerCAmelCase     :							Optional[Any]    =    b""
							for i in [3, 2, 1, 0]:
														little_endian += string_aa[8 * i : 8 * i + 8]
							return little_endian






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if i < 0:
														raise ValueError("Input must be non-negative"  )

							_lowerCAmelCase     :							Dict    =    format(_lowerCamelCase				, "08x"  )[-8:]
							_lowerCAmelCase     :							int    =    b""
							for i in [3, 2, 1, 0]:
														little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8"  )
							return little_endian_hex






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    b""
							for char in message:
														bit_string += format(_lowerCamelCase				, "08b"  ).encode("utf-8"  )
							_lowerCAmelCase     :							Tuple    =    format(len(_lowerCamelCase  )				, "064b"  ).encode("utf-8"  )

							# Pad bit_string to a multiple of 512 chars
							bit_string += b"1"
							while len(_lowerCamelCase  ) % 512 != 448:
														bit_string += b"0"
							bit_string += to_little_endian(start_len[32:]  ) + to_little_endian(start_len[:32]  )

							return bit_string






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if len(_lowerCamelCase  ) % 512 != 0:
														raise ValueError("Input must have length that's a multiple of 512"  )

							for pos in range(0				, len(_lowerCamelCase  )				, 512  ):
														_lowerCAmelCase     :							List[str]    =    bit_string[pos : pos + 512]
														_lowerCAmelCase     :							Optional[Any]    =    []
														for i in range(0				, 512				, 32  ):
																					block_words.append(int(to_little_endian(block[i : i + 32]  )				, 2  )  )
														yield block_words






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if i < 0:
														raise ValueError("Input must be non-negative"  )

							_lowerCAmelCase     :							str    =    format(_lowerCamelCase				, "032b"  )
							_lowerCAmelCase     :							Optional[Any]    =    ""
							for c in i_str:
														new_str += "1" if c == "0" else "0"
							return int(_lowerCamelCase				, 2  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							return (a + b) % 2**32






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if i < 0:
														raise ValueError("Input must be non-negative"  )
							if shift < 0:
														raise ValueError("Shift must be non-negative"  )
							return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    preprocess(_lowerCamelCase  )

							_lowerCAmelCase     :							Tuple    =    [int(2**32 * abs(sin(i + 1  )  )  ) for i in range(64  )]

							# Starting states
							_lowerCAmelCase     :							int    =    0X67_45_23_01
							_lowerCAmelCase     :							Optional[Any]    =    0XEF_CD_AB_89
							_lowerCAmelCase     :							Union[str, Any]    =    0X98_BA_DC_FE
							_lowerCAmelCase     :							Dict    =    0X10_32_54_76

							_lowerCAmelCase     :							int    =    [
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    7,
							    12,
							    17,
							    22,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    5,
							    9,
							    14,
							    20,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    4,
							    11,
							    16,
							    23,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							    6,
							    10,
							    15,
							    21,
							]

							# Process bit string in chunks, each with 16 32-char words
							for block_words in get_block_words(_lowerCamelCase  ):
														_lowerCAmelCase     :							Dict    =    aa
														_lowerCAmelCase     :							Union[str, Any]    =    ba
														_lowerCAmelCase     :							Dict    =    ca
														_lowerCAmelCase     :							Tuple    =    da

														# Hash current chunk
														for i in range(64  ):
																					if i <= 15:
																												# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
																												_lowerCAmelCase     :							List[Any]    =    d ^ (b & (c ^ d))
																												_lowerCAmelCase     :							int    =    i
																					elif i <= 31:
																												# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
																												_lowerCAmelCase     :							List[str]    =    c ^ (d & (b ^ c))
																												_lowerCAmelCase     :							Dict    =    (5 * i + 1) % 16
																					elif i <= 47:
																												_lowerCAmelCase     :							Union[str, Any]    =    b ^ c ^ d
																												_lowerCAmelCase     :							Optional[int]    =    (3 * i + 5) % 16
																					else:
																												_lowerCAmelCase     :							List[str]    =    c ^ (b | not_aa(_lowerCamelCase  ))
																												_lowerCAmelCase     :							int    =    (7 * i) % 16
																					_lowerCAmelCase     :							int    =    (f + a + added_consts[i] + block_words[g]) % 2**32
																					_lowerCAmelCase     :							Optional[int]    =    d
																					_lowerCAmelCase     :							Tuple    =    c
																					_lowerCAmelCase     :							int    =    b
																					_lowerCAmelCase     :							Tuple    =    sum_aa(_lowerCamelCase				, left_rotate_aa(_lowerCamelCase				, shift_amounts[i]  )  )

														# Add hashed chunk to running total
														_lowerCAmelCase     :							List[str]    =    sum_aa(_lowerCamelCase				, _lowerCamelCase  )
														_lowerCAmelCase     :							Any    =    sum_aa(_lowerCamelCase				, _lowerCamelCase  )
														_lowerCAmelCase     :							Any    =    sum_aa(_lowerCamelCase				, _lowerCamelCase  )
														_lowerCAmelCase     :							str    =    sum_aa(_lowerCamelCase				, _lowerCamelCase  )

							_lowerCAmelCase     :							Union[str, Any]    =    reformat_hex(_lowerCamelCase  ) + reformat_hex(_lowerCamelCase  ) + reformat_hex(_lowerCamelCase  ) + reformat_hex(_lowerCamelCase  )
							return digest


if __name__ == "__main__":
						import doctest

						doctest.testmod()




'''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()
__lowercase	:						Tuple       =	logging.get_logger(__name__)

__lowercase	:						List[Any]       =	torch.device('cpu')






def   lowerCamelCase							():
					__a			:       int					    =  'http://images.cocodataset.org/val2017/000000039769.jpg'
					__a			:       Tuple					    =  Image.open(requests.get(_SCREAMING_SNAKE_CASE						,      stream=_SCREAMING_SNAKE_CASE   ).raw   )
					return im






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					if swiftformer_name == "swiftformer_xs":
										return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]   )

					elif swiftformer_name == "swiftformer_s":
										return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]   )

					elif swiftformer_name == "swiftformer_l1":
										return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]   )

					elif swiftformer_name == "swiftformer_l3":
										return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       int					    =  dct.pop(_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  val






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					__a			:       Dict					    =  []
					for k in state_dict.keys():
										__a			:       List[Any]					    =  k
										if ".pwconv" in k:
															__a			:       List[Any]					    =  k_new.replace('.pwconv'						,      '.point_wise_conv'   )
										if ".dwconv" in k:
															__a			:       Dict					    =  k_new.replace('.dwconv'						,      '.depth_wise_conv'   )
										if ".Proj." in k:
															__a			:       Optional[int]					    =  k_new.replace('.Proj.'						,      '.proj.'   )
										if "patch_embed" in k_new:
															__a			:       List[Any]					    =  k_new.replace('patch_embed'						,      'swiftformer.patch_embed.patch_embedding'   )
										if "network" in k_new:
															__a			:       Union[str, Any]					    =  k_new.split('.'   )
															if ls[2].isdigit():
																				__a			:       Union[str, Any]					    =  'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]   )
															else:
																				__a			:       Union[str, Any]					    =  k_new.replace('network'						,      'swiftformer.encoder.network'   )
										rename_keys.append((k, k_new)   )
					return rename_keys






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       Union[str, Any]					    =  SwiftFormerConfig()

					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					__a			:       List[str]					    =  1_000
					__a			:       Tuple					    =  'huggingface/label-files'
					__a			:       str					    =  'imagenet-1k-id2label.json'
					__a			:       Dict					    =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      repo_type='dataset'   )						,      'r'   )   )
					__a			:       Optional[int]					    =  {int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
					__a			:       Any					    =  idalabel
					__a			:       str					    =  {v: k for k, v in idalabel.items()}

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

					elif swiftformer_name == "swiftformer_s":
										__a			:       Dict					    =  [3, 3, 9, 6]
										__a			:       List[str]					    =  [48, 64, 168, 224]

					elif swiftformer_name == "swiftformer_l1":
										__a			:       Dict					    =  [4, 3, 10, 5]
										__a			:       Optional[int]					    =  [48, 96, 192, 384]

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

					# load state_dict of original model, remove and rename some keys
					if original_ckpt:
										if original_ckpt.startswith('https'   ):
															__a			:       List[Any]					    =  torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE						,      map_location='cpu'						,      check_hash=_SCREAMING_SNAKE_CASE   )
										else:
															__a			:       Union[str, Any]					    =  torch.load(_SCREAMING_SNAKE_CASE						,      map_location='cpu'   )
					__a			:       Optional[Any]					    =  checkpoint

					__a			:       Dict					    =  create_rename_keys(_SCREAMING_SNAKE_CASE   )
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					# load HuggingFace model
					__a			:       Tuple					    =  SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE   ).eval()
					hf_model.load_state_dict(_SCREAMING_SNAKE_CASE   )

					# prepare test inputs
					__a			:       Tuple					    =  prepare_img()
					__a			:       str					    =  ViTImageProcessor.from_pretrained('preprocessor_config'   )
					__a			:       Tuple					    =  processor(images=_SCREAMING_SNAKE_CASE						,      return_tensors='pt'   )

					# compare outputs from both models
					__a			:       List[Any]					    =  get_expected_output(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  hf_model(inputs['pixel_values']   ).logits

					assert hf_logits.shape == torch.Size([1, 1_000]   )
					assert torch.allclose(hf_logits[0, 0:5]						,      _SCREAMING_SNAKE_CASE						,      atol=1e-3   )

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


if __name__ == "__main__":
	__lowercase	:						Optional[int]       =	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.')

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



