



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()
A__								=						logging.get_logger(__name__)

A__								=						torch.device('''cpu''')



def 			_lowerCAmelCase       (					)   ->					List[str]:


				"""simple docstring"""

				snake_case__	:      List[Any]     		=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				snake_case__	:      str     		=	Image.open(requests.get(lowerCamelCase_  ,  stream=lowerCamelCase_      ).raw      )
				return im



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]      )

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]      )

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]      )

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]      )



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Any:


				"""simple docstring"""

				snake_case__	:      Any     		=	dct.pop(lowerCamelCase_      )
				snake_case__	:      List[Any]     		=	val



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Any:


				"""simple docstring"""

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



@torch.no_grad()
def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      Optional[Any]     		=	SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				snake_case__	:      Optional[int]     		=	1000
				snake_case__	:      Optional[int]     		=	'''huggingface/label-files'''
				snake_case__	:      int     		=	'''imagenet-1k-id2label.json'''
				snake_case__	:      List[Any]     		=	json.load(open(hf_hub_download(lowerCamelCase_  ,  lowerCamelCase_  ,  repo_type='''dataset'''      )  ,  '''r'''      )      )
				snake_case__	:      Any     		=	{int(lowerCamelCase_      ): v for k, v in idalabel.items()}
				snake_case__	:      Optional[int]     		=	idalabel
				snake_case__	:      Union[str, Any]     		=	{v: k for k, v in idalabel.items()}

				# size of the architecture
				if swiftformer_name == "swiftformer_xs":
								snake_case__	:      Union[str, Any]     		=	[3, 3, 6, 4]
								snake_case__	:      List[str]     		=	[48, 56, 112, 220]

				elif swiftformer_name == "swiftformer_s":
								snake_case__	:      Optional[Any]     		=	[3, 3, 9, 6]
								snake_case__	:      Dict     		=	[48, 64, 168, 224]

				elif swiftformer_name == "swiftformer_l1":
								snake_case__	:      List[str]     		=	[4, 3, 10, 5]
								snake_case__	:      Any     		=	[48, 96, 192, 384]

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

				# load state_dict of original model, remove and rename some keys
				if original_ckpt:
								if original_ckpt.startswith('''https'''      ):
												snake_case__	:      int     		=	torch.hub.load_state_dict_from_url(lowerCamelCase_  ,  map_location='''cpu'''  ,  check_hash=lowerCamelCase_      )
								else:
												snake_case__	:      Tuple     		=	torch.load(lowerCamelCase_  ,  map_location='''cpu'''      )
				snake_case__	:      Optional[int]     		=	checkpoint

				snake_case__	:      Any     		=	create_rename_keys(lowerCamelCase_      )
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(lowerCamelCase_  ,  lowerCamelCase_  ,  lowerCamelCase_      )

				# load HuggingFace model
				snake_case__	:      Dict     		=	SwiftFormerForImageClassification(lowerCamelCase_      ).eval()
				hf_model.load_state_dict(lowerCamelCase_      )

				# prepare test inputs
				snake_case__	:      List[str]     		=	prepare_img()
				snake_case__	:      List[str]     		=	ViTImageProcessor.from_pretrained('''preprocessor_config'''      )
				snake_case__	:      Tuple     		=	processor(images=lowerCamelCase_  ,  return_tensors='''pt'''      )

				# compare outputs from both models
				snake_case__	:      Union[str, Any]     		=	get_expected_output(lowerCamelCase_      )
				snake_case__	:      str     		=	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__":
				A__								=						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.''')

				A__								=						parser.parse_args()
				convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









from math import asin, atan, cos, radians, sin, sqrt, tan

A__	:			Optional[int]			   = 637_8137.0
A__	:			List[str]			   = 635_6752.31_4245
A__	:			Union[str, Any]			   = 6_37_81_37






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			(AXIS_A - AXIS_B) / AXIS_A
				lowercase__    =			atan((1 - flattening) * tan(radians(lowerCamelCase_				)				)				)
				lowercase__    =			atan((1 - flattening) * tan(radians(lowerCamelCase_				)				)				)
				lowercase__    =			radians(lowerCamelCase_				)
				lowercase__    =			radians(lowerCamelCase_				)
				# Equation
				lowercase__    =			sin((phi_a - phi_a) / 2				)
				lowercase__    =			sin((lambda_a - lambda_a) / 2				)
				# Square both values
				sin_sq_phi *= sin_sq_phi
				sin_sq_lambda *= sin_sq_lambda
				lowercase__    =			sqrt(sin_sq_phi + (cos(lowerCamelCase_				) * cos(lowerCamelCase_				) * sin_sq_lambda)				)
				return 2 * RADIUS * asin(lowerCamelCase_				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




