



'''simple docstring'''




from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
a   :			str					  =    Lock()



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			str:
							global process_lock

							# we perform n swaps since after n swaps we know we are sorted
							# we *could* stop early if we are sorted already, but it takes as long to
							# find out we are sorted as it does to sort the list with this algorithm
							for i in range(0	,				1_0		):
														if (i + position) % 2 == 0 and r_send is not None:
																					# send your value to your right neighbor
																					process_lock.acquire()
																					r_send[1].send(_lowercase		)
																					process_lock.release()

																					# receive your right neighbor's value
																					process_lock.acquire()
																					UpperCAmelCase     :		Dict							     =		rr_cv[0].recv()
																					process_lock.release()

																					# take the lower value since you are on the left
																					UpperCAmelCase     :		Optional[int]							     =		min(_lowercase	,				_lowercase		)
														elif (i + position) % 2 != 0 and l_send is not None:
																					# send your value to your left neighbor
																					process_lock.acquire()
																					l_send[1].send(_lowercase		)
																					process_lock.release()

																					# receive your left neighbor's value
																					process_lock.acquire()
																					UpperCAmelCase     :		Optional[Any]							     =		lr_cv[0].recv()
																					process_lock.release()

																					# take the higher value since you are on the right
																					UpperCAmelCase     :		Dict							     =		max(_lowercase	,				_lowercase		)
    # after all swaps are performed, send the values back to main
							result_pipe[1].send(_lowercase		)



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		int							     =		[]
							UpperCAmelCase     :		str							     =		[]
							# initialize the list of pipes where the values will be retrieved
							for _ in arr:
														result_pipe.append(Pipe()		)
							# creates the processes
							# the first and last process only have one neighbor so they are made outside
							# of the loop
							UpperCAmelCase     :		List[str]							     =		Pipe()
							UpperCAmelCase     :		Union[str, Any]							     =		Pipe()
							process_array_.append(
							    Process(
							        target=_lowercase	,				args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])	,				)		)
							UpperCAmelCase     :		Union[str, Any]							     =		temp_rs
							UpperCAmelCase     :		Any							     =		temp_rr

							for i in range(1	,				len(_lowercase		) - 1		):
														UpperCAmelCase     :		Optional[Any]							     =		Pipe()
														UpperCAmelCase     :		Optional[int]							     =		Pipe()
														process_array_.append(
														    Process(
														        target=_lowercase	,				args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])	,				)		)
														UpperCAmelCase     :		int							     =		temp_rs
														UpperCAmelCase     :		Any							     =		temp_rr

							process_array_.append(
							    Process(
							        target=_lowercase	,				args=(
							            len(_lowercase		) - 1,
							            arr[len(_lowercase		) - 1],
							            temp_ls,
							            None,
							            temp_lr,
							            None,
							            result_pipe[len(_lowercase		) - 1],
							        )	,				)		)

							# start the processes
							for p in process_array_:
														p.start()

							# wait for the processes to end and write their values to the list
							for p in range(0	,				len(_lowercase		)		):
														UpperCAmelCase     :		Any							     =		result_pipe[p][0].recv()
														process_array_[p].join()
							return arr



def       __lowerCamelCase  (					)      ->			List[str]:
							UpperCAmelCase     :		Any							     =		list(range(1_0	,				0	,				-1		)		)
							print("""Initial List"""		)
							print(*_lowercase		)
							UpperCAmelCase     :		Dict							     =		odd_even_transposition(_lowercase		)
							print("""Sorted List\n"""		)
							print(*_lowercase		)


if __name__ == "__main__":
							main()






'''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()
a   :			List[Any]					  =    logging.get_logger(__name__)

a   :			Union[str, Any]					  =    torch.device("""cpu""")



def       __lowerCamelCase  (					)      ->			Any:
							UpperCAmelCase     :		Optional[Any]							     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase     :		Dict							     =		Image.open(requests.get(_lowercase	,				stream=_lowercase		).raw		)
							return im



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]		)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]		)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]		)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			str:
							UpperCAmelCase     :		Union[str, Any]							     =		dct.pop(_lowercase		)
							UpperCAmelCase     :		str							     =		val



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



@torch.no_grad()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[Any]							     =		SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							UpperCAmelCase     :		List[Any]							     =		1_0_0_0
							UpperCAmelCase     :		List[str]							     =		"""huggingface/label-files"""
							UpperCAmelCase     :		Tuple							     =		"""imagenet-1k-id2label.json"""
							UpperCAmelCase     :		Dict							     =		json.load(open(hf_hub_download(_lowercase	,				_lowercase	,				repo_type="""dataset"""		)	,				"""r"""		)		)
							UpperCAmelCase     :		Tuple							     =		{int(_lowercase		): v for k, v in idalabel.items()}
							UpperCAmelCase     :		Tuple							     =		idalabel
							UpperCAmelCase     :		List[Any]							     =		{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														UpperCAmelCase     :		List[Any]							     =		[3, 3, 6, 4]
														UpperCAmelCase     :		int							     =		[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														UpperCAmelCase     :		str							     =		[3, 3, 9, 6]
														UpperCAmelCase     :		str							     =		[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														UpperCAmelCase     :		List[Any]							     =		[4, 3, 1_0, 5]
														UpperCAmelCase     :		Union[str, Any]							     =		[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														UpperCAmelCase     :		Any							     =		[4, 4, 1_2, 6]
														UpperCAmelCase     :		List[Any]							     =		[6_4, 1_2_8, 3_2_0, 5_1_2]

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

							UpperCAmelCase     :		Tuple							     =		create_rename_keys(_lowercase		)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(_lowercase	,				_lowercase	,				_lowercase		)

							# load HuggingFace model
							UpperCAmelCase     :		str							     =		SwiftFormerForImageClassification(_lowercase		).eval()
							hf_model.load_state_dict(_lowercase		)

							# prepare test inputs
							UpperCAmelCase     :		Any							     =		prepare_img()
							UpperCAmelCase     :		List[Any]							     =		ViTImageProcessor.from_pretrained("""preprocessor_config"""		)
							UpperCAmelCase     :		List[str]							     =		processor(images=_lowercase	,				return_tensors="""pt"""		)

							# compare outputs from both models
							UpperCAmelCase     :		List[str]							     =		get_expected_output(_lowercase		)
							UpperCAmelCase     :		Dict							     =		hf_model(inputs["""pixel_values"""]		).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]		)
							assert torch.allclose(hf_logits[0, 0:5]	,				_lowercase	,				atol=1e-3		)

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


if __name__ == "__main__":
							a   :			Tuple					  =    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   :			Union[str, Any]					  =    parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

