


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							:					Tuple     						=     logging.get_logger(__name__)

A							:					Tuple     						=     torch.device('cpu')





def 	_lowerCAmelCase       (    )						->	Optional[int]:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	List[Any]:


						'''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 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						__snake_case				=		dct.pop(_lowerCAmelCase )
						__snake_case				=		val





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Any:


						'''simple docstring'''






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





@torch.no_grad()
def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Dict:


						'''simple docstring'''






						__snake_case				=		SwiftFormerConfig()

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

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

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

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

						elif swiftformer_name == "swiftformer_l3":
												__snake_case				=		[4, 4, 12, 6]
												__snake_case				=		[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				=		torch.hub.load_state_dict_from_url(_lowerCAmelCase      ,	map_location="cpu"      ,	check_hash=_lowerCAmelCase )
												else:
																		__snake_case				=		torch.load(_lowerCAmelCase      ,	map_location="cpu" )
						__snake_case				=		checkpoint

						__snake_case				=		create_rename_keys(_lowerCAmelCase )
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )

						# load HuggingFace model
						__snake_case				=		SwiftFormerForImageClassification(_lowerCAmelCase ).eval()
						hf_model.load_state_dict(_lowerCAmelCase )

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

						# compare outputs from both models
						__snake_case				=		get_expected_output(_lowerCAmelCase )
						__snake_case				=		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							:					str     						=     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							:					Any     						=     parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









class     UpperCamelCase:


					def __init__(						self	:		Any						)			->	Any:




											'''simple docstring'''

											__snake_case				=		0
											__snake_case				=		0
											__snake_case				=		{}


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	Optional[Any]:




											'''simple docstring'''

											if vertex not in self.adjacency:
																	__snake_case				=		{}
																	self.num_vertices += 1


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Optional[Any]:




											'''simple docstring'''

											self.add_vertex(SCREAMING_SNAKE_CASE						)
											self.add_vertex(SCREAMING_SNAKE_CASE						)

											if head == tail:
																	return

											__snake_case				=		weight
											__snake_case				=		weight


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		self.get_edges()
											for edge in edges:
																	__snake_case		,					__snake_case		,					__snake_case				=		edge
																	edges.remove((tail, head, weight)						)
											for i in range(len(SCREAMING_SNAKE_CASE						)						):
																	__snake_case				=		list(edges[i]						)

											edges.sort(key=lambda SCREAMING_SNAKE_CASE						: e[2]						)
											for i in range(len(SCREAMING_SNAKE_CASE						) - 1						):
																	if edges[i][2] >= edges[i + 1][2]:
																							__snake_case				=		edges[i][2] + 1
											for edge in edges:
																	__snake_case		,					__snake_case		,					__snake_case				=		edge
																	__snake_case				=		weight
																	__snake_case				=		weight


					def __str__(						self	:		Tuple						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		""
											for tail in self.adjacency:
																	for head in self.adjacency[tail]:
																							__snake_case				=		self.adjacency[head][tail]
																							string += f'''{head} -> {tail} == {weight}\n'''
											return string.rstrip("\n"						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		[]
											for tail in self.adjacency:
																	for head in self.adjacency[tail]:
																							output.append((tail, head, self.adjacency[head][tail])						)
											return output


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[Any]:




											'''simple docstring'''

											return self.adjacency.keys()


					@staticmethod
					def 							SCREAMING_SNAKE_CASE_  (						SCREAMING_SNAKE_CASE	:		Any=None     ,    SCREAMING_SNAKE_CASE	:		List[Any]=None						)			->	int:




											'''simple docstring'''

											__snake_case				=		Graph()
											if vertices is None:
																	__snake_case				=		[]
											if edges is None:
																	__snake_case				=		[]
											for vertex in vertices:
																	g.add_vertex(SCREAMING_SNAKE_CASE						)
											for edge in edges:
																	g.add_edge(*SCREAMING_SNAKE_CASE						)
											return g






					class     UpperCamelCase:


										def __init__(						self	:		Union[str, Any]						)			->	Union[str, Any]:




																'''simple docstring'''

																__snake_case				=		{}
																__snake_case				=		{}


										def __len__(						self	:		List[str]						)			->	Dict:




																'''simple docstring'''

																return len(self.parent						)


										def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	List[str]:




																'''simple docstring'''

																if item in self.parent:
																						return self.find(SCREAMING_SNAKE_CASE						)

																__snake_case				=		item
																__snake_case				=		0
																return item


										def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	Any:




																'''simple docstring'''

																if item not in self.parent:
																						return self.make_set(SCREAMING_SNAKE_CASE						)
																if item != self.parent[item]:
																						__snake_case				=		self.find(self.parent[item]						)
																return self.parent[item]






										def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Dict:




																'''simple docstring'''

																__snake_case				=		self.find(SCREAMING_SNAKE_CASE						)
																__snake_case				=		self.find(SCREAMING_SNAKE_CASE						)

																if roota == roota:
																						return roota

																if self.rank[roota] > self.rank[roota]:
																						__snake_case				=		roota
																						return roota

																if self.rank[roota] < self.rank[roota]:
																						__snake_case				=		roota
																						return roota

																if self.rank[roota] == self.rank[roota]:
																						self.rank[roota] += 1
																						__snake_case				=		roota
																						return roota
																return None




					@staticmethod
					def 							SCREAMING_SNAKE_CASE_  (						SCREAMING_SNAKE_CASE	:		str						)			->	Any:




											'''simple docstring'''

											__snake_case				=		graph.num_vertices

											__snake_case				=		Graph.UnionFind()
											__snake_case				=		[]
											while num_components > 1:
																	__snake_case				=		{}
																	for vertex in graph.get_vertices():
																							__snake_case				=		-1

																	__snake_case				=		graph.get_edges()
																	for edge in edges:
																							__snake_case		,					__snake_case		,					__snake_case				=		edge
																							edges.remove((tail, head, weight)						)
																	for edge in edges:
																							__snake_case		,					__snake_case		,					__snake_case				=		edge
																							__snake_case				=		union_find.find(SCREAMING_SNAKE_CASE						)
																							__snake_case				=		union_find.find(SCREAMING_SNAKE_CASE						)
																							if seta != seta:
																													if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																			__snake_case				=		[head, tail, weight]

																													if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																			__snake_case				=		[head, tail, weight]
																	for vertex in cheap_edge:
																							if cheap_edge[vertex] != -1:
																													__snake_case		,					__snake_case		,					__snake_case				=		cheap_edge[vertex]
																													if union_find.find(SCREAMING_SNAKE_CASE						) != union_find.find(SCREAMING_SNAKE_CASE						):
																																			union_find.union(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																																			mst_edges.append(cheap_edge[vertex]						)
																																			__snake_case				=		num_components - 1
											__snake_case				=		Graph.build(edges=SCREAMING_SNAKE_CASE						)
											return mst





