






"""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_							      = logging.get_logger(__name__)

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

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCAmelCase_	:					str     	=   Image.open(requests.get(A__      ,stream=A__		).raw		)
			return im

def 							snake_case (  A__		):
			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 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Tuple     	=   dct.pop(A__		)
			UpperCAmelCase_	:					Optional[Any]     	=   val

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

@torch.no_grad()
def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			UpperCAmelCase_	:					Optional[Any]     	=   10_00
			UpperCAmelCase_	:					str     	=   "huggingface/label-files"
			UpperCAmelCase_	:					str     	=   "imagenet-1k-id2label.json"
			UpperCAmelCase_	:					List[str]     	=   json.load(open(hf_hub_download(A__      ,A__      ,repo_type="dataset"		)      ,"r"		)		)
			UpperCAmelCase_	:					Tuple     	=   {int(A__		): v for k, v in idalabel.items()}
			UpperCAmelCase_	:					List[Any]     	=   idalabel
			UpperCAmelCase_	:					Optional[Any]     	=   {v: k for k, v in idalabel.items()}

			# size of the architecture
			if swiftformer_name == "swiftformer_xs":
						UpperCAmelCase_	:					Tuple     	=   [3, 3, 6, 4]
						UpperCAmelCase_	:					str     	=   [48, 56, 1_12, 2_20]

			elif swiftformer_name == "swiftformer_s":
						UpperCAmelCase_	:					Optional[Any]     	=   [3, 3, 9, 6]
						UpperCAmelCase_	:					Optional[Any]     	=   [48, 64, 1_68, 2_24]

			elif swiftformer_name == "swiftformer_l1":
						UpperCAmelCase_	:					int     	=   [4, 3, 10, 5]
						UpperCAmelCase_	:					Union[str, Any]     	=   [48, 96, 1_92, 3_84]

			elif swiftformer_name == "swiftformer_l3":
						UpperCAmelCase_	:					Dict     	=   [4, 4, 12, 6]
						UpperCAmelCase_	:					Optional[int]     	=   [64, 1_28, 3_20, 5_12]

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

			UpperCAmelCase_	:					Dict     	=   create_rename_keys(A__		)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(A__      ,A__      ,A__		)

			# load HuggingFace model
			UpperCAmelCase_	:					Optional[int]     	=   SwiftFormerForImageClassification(A__		).eval()
			hf_model.load_state_dict(A__		)

			# prepare test inputs
			UpperCAmelCase_	:					Tuple     	=   prepare_img()
			UpperCAmelCase_	:					int     	=   ViTImageProcessor.from_pretrained("preprocessor_config"		)
			UpperCAmelCase_	:					int     	=   processor(images=A__      ,return_tensors="pt"		)

			# compare outputs from both models
			UpperCAmelCase_	:					List[Any]     	=   get_expected_output(A__		)
			UpperCAmelCase_	:					int     	=   hf_model(inputs["pixel_values"]		).logits

			assert hf_logits.shape == torch.Size([1, 10_00]		)
			assert torch.allclose(hf_logits[0, 0:5]      ,A__      ,atol=1e-3		)

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


if __name__ == "__main__":
			lowerCamelCase_							      = 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_							      = parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=0
			if start < end:
						snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=a[end]
						snake_case:    List[Any]      							=a[pivot]
						snake_case:    int      							=temp

						snake_case							,  snake_case:    Optional[int]      							=_in_place_partition(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							__UpperCAmelCase				,							p - 1					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							p + 1				,							__UpperCAmelCase					)
			return count







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=0
			snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=a[end]
			snake_case:    List[str]      							=a[pivot]
			snake_case:    Dict      							=temp
			snake_case:    Union[str, Any]      							=start - 1
			for index in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									snake_case:    Optional[Any]      							=new_pivot_index + 1
									snake_case:    Dict      							=a[new_pivot_index]
									snake_case:    Union[str, Any]      							=a[index]
									snake_case:    List[Any]      							=temp

			snake_case:    Union[str, Any]      							=a[new_pivot_index + 1]
			snake_case:    Tuple      							=a[end]
			snake_case:    Union[str, Any]      							=temp
			return new_pivot_index + 1, count


a							   =				TemporaryFile()
a							   =				100  # 1000 elements are to be sorted


a   ,      a							   =				0, 1  # mean and standard deviation
a							   =				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
a							   =				np.load(outfile)
a							   =				len(M) - 1
a							   =				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)







print(z)




