

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

__lowerCamelCase      :    str   		=   torch.device("""cpu""")







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   List[Any]     =						"http://images.cocodataset.org/val2017/000000039769.jpg"
	snake_case__				:   Optional[int]     =						Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,						stream=_SCREAMING_SNAKE_CASE     ).raw     )
	return im







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[str]     ):
	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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]  ,						snake_case_      :							int  ,						snake_case_      :							str     ):
	snake_case__				:   Any     =						dct.pop(_SCREAMING_SNAKE_CASE     )
	snake_case__				:   Dict     =						val







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]     ):
	snake_case__				:   int     =						[]
	for k in state_dict.keys():
		snake_case__				:   Dict     =						k
		if ".pwconv" in k:
			snake_case__				:   Union[str, Any]     =						k_new.replace(".pwconv"  ,						".point_wise_conv"     )
		if ".dwconv" in k:
			snake_case__				:   List[Any]     =						k_new.replace(".dwconv"  ,						".depth_wise_conv"     )
		if ".Proj." in k:
			snake_case__				:   Union[str, Any]     =						k_new.replace(".Proj."  ,						".proj."     )
		if "patch_embed" in k_new:
			snake_case__				:   Union[str, Any]     =						k_new.replace("patch_embed"  ,						"swiftformer.patch_embed.patch_embedding"     )
		if "network" in k_new:
			snake_case__				:   Any     =						k_new.split("."     )
			if ls[2].isdigit():
				snake_case__				:   List[str]     =						"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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Dict  ,						snake_case_      :							int  ,						snake_case_      :							List[str]     ):
	snake_case__				:   int     =						SwiftFormerConfig()

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

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

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

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

	elif swiftformer_name == "swiftformer_l3":
		snake_case__				:   str     =						[4, 4, 12, 6]
		snake_case__				:   str     =						[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__				:   Tuple     =						torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE  ,						map_location="cpu"  ,						check_hash=_SCREAMING_SNAKE_CASE     )
		else:
			snake_case__				:   List[str]     =						torch.load(_SCREAMING_SNAKE_CASE  ,						map_location="cpu"     )
	snake_case__				:   List[str]     =						checkpoint

	snake_case__				:   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
	snake_case__				:   str     =						SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE     ).eval()
	hf_model.load_state_dict(_SCREAMING_SNAKE_CASE     )

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

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

	assert hf_logits.shape == torch.Size([1, 1000]     )
	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__":
	__lowerCamelCase      :    Any   		=   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      :    Tuple   		=   parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)











from __future__ import annotations







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	snake_case__				:   str     =						[True] * limit
	snake_case__				:   str     =						False
	snake_case__				:   str     =						False
	snake_case__				:   str     =						True

	for i in range(3  ,						int(limit**0.5 + 1     )  ,						2     ):
		snake_case__				:   Optional[Any]     =						i * 2
		while index < limit:
			snake_case__				:   Union[str, Any]     =						False
			snake_case__				:   Any     =						index + i

	snake_case__				:   Optional[Any]     =						[2]

	for i in range(3  ,						snake_case_  ,						2     ):
		if is_prime[i]:
			primes.append(snake_case_     )

	return primes







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int = 1000000     ):
	snake_case__				:   Optional[int]     =						prime_sieve(snake_case_     )
	snake_case__				:   List[Any]     =						0
	snake_case__				:   List[str]     =						0

	for i in range(len(snake_case_     )     ):
		for j in range(i + length  ,						len(snake_case_     )     ):
			snake_case__				:   Dict     =						sum(primes[i:j]     )
			if sol >= ceiling:
				break

			if sol in primes:
				snake_case__				:   Tuple     =						j - i
				snake_case__				:   str     =						sol

	return largest


if __name__ == "__main__":
	print(f"{solution() = }")



