

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	snake_case__				:   Tuple     =						SwinvaConfig()
	snake_case__				:   Dict     =						swinva_name.split("_"     )

	snake_case__				:   Dict     =						name_split[1]
	if "to" in name_split[3]:
		snake_case__				:   Optional[int]     =						int(name_split[3][-3:]     )
	else:
		snake_case__				:   Optional[Any]     =						int(name_split[3]     )
	if "to" in name_split[2]:
		snake_case__				:   Optional[int]     =						int(name_split[2][-2:]     )
	else:
		snake_case__				:   Optional[int]     =						int(name_split[2][6:]     )

	if model_size == "tiny":
		snake_case__				:   Dict     =						96
		snake_case__				:   List[str]     =						(2, 2, 6, 2)
		snake_case__				:   List[Any]     =						(3, 6, 12, 24)
	elif model_size == "small":
		snake_case__				:   str     =						96
		snake_case__				:   Union[str, Any]     =						(2, 2, 18, 2)
		snake_case__				:   Optional[Any]     =						(3, 6, 12, 24)
	elif model_size == "base":
		snake_case__				:   List[str]     =						128
		snake_case__				:   Union[str, Any]     =						(2, 2, 18, 2)
		snake_case__				:   Optional[int]     =						(4, 8, 16, 32)
	else:
		snake_case__				:   str     =						192
		snake_case__				:   str     =						(2, 2, 18, 2)
		snake_case__				:   List[Any]     =						(6, 12, 24, 48)

	if "to" in swinva_name:
		snake_case__				:   Optional[Any]     =						(12, 12, 12, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		snake_case__				:   int     =						21841
		snake_case__				:   int     =						"huggingface/label-files"
		snake_case__				:   Union[str, Any]     =						"imagenet-22k-id2label.json"
		snake_case__				:   int     =						json.load(open(hf_hub_download(snake_case__  ,						snake_case__  ,						repo_type="dataset"     )  ,						"r"     )     )
		snake_case__				:   Union[str, Any]     =						{int(snake_case__     ): v for k, v in idalabel.items()}
		snake_case__				:   Union[str, Any]     =						idalabel
		snake_case__				:   Union[str, Any]     =						{v: k for k, v in idalabel.items()}

	else:
		snake_case__				:   List[Any]     =						1000
		snake_case__				:   List[str]     =						"huggingface/label-files"
		snake_case__				:   List[Any]     =						"imagenet-1k-id2label.json"
		snake_case__				:   str     =						json.load(open(hf_hub_download(snake_case__  ,						snake_case__  ,						repo_type="dataset"     )  ,						"r"     )     )
		snake_case__				:   List[Any]     =						{int(snake_case__     ): v for k, v in idalabel.items()}
		snake_case__				:   Dict     =						idalabel
		snake_case__				:   int     =						{v: k for k, v in idalabel.items()}

	snake_case__				:   List[str]     =						img_size
	snake_case__				:   Any     =						num_classes
	snake_case__				:   Dict     =						embed_dim
	snake_case__				:   Union[str, Any]     =						depths
	snake_case__				:   Optional[Any]     =						num_heads
	snake_case__				:   Any     =						window_size

	return config







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]     ):
	if "patch_embed.proj" in name:
		snake_case__				:   Optional[int]     =						name.replace("patch_embed.proj"  ,						"embeddings.patch_embeddings.projection"     )
	if "patch_embed.norm" in name:
		snake_case__				:   List[str]     =						name.replace("patch_embed.norm"  ,						"embeddings.norm"     )
	if "layers" in name:
		snake_case__				:   Optional[int]     =						"encoder." + name
	if "attn.proj" in name:
		snake_case__				:   Optional[int]     =						name.replace("attn.proj"  ,						"attention.output.dense"     )
	if "attn" in name:
		snake_case__				:   Union[str, Any]     =						name.replace("attn"  ,						"attention.self"     )
	if "norm1" in name:
		snake_case__				:   str     =						name.replace("norm1"  ,						"layernorm_before"     )
	if "norm2" in name:
		snake_case__				:   Optional[int]     =						name.replace("norm2"  ,						"layernorm_after"     )
	if "mlp.fc1" in name:
		snake_case__				:   Optional[int]     =						name.replace("mlp.fc1"  ,						"intermediate.dense"     )
	if "mlp.fc2" in name:
		snake_case__				:   int     =						name.replace("mlp.fc2"  ,						"output.dense"     )
	if "q_bias" in name:
		snake_case__				:   Dict     =						name.replace("q_bias"  ,						"query.bias"     )
	if "k_bias" in name:
		snake_case__				:   int     =						name.replace("k_bias"  ,						"key.bias"     )
	if "v_bias" in name:
		snake_case__				:   List[str]     =						name.replace("v_bias"  ,						"value.bias"     )
	if "cpb_mlp" in name:
		snake_case__				:   int     =						name.replace("cpb_mlp"  ,						"continuous_position_bias_mlp"     )
	if name == "norm.weight":
		snake_case__				:   str     =						"layernorm.weight"
	if name == "norm.bias":
		snake_case__				:   str     =						"layernorm.bias"

	if "head" in name:
		snake_case__				:   List[str]     =						name.replace("head"  ,						"classifier"     )
	else:
		snake_case__				:   List[str]     =						"swinv2." + name

	return name







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							List[Any]     ):
	for key in orig_state_dict.copy().keys():
		snake_case__				:   Optional[Any]     =						orig_state_dict.pop(snake_case__     )

		if "mask" in key:
			continue
		elif "qkv" in key:
			snake_case__				:   Optional[int]     =						key.split("."     )
			snake_case__				:   Any     =						int(key_split[1]     )
			snake_case__				:   Optional[Any]     =						int(key_split[3]     )
			snake_case__				:   Optional[int]     =						model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				snake_case__				:   int     =						val[:dim, :]
				snake_case__				:   Dict     =						val[dim : dim * 2, :]
				snake_case__				:   Optional[int]     =						val[-dim:, :]
			else:
				snake_case__				:   Union[str, Any]     =						val[:dim]
				snake_case__				:   Optional[Any]     =						val[
				    dim : dim * 2
				]
				snake_case__				:   Any     =						val[-dim:]
		else:
			snake_case__				:   Dict     =						val

	return orig_state_dict







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]  ,						snake_case_      :							Tuple     ):
	snake_case__				:   Any     =						timm.create_model(snake_case__  ,						pretrained=snake_case__     )
	timm_model.eval()

	snake_case__				:   int     =						get_swinva_config(snake_case__     )
	snake_case__				:   List[Any]     =						SwinvaForImageClassification(snake_case__     )
	model.eval()

	snake_case__				:   Tuple     =						convert_state_dict(timm_model.state_dict()  ,						snake_case__     )
	model.load_state_dict(snake_case__     )

	snake_case__				:   Union[str, Any]     =						"http://images.cocodataset.org/val2017/000000039769.jpg"

	snake_case__				:   List[Any]     =						AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"  ,						"-"     )     )     )
	snake_case__				:   Union[str, Any]     =						Image.open(requests.get(snake_case__  ,						stream=snake_case__     ).raw     )
	snake_case__				:   List[Any]     =						image_processor(images=snake_case__  ,						return_tensors="pt"     )

	snake_case__				:   List[str]     =						timm_model(inputs["pixel_values"]     )
	snake_case__				:   Tuple     =						model(**snake_case__     ).logits

	assert torch.allclose(snake_case__  ,						snake_case__  ,						atol=1E-3     )

	print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''     )
	model.save_pretrained(snake_case__     )

	print(F'''Saving image processor to {pytorch_dump_folder_path}'''     )
	image_processor.save_pretrained(snake_case__     )

	model.push_to_hub(
	    repo_path_or_name=Path(snake_case__  ,						snake_case__     )  ,						organization="nandwalritik"  ,						commit_message="Add model"  ,						)


if __name__ == "__main__":
	__lowerCamelCase      :    List[Any]   		=   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--swinv2_name""",
	    default="""swinv2_tiny_patch4_window8_256""",
	    type=str,
	    help="""Name of the Swinv2 timm model you\'d like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)

	__lowerCamelCase      :    Tuple   		=   parser.parse_args()
	convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)











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() = }")



