
'''simple docstring'''


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 lowercase_	(   _lowercase				)     ->							List[str]:
					'''simple docstring'''

					lowerCamelCase_					:      int     =		SwinvaConfig()
					lowerCamelCase_					:      Union[str, Any]     =		swinva_name.split('''_'''				)

					lowerCamelCase_					:      List[Any]     =		name_split[1]
					if "to" in name_split[3]:
										lowerCamelCase_					:      Union[str, Any]     =		int(name_split[3][-3:]				)
					else:
										lowerCamelCase_					:      Optional[int]     =		int(name_split[3]				)
					if "to" in name_split[2]:
										lowerCamelCase_					:      Dict     =		int(name_split[2][-2:]				)
					else:
										lowerCamelCase_					:      int     =		int(name_split[2][6:]				)

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

					if "to" in swinva_name:
										lowerCamelCase_					:      str     =		(12, 12, 12, 6)

					if ("22k" in swinva_name) and ("to" not in swinva_name):
										lowerCamelCase_					:      str     =		21_841
										lowerCamelCase_					:      str     =		"""huggingface/label-files"""
										lowerCamelCase_					:      List[str]     =		"""imagenet-22k-id2label.json"""
										lowerCamelCase_					:      Union[str, Any]     =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       repo_type='''dataset'''				)			,       '''r'''				)				)
										lowerCamelCase_					:      Optional[int]     =		{int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
										lowerCamelCase_					:      int     =		idalabel
										lowerCamelCase_					:      Optional[Any]     =		{v: k for k, v in idalabel.items()}

					else:
										lowerCamelCase_					:      int     =		1_000
										lowerCamelCase_					:      List[Any]     =		"""huggingface/label-files"""
										lowerCamelCase_					:      List[Any]     =		"""imagenet-1k-id2label.json"""
										lowerCamelCase_					:      Union[str, Any]     =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       repo_type='''dataset'''				)			,       '''r'''				)				)
										lowerCamelCase_					:      Union[str, Any]     =		{int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
										lowerCamelCase_					:      Dict     =		idalabel
										lowerCamelCase_					:      Optional[int]     =		{v: k for k, v in idalabel.items()}

					lowerCamelCase_					:      Dict     =		img_size
					lowerCamelCase_					:      int     =		num_classes
					lowerCamelCase_					:      str     =		embed_dim
					lowerCamelCase_					:      Dict     =		depths
					lowerCamelCase_					:      int     =		num_heads
					lowerCamelCase_					:      Optional[Any]     =		window_size

					return config




def lowercase_	(   _lowercase				)     ->							int:
					'''simple docstring'''

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

					if "head" in name:
										lowerCamelCase_					:      Union[str, Any]     =		name.replace('''head'''			,       '''classifier'''				)
					else:
										lowerCamelCase_					:      Optional[Any]     =		"""swinv2.""" + name

					return name




def lowercase_	(   _lowercase			,       _lowercase				)     ->							int:
					'''simple docstring'''

					for key in orig_state_dict.copy().keys():
										lowerCamelCase_					:      Tuple     =		orig_state_dict.pop(SCREAMING_SNAKE_CASE__				)

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

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

					return orig_state_dict




def lowercase_	(   _lowercase			,       _lowercase				)     ->							Tuple:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		timm.create_model(SCREAMING_SNAKE_CASE__			,       pretrained=SCREAMING_SNAKE_CASE__				)
					timm_model.eval()

					lowerCamelCase_					:      Any     =		get_swinva_config(SCREAMING_SNAKE_CASE__				)
					lowerCamelCase_					:      List[str]     =		SwinvaForImageClassification(SCREAMING_SNAKE_CASE__				)
					model.eval()

					lowerCamelCase_					:      Any     =		convert_state_dict(timm_model.state_dict()			,       SCREAMING_SNAKE_CASE__				)
					model.load_state_dict(SCREAMING_SNAKE_CASE__				)

					lowerCamelCase_					:      Any     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""

					lowerCamelCase_					:      Optional[Any]     =		AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''			,       '''-'''				)				)				)
					lowerCamelCase_					:      Dict     =		Image.open(requests.get(SCREAMING_SNAKE_CASE__			,       stream=SCREAMING_SNAKE_CASE__				).raw				)
					lowerCamelCase_					:      Optional[Any]     =		image_processor(images=SCREAMING_SNAKE_CASE__			,       return_tensors='''pt'''				)

					lowerCamelCase_					:      Optional[int]     =		timm_model(inputs['''pixel_values''']				)
					lowerCamelCase_					:      List[str]     =		model(**SCREAMING_SNAKE_CASE__				).logits

					assert torch.allclose(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       atol=1e-3				)

					print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""				)
					model.save_pretrained(SCREAMING_SNAKE_CASE__				)

					print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
					image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)

					model.push_to_hub(
					    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__				)			,       organization='''nandwalritik'''			,       commit_message='''Add model'''			,       )


if __name__ == "__main__":
					__lowercase							:   Optional[int]					=     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.'''
					)

					__lowercase							:   Optional[int]					=     parser.parse_args()
					convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)




'''simple docstring'''


def lowercase_	(   _lowercase = 1_000				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		-1
					lowerCamelCase_					:      Optional[Any]     =		0
					for a in range(1			,       n // 3				):
										# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
										lowerCamelCase_					:      Any     =		(n * n - 2 * a * n) // (2 * n - 2 * a)
										lowerCamelCase_					:      int     =		n - a - b
										if c * c == (a * a + b * b):
															lowerCamelCase_					:      int     =		a * b * c
															if candidate >= product:
																				lowerCamelCase_					:      Any     =		candidate
					return product


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


