





'''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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a__					:			Dict          =     logging.get_logger(__name__)




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Union[str, Any]:
	snake_case__	       =   'huggingface/label-files'
	snake_case__	       =   'imagenet-1k-id2label.json'
	snake_case__	       =   json.load(open(hf_hub_download(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type='dataset'							)					,				'r'							)							)
	snake_case__	       =   {int(UpperCAmelCase_							): v for k, v in idalabel.items()}
	snake_case__	       =   {v: k for k, v in idalabel.items()}

	snake_case__	       =   'std_conv' if 'bit' in model_name else False

	# note that when using BiT as backbone for ViT-hybrid checkpoints,
	# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
	# config.conv_layer = "std_conv_same"
	snake_case__	       =   BitConfig(
	    conv_layer=UpperCAmelCase_					,				num_labels=10_00					,				idalabel=UpperCAmelCase_					,				labelaid=UpperCAmelCase_					,				)

	return config




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->str:
	if "stem.conv" in name:
		snake_case__	       =   name.replace('stem.conv'					,				'bit.embedder.convolution'							)
	if "blocks" in name:
		snake_case__	       =   name.replace('blocks'					,				'layers'							)
	if "head.fc" in name:
		snake_case__	       =   name.replace('head.fc'					,				'classifier.1'							)
	if name.startswith('norm'							):
		snake_case__	       =   'bit.' + name
	if "bit" not in name and "classifier" not in name:
		snake_case__	       =   'bit.encoder.' + name

	return name




def 						__lowerCamelCase			(	)   ->Union[str, Any]:
	snake_case__	       =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case__	       =   Image.open(requests.get(UpperCAmelCase_					,				stream=UpperCAmelCase_							).raw							)
	return im




@torch.no_grad()
def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=False							)   ->int:
	snake_case__	       =   get_config(UpperCAmelCase_							)

	# load original model from timm
	snake_case__	       =   create_model(UpperCAmelCase_					,				pretrained=UpperCAmelCase_							)
	timm_model.eval()

	# load state_dict of original model
	snake_case__	       =   timm_model.state_dict()
	for key in state_dict.copy().keys():
		snake_case__	       =   state_dict.pop(UpperCAmelCase_							)
		snake_case__	       =   val.squeeze() if 'head' in key else val

	# load HuggingFace model
	snake_case__	       =   BitForImageClassification(UpperCAmelCase_							)
	model.eval()
	model.load_state_dict(UpperCAmelCase_							)

	# create image processor
	snake_case__	       =   create_transform(**resolve_data_config({}					,				model=UpperCAmelCase_							)							)
	snake_case__	       =   transform.transforms

	snake_case__	       =   {
	    'bilinear': PILImageResampling.BILINEAR,
	    'bicubic': PILImageResampling.BICUBIC,
	    'nearest': PILImageResampling.NEAREST,
	}

	snake_case__	       =   BitImageProcessor(
	    do_resize=UpperCAmelCase_					,				size={'shortest_edge': timm_transforms[0].size}					,				resample=pillow_resamplings[timm_transforms[0].interpolation.value]					,				do_center_crop=UpperCAmelCase_					,				crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}					,				do_normalize=UpperCAmelCase_					,				image_mean=timm_transforms[-1].mean.tolist()					,				image_std=timm_transforms[-1].std.tolist()					,				)

	snake_case__	       =   prepare_img()
	snake_case__	       =   transform(UpperCAmelCase_							).unsqueeze(0							)
	snake_case__	       =   processor(UpperCAmelCase_					,				return_tensors='pt'							).pixel_values

	# verify pixel values
	assert torch.allclose(UpperCAmelCase_					,				UpperCAmelCase_							)

	# verify logits
	with torch.no_grad():
		snake_case__	       =   model(UpperCAmelCase_							)
		snake_case__	       =   outputs.logits

	print('Logits:'					,				logits[0, :3]							)
	print('Predicted class:'					,				model.config.idalabel[logits.argmax(-1							).item()]							)
	snake_case__	       =   timm_model(UpperCAmelCase_							)
	assert timm_logits.shape == outputs.logits.shape
	assert torch.allclose(UpperCAmelCase_					,				outputs.logits					,				atol=1E-3							)
	print('Looks ok!'							)

	if pytorch_dump_folder_path is not None:
		Path(UpperCAmelCase_							).mkdir(exist_ok=UpperCAmelCase_							)
		print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''							)
		model.save_pretrained(UpperCAmelCase_							)
		processor.save_pretrained(UpperCAmelCase_							)

	if push_to_hub:
		print(f'''Pushing model {model_name} and processor to the hub'''							)
		model.push_to_hub(f'''ybelkada/{model_name}'''							)
		processor.push_to_hub(f'''ybelkada/{model_name}'''							)


if __name__ == "__main__":
			a__					:			str          =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''resnetv2_50x1_bitm''',
			    type=str,
			    help='''Name of the BiT 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.'''
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    action='''store_true''',
			    help='''Whether to push the model to the hub.''',
			)

			a__					:			int          =     parser.parse_args()
			convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''




import cmath
import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->complex:
	snake_case__	       =   math.radians(UpperCAmelCase_							)
	snake_case__	       =   math.radians(UpperCAmelCase_							)

	# Convert voltage and current to rectangular form
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)

	# Calculate apparent power
	return voltage_rect * current_rect


if __name__ == "__main__":
			import doctest

			doctest.testmod()



