

'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class lowerCamelCase   :

				def __init__(       self,						lowercase_,						lowercase_=sys.maxsize )    ->							List[str]:
					snake_case							=					'bilinear'
					snake_case							=					max_size
					snake_case							=					short_edge_length






				def __call__(       self,						lowercase_ )    ->							Tuple:
					snake_case							=					[]
					for img in imgs:
						snake_case			,			snake_case							=					img.shape[:2]
						# later: provide list and randomly choose index for resize
						snake_case							=					np.random.randint(self.short_edge_length[0],						self.short_edge_length[1] + 1 )
						if size == 0:
							return img
						snake_case							=					size * 1.0 / min(lowercase_,						lowercase_ )
						if h < w:
							snake_case			,			snake_case							=					size, scale * w
						else:
							snake_case			,			snake_case							=					scale * h, size
						if max(lowercase_,						lowercase_ ) > self.max_size:
							snake_case							=					self.max_size * 1.0 / max(lowercase_,						lowercase_ )
							snake_case							=					newh * scale
							snake_case							=					neww * scale
						snake_case							=					int(neww + 0.5 )
						snake_case							=					int(newh + 0.5 )

						if img.dtype == np.uinta:
							snake_case							=					Image.fromarray(lowercase_ )
							snake_case							=					pil_image.resize((neww, newh),						PILImageResampling.BILINEAR )
							snake_case							=					np.asarray(lowercase_ )
						else:
							snake_case							=					img.permute(2,						0,						1 ).unsqueeze(0 )  # 3, 0, 1)  # hw(c) -> nchw
							snake_case							=					nn.functional.interpolate(
							    lowercase_,						(newh, neww),						mode=self.interp_method,						align_corners=lowercase_ ).squeeze(0 )
						img_augs.append(lowercase_ )

					return img_augs







class lowerCamelCase   :

				def __init__(       self,						lowercase_ )    ->							Tuple:
					snake_case							=					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],						cfg.INPUT.MAX_SIZE_TEST )
					snake_case							=					cfg.INPUT.FORMAT
					snake_case							=					cfg.SIZE_DIVISIBILITY
					snake_case							=					cfg.PAD_VALUE
					snake_case							=					cfg.INPUT.MAX_SIZE_TEST
					snake_case							=					cfg.MODEL.DEVICE
					snake_case							=					torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ),						1,						1 )
					snake_case							=					torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ),						1,						1 )
					snake_case							=					lambda lowercase_ : (x - self.pixel_mean) / self.pixel_std

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							int:
					snake_case							=					tuple(max(lowercase_ ) for s in zip(*[img.shape for img in images] ) )
					snake_case							=					[im.shape[-2:] for im in images]
					snake_case							=					[
					    nn.functional.pad(
					        lowercase_,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],						value=self.pad_value,						)
					    for size, im in zip(lowercase_,						lowercase_ )
					]

					return torch.stack(lowercase_ ), torch.tensor(lowercase_ )






				def __call__(       self,						lowercase_,						lowercase_=False )    ->							Optional[Any]:
					with torch.no_grad():
						if not isinstance(lowercase_,						lowercase_ ):
							snake_case							=					[images]
						if single_image:
							assert len(lowercase_ ) == 1
						for i in range(len(lowercase_ ) ):
							if isinstance(images[i],						torch.Tensor ):
								images.insert(lowercase_,						images.pop(lowercase_ ).to(self.device ).float() )
							elif not isinstance(images[i],						torch.Tensor ):
								images.insert(
								    lowercase_,						torch.as_tensor(img_tensorize(images.pop(lowercase_ ),						input_format=self.input_format ) )
								    .to(self.device )
								    .float(),						)
            # resize smallest edge
						snake_case							=					torch.tensor([im.shape[:2] for im in images] )
						snake_case							=					self.aug(lowercase_ )
						# transpose images and convert to torch tensors
						# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
						# now normalize before pad to avoid useless arithmetic
						snake_case							=					[self.normalizer(lowercase_ ) for x in images]
						# now pad them to do the following operations
						snake_case			,			snake_case							=					self.pad(lowercase_ )
						# Normalize

						if self.size_divisibility > 0:
							raise NotImplementedError()
						# pad
						snake_case							=					torch.true_divide(lowercase_,						lowercase_ )
						if single_image:
							return images[0], sizes[0], scales_yx[0]
						else:
							return images, sizes, scales_yx

def      __magic_name__							(			A       , A				)      ->		str:
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes


def      __magic_name__							(			A       , A				)      ->		Any:
	assert torch.isfinite(A				).all(), "Box tensor contains infinite or NaN!"
	snake_case			,			snake_case							=					box_size
	tensor[:, 0].clamp_(min=0       , max=A				)
	tensor[:, 1].clamp_(min=0       , max=A				)
	tensor[:, 2].clamp_(min=0       , max=A				)
	tensor[:, 3].clamp_(min=0       , max=A				)








'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def      __magic_name__							(			A				)      ->		str:
	snake_case							=					args.serialization_dir
	snake_case							=					args.pruning_method
	snake_case							=					args.threshold

	snake_case							=					torch.load(os.path.join(A       , 'pytorch_model.bin'				)       , map_location='cpu'				)

	snake_case							=					0  # Number of remaining (not pruned) params in the encoder
	snake_case							=					0  # Number of params in the encoder

	print('name'.ljust(6_0       , ' '				)       , 'Remaining Weights %'       , 'Remaining Weight'				)
	for name, param in st.items():
		if "encoder" not in name:
			continue

		if "mask_scores" in name:
			if pruning_method == "topK":
				snake_case							=					TopKBinarizer.apply(A       , A				).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case							=					ThresholdBinarizer.apply(A       , A       , A				).sum().item()
			elif pruning_method == "l0":
				snake_case			,			snake_case							=					-0.1, 1.1
				snake_case							=					torch.sigmoid(A				)
				snake_case							=					s * (r - l) + l
				snake_case							=					s_bar.clamp(min=0.0       , max=1.0				)
				snake_case							=					(mask > 0.0).sum().item()
			else:
				raise ValueError('Unknown pruning method'				)
			remaining_count += mask_ones
			print(name.ljust(6_0       , ' '				)       , str(round(1_0_0 * mask_ones / param.numel()       , 3				)				).ljust(2_0       , ' '				)       , str(A				)				)
		else:
			encoder_count += param.numel()
			if "bias" in name or "LayerNorm" in name:
				remaining_count += param.numel()

	print(''				)
	print('Remaining Weights (global) %: '       , 1_0_0 * remaining_count / encoder_count				)


if __name__ == "__main__":
						lowerCAmelCase_     =      argparse.ArgumentParser()

						parser.add_argument(
						    "--pruning_method",
						    choices=["l0", "topK", "sigmoied_threshold"],
						    type=str,
						    required=True,
						    help=(
						        "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
						        " pruning)"
						    ),
						)
						parser.add_argument(
						    "--threshold",
						    type=float,
						    required=False,
						    help=(
						        "For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
						        "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
						        "Not needed for `l0`"
						    ),
						)
						parser.add_argument(
						    "--serialization_dir",
						    type=str,
						    required=True,
						    help="Folder containing the model that was previously fine-pruned",
						)

						lowerCAmelCase_     =      parser.parse_args()

						main(args)





