




'''simple docstring'''







from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather






@overload
def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		...






@overload
def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		...






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       aatype == rc.restype_order["G"]
		_snake_case	       =       rc.atom_order["CA"]
		_snake_case	       =       rc.atom_order["CB"]
		_snake_case	       =       torch.where(
		    is_gly[..., None].expand(*((-1,) * len(is_gly.shape  ))						,							3  )						,							all_atom_positions[..., ca_idx, :]						,							all_atom_positions[..., cb_idx, :]						,							)

		if all_atom_masks is not None:
				_snake_case	       =       torch.where(
				    lowercase_						,							all_atom_masks[..., ca_idx]						,							all_atom_masks[..., cb_idx]						,							)
				return pseudo_beta, pseudo_beta_mask
		else:
				return pseudo_beta






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       batched_gather(
		    lowercase_						,							batch["residx_atom37_to_atom14"]						,							dim=-2						,							no_batch_dims=len(atomaa.shape[:-2]  )						,							)

		_snake_case	       =       atomaa_data * batch["atom37_atom_exists"][..., None]

		return atomaa_data






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       template_feats["template_aatype"]
		_snake_case	       =       template_feats["template_torsion_angles_sin_cos"]
		_snake_case	       =       template_feats["template_alt_torsion_angles_sin_cos"]
		_snake_case	       =       template_feats["template_torsion_angles_mask"]
		_snake_case	       =       torch.cat(
		    [
		        nn.functional.one_hot(lowercase_						,							22  ),
		        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]						,							14  ),
		        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]						,							14  ),
		        torsion_angles_mask,
		    ]						,							dim=-1						,							)

		return template_angle_feat






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_ = False						,							lowercase_ = 1e-20						,							lowercase_ = 1e8						,							):



		'''simple docstring'''







		_snake_case	       =       batch["template_pseudo_beta_mask"]
		_snake_case	       =       template_mask[..., None] * template_mask[..., None, :]

		# Compute distogram (this seems to differ slightly from Alg. 5)
		_snake_case	       =       batch["template_pseudo_beta"]
		_snake_case	       =       torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2						,							dim=-1						,							keepdim=lowercase_  )
		_snake_case	       =       torch.linspace(lowercase_						,							lowercase_						,							lowercase_						,							device=tpb.device  ) ** 2
		_snake_case	       =       torch.cat([lower[1:], lower.new_tensor([inf]  )]						,							dim=-1  )
		_snake_case	       =       ((dgram > lower) * (dgram < upper)).type(dgram.dtype  )

		_snake_case	       =       [dgram, template_mask_ad[..., None]]

		_snake_case	       =       nn.functional.one_hot(
		    batch["template_aatype"]						,							rc.restype_num + 2						,							)

		_snake_case	       =       batch["template_aatype"].shape[-1]
		to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]						,							lowercase_						,							-1						,							-1  )  )
		to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]						,							-1						,							lowercase_						,							-1  )  )

		_snake_case       ,    _snake_case       ,    _snake_case	       =       [rc.atom_order[a] for a in ["N", "CA", "C"]]
		_snake_case	       =       Rigid.make_transform_from_reference(
		    n_xyz=batch["template_all_atom_positions"][..., n, :]						,							ca_xyz=batch["template_all_atom_positions"][..., ca, :]						,							c_xyz=batch["template_all_atom_positions"][..., c, :]						,							eps=lowercase_						,							)
		_snake_case	       =       rigids.get_trans()[..., None, :, :]
		_snake_case	       =       rigids[..., None].invert_apply(lowercase_  )

		_snake_case	       =       torch.rsqrt(eps + torch.sum(rigid_vec**2						,							dim=-1  )  )

		_snake_case	       =       batch["template_all_atom_mask"]
		_snake_case	       =       t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
		_snake_case	       =       template_mask[..., None] * template_mask[..., None, :]

		_snake_case	       =       inv_distance_scalar * template_mask_ad
		_snake_case	       =       rigid_vec * inv_distance_scalar[..., None]

		if not use_unit_vector:
				_snake_case	       =       unit_vector * 0.0

		to_concat.extend(torch.unbind(unit_vector[..., None, :]						,							dim=-1  )  )
		to_concat.append(template_mask_ad[..., None]  )

		_snake_case	       =       torch.cat(lowercase_						,							dim=-1  )
		_snake_case	       =       act * template_mask_ad[..., None]

		return act






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       nn.functional.one_hot(batch["extra_msa"]						,							23  )
		_snake_case	       =       [
		    msa_ahot,
		    batch["extra_has_deletion"].unsqueeze(-1  ),
		    batch["extra_deletion_value"].unsqueeze(-1  ),
		]
		return torch.cat(lowercase_						,							dim=-1  )






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							):



		'''simple docstring'''







		_snake_case	       =       rrgdf[aatype, ...]

		# [*, N, 8] transformations, i.e.
		#   One [*, N, 8, 3, 3] rotation matrix and
		#   One [*, N, 8, 3]    translation matrix
		_snake_case	       =       r.from_tensor_axa(lowercase_  )

		_snake_case	       =       alpha.new_zeros((*((1,) * len(alpha.shape[:-1]  )), 2)  )
		_snake_case	       =       1

		# [*, N, 8, 2]
		_snake_case	       =       torch.cat([bb_rot.expand(*alpha.shape[:-2]						,							-1						,							-1  ), alpha]						,							dim=-2  )

		# [*, N, 8, 3, 3]
		# Produces rotation matrices of the form:
		# [
		#   [1, 0  , 0  ],
		#   [0, a_2,-a_1],
		#   [0, a_1, a_2]
		# ]
		# This follows the original code rather than the supplement, which uses
		# different indices.

		_snake_case	       =       alpha.new_zeros(default_r.get_rots().get_rot_mats().shape  )
		_snake_case	       =       1
		_snake_case	       =       alpha[..., 1]
		_snake_case	       =       -alpha[..., 0]
		_snake_case	       =       alpha

		_snake_case	       =       default_r.compose(Rigid(Rotation(rot_mats=lowercase_  )						,							lowercase_  )  )

		_snake_case	       =       all_frames[..., 5]
		_snake_case	       =       all_frames[..., 6]
		_snake_case	       =       all_frames[..., 7]

		_snake_case	       =       all_frames[..., 4]
		_snake_case	       =       chia_frame_to_bb.compose(lowercase_  )
		_snake_case	       =       chia_frame_to_bb.compose(lowercase_  )
		_snake_case	       =       chia_frame_to_bb.compose(lowercase_  )

		_snake_case	       =       Rigid.cat(
		    [
		        all_frames[..., :5],
		        chia_frame_to_bb.unsqueeze(-1  ),
		        chia_frame_to_bb.unsqueeze(-1  ),
		        chia_frame_to_bb.unsqueeze(-1  ),
		    ]						,							dim=-1						,							)

		_snake_case	       =       r[..., None].compose(lowercase_  )

		return all_frames_to_global






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							):



		'''simple docstring'''







		_snake_case	       =       group_idx[aatype, ...]

		# [*, N, 14, 8]
		_snake_case	       =       nn.functional.one_hot(
		    lowercase_						,							num_classes=default_frames.shape[-3]						,							)

		# [*, N, 14, 8]
		_snake_case	       =       r[..., None, :] * group_mask_one_hot

		# [*, N, 14]
		_snake_case	       =       t_atoms_to_global.map_tensor_fn(lambda lowercase_  : torch.sum(lowercase_						,							dim=-1  )  )

		# [*, N, 14, 1]
		_snake_case	       =       atom_mask[aatype, ...].unsqueeze(-1  )

		# [*, N, 14, 3]
		_snake_case	       =       lit_positions[aatype, ...]
		_snake_case	       =       t_atoms_to_global.apply(lowercase_  )
		_snake_case	       =       pred_positions * atom_mask

		return pred_positions











'''simple docstring'''







import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       fname.split(os.path.sep  )[-1]
		return re.search(r"^(.*)_\d+\.jpg$"						,							lowercase_  ).groups()[0]





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=None,  __UpperCamelCase=None ):
							_snake_case	       =       file_names
							_snake_case	       =       image_transform
							_snake_case	       =       label_to_id






					def __len__(    self ):
							return len(self.file_names )






					def __getitem__(    self,  __UpperCamelCase ):
							_snake_case	       =       self.file_names[idx]
							_snake_case	       =       PIL.Image.open(__UpperCamelCase )
							_snake_case	       =       raw_image.convert("RGB" )
							if self.image_transform is not None:
									_snake_case	       =       self.image_transform(__UpperCamelCase )
							_snake_case	       =       extract_label(__UpperCamelCase )
							if self.label_to_id is not None:
									_snake_case	       =       self.label_to_id[label]
							return {"image": image, "label": label}






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       Accelerator(cpu=args.cpu						,							mixed_precision=args.mixed_precision  )

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_snake_case	       =       config["lr"]
		_snake_case	       =       int(config["num_epochs"]  )
		_snake_case	       =       int(config["seed"]  )
		_snake_case	       =       int(config["batch_size"]  )
		_snake_case	       =       config["image_size"]
		if not isinstance(lowercase_						,							(list, tuple)  ):
				_snake_case	       =       (image_size, image_size)

		# Grab all the image filenames
		_snake_case	       =       [os.path.join(args.data_dir						,							lowercase_  ) for fname in os.listdir(args.data_dir  ) if fname.endswith(".jpg"  )]

		# Build the label correspondences
		_snake_case	       =       [extract_label(lowercase_  ) for fname in file_names]
		_snake_case	       =       list(set(lowercase_  )  )
		id_to_label.sort()
		_snake_case	       =       {lbl: i for i, lbl in enumerate(lowercase_  )}

		# Set the seed before splitting the data.
		np.random.seed(lowercase_  )
		torch.manual_seed(lowercase_  )
		torch.cuda.manual_seed_all(lowercase_  )

		# Split our filenames between train and validation
		_snake_case	       =       np.random.permutation(len(lowercase_  )  )
		_snake_case	       =       int(0.8 * len(lowercase_  )  )
		_snake_case	       =       random_perm[:cut]
		_snake_case	       =       random_perm[cut:]

		# For training we use a simple RandomResizedCrop
		_snake_case	       =       Compose([RandomResizedCrop(lowercase_						,							scale=(0.5, 1.0)  ), ToTensor()]  )
		_snake_case	       =       PetsDataset(
		    [file_names[i] for i in train_split]						,							image_transform=lowercase_						,							label_to_id=lowercase_  )

		# For evaluation, we use a deterministic Resize
		_snake_case	       =       Compose([Resize(lowercase_  ), ToTensor()]  )
		_snake_case	       =       PetsDataset([file_names[i] for i in eval_split]						,							image_transform=lowercase_						,							label_to_id=lowercase_  )

		# Instantiate dataloaders.
		_snake_case	       =       DataLoader(lowercase_						,							shuffle=lowercase_						,							batch_size=lowercase_						,							num_workers=4  )
		_snake_case	       =       DataLoader(lowercase_						,							shuffle=lowercase_						,							batch_size=lowercase_						,							num_workers=4  )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_snake_case	       =       create_model("resnet50d"						,							pretrained=lowercase_						,							num_classes=len(lowercase_  )  )

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		_snake_case	       =       model.to(accelerator.device  )

		# Freezing the base model
		for param in model.parameters():
				_snake_case	       =       False
		for param in model.get_classifier().parameters():
				_snake_case	       =       True

		# We normalize the batches of images to be a bit faster.
		_snake_case	       =       torch.tensor(model.default_cfg["mean"]  )[None, :, None, None].to(accelerator.device  )
		_snake_case	       =       torch.tensor(model.default_cfg["std"]  )[None, :, None, None].to(accelerator.device  )

		# Instantiate optimizer
		_snake_case	       =       torch.optim.Adam(params=model.parameters()						,							lr=lr / 25  )

		# Instantiate learning rate scheduler
		_snake_case	       =       OneCycleLR(optimizer=lowercase_						,							max_lr=lowercase_						,							epochs=lowercase_						,							steps_per_epoch=len(lowercase_  )  )

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case	       =       accelerator.prepare(
		    lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  )

		# Now we train the model
		for epoch in range(lowercase_  ):
				model.train()
				for step, batch in enumerate(lowercase_  ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						_snake_case	       =       {k: v.to(accelerator.device  ) for k, v in batch.items()}
						_snake_case	       =       (batch["image"] - mean) / std
						_snake_case	       =       model(lowercase_  )
						_snake_case	       =       torch.nn.functional.cross_entropy(lowercase_						,							batch["label"]  )
						accelerator.backward(lowercase_  )
						optimizer.step()
						lr_scheduler.step()
						optimizer.zero_grad()

				model.eval()
				_snake_case	       =       0
				_snake_case	       =       0
				for _, batch in enumerate(lowercase_  ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						_snake_case	       =       {k: v.to(accelerator.device  ) for k, v in batch.items()}
						_snake_case	       =       (batch["image"] - mean) / std
						with torch.no_grad():
								_snake_case	       =       model(lowercase_  )
						_snake_case	       =       outputs.argmax(dim=-1  )
						_snake_case       ,    _snake_case	       =       accelerator.gather_for_metrics((predictions, batch["label"])  )
						_snake_case	       =       predictions == references
						num_elems += accurate_preds.shape[0]
						accurate += accurate_preds.long().sum()

				_snake_case	       =       accurate.item() / num_elems
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''  )






def 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       argparse.ArgumentParser(description="Simple example of training script."  )
		parser.add_argument("--data_dir"						,							required=lowercase_						,							help="The data folder on disk."  )
		parser.add_argument(
		    "--mixed_precision"						,							type=lowercase_						,							default=lowercase_						,							choices=["no", "fp16", "bf16", "fp8"]						,							help="Whether to use mixed precision. Choose"
		    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
		    "and an Nvidia Ampere GPU."						,							)
		parser.add_argument(
		    "--checkpointing_steps"						,							type=lowercase_						,							default=lowercase_						,							help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."						,							)
		parser.add_argument("--cpu"						,							action="store_true"						,							help="If passed, will train on the CPU."  )
		_snake_case	       =       parser.parse_args()
		_snake_case	       =       {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
		training_function(lowercase_						,							lowercase_  )


if __name__ == "__main__":
						main()





