
"""simple docstring"""


from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map






def 					_snake_case (						snake_case__      :   Optional[int]   ):
	A        =      []
	A        =      []
	A        =      []

	for rt in rc.restypes:
		A        =      rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
		restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]   )
		A        =      {name: i for i, name in enumerate(lowerCamelCase__   )}
		restype_atomaa_to_atomaa_list.append(
		    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]   )

		restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]   )

	# Add dummy mapping for restype 'UNK'
	restype_atomaa_to_atomaa_list.append([0] * 14   )
	restype_atomaa_to_atomaa_list.append([0] * 37   )
	restype_atomaa_mask_list.append([0.0] * 14   )

	A        =      torch.tensor(
	    lowerCamelCase__		,					dtype=torch.intaa		,					device=protein['aatype'].device		,					)
	A        =      torch.tensor(
	    lowerCamelCase__		,					dtype=torch.intaa		,					device=protein['aatype'].device		,					)
	A        =      torch.tensor(
	    lowerCamelCase__		,					dtype=torch.floataa		,					device=protein['aatype'].device		,					)
	A        =      protein['aatype'].to(torch.long   )

	# create the mapping for (residx, atom14) --> atom37, i.e. an array
	# with shape (num_res, 14) containing the atom37 indices for this protein
	A        =      restype_atomaa_to_atomaa[protein_aatype]
	A        =      restype_atomaa_mask[protein_aatype]

	A        =      residx_atomaa_mask
	A        =      residx_atomaa_to_atomaa.long()

	# create the gather indices for mapping back
	A        =      restype_atomaa_to_atomaa[protein_aatype]
	A        =      residx_atomaa_to_atomaa.long()

	# create the corresponding mask
	A        =      torch.zeros([21, 37]		,					dtype=torch.floataa		,					device=protein['aatype'].device   )
	for restype, restype_letter in enumerate(rc.restypes   ):
		A        =      rc.restype_atoa[restype_letter]
		A        =      rc.residue_atoms[restype_name]
		for atom_name in atom_names:
			A        =      rc.atom_order[atom_name]
			A        =      1

	A        =      restype_atomaa_mask[protein_aatype]
	A        =      residx_atomaa_mask

	return protein






def 					_snake_case (						snake_case__      :   List[Any]   ):
	A        =      tree_map(lambda snake_case__   : torch.tensor(lowerCamelCase__		,					device=batch['aatype'].device   )		,					lowerCamelCase__		,					np.ndarray   )
	A        =      tensor_tree_map(lambda snake_case__   : np.array(lowerCamelCase__   )		,					make_atomaa_masks(lowerCamelCase__   )   )
	return out

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__A				=1_6
__A				=3_2





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = 1_6    ):
				lowerCamelCase_						=			AutoTokenizer.from_pretrained("bert-base-cased"    )
				lowerCamelCase_						=			load_dataset("glue"	, "mrpc"    )

				def tokenize_function(lowerCamelCase__    ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCamelCase_						=			tokenizer(examples["sentence1"]	, examples["sentence2"]	, truncation=lowerCamelCase__	, max_length=lowerCamelCase__    )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								lowerCamelCase_						=			datasets.map(
								    lowerCamelCase__	, batched=lowerCamelCase__	, remove_columns=["idx", "sentence1", "sentence2"]	, )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				lowerCamelCase_						=			tokenized_datasets.rename_column("label"	, "labels"    )

				def collate_fn(lowerCamelCase__    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCamelCase_						=			1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCamelCase_						=			1_6
								elif accelerator.mixed_precision != "no":
												lowerCamelCase_						=			8
								else:
												lowerCamelCase_						=			None

								return tokenizer.pad(
								    lowerCamelCase__	, padding="longest"	, max_length=lowerCamelCase__	, pad_to_multiple_of=lowerCamelCase__	, return_tensors="pt"	, )

				# Instantiate dataloaders.
				lowerCamelCase_						=			DataLoader(
				    tokenized_datasets["train"]	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__    )
				lowerCamelCase_						=			DataLoader(
				    tokenized_datasets["validation"]	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__    )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
			from accelerate.test_utils.training import mocked_dataloaders

			__A				=mocked_dataloaders  # noqa: F811





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"	, lowerCamelCase__    ) == "1":
								lowerCamelCase_						=			2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								lowerCamelCase_						=			Accelerator(
								    cpu=args.cpu	, mixed_precision=args.mixed_precision	, log_with="all"	, project_dir=args.project_dir    )
				else:
								lowerCamelCase_						=			Accelerator(cpu=args.cpu	, mixed_precision=args.mixed_precision    )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCamelCase_						=			config["lr"]
				lowerCamelCase_						=			int(config["num_epochs"]    )
				lowerCamelCase_						=			int(config["seed"]    )
				lowerCamelCase_						=			int(config["batch_size"]    )
				set_seed(lowerCamelCase__    )

				lowerCamelCase_	,    lowerCamelCase_						=			get_dataloaders(lowerCamelCase__	, lowerCamelCase__    )
				lowerCamelCase_						=			evaluate.load("glue"	, "mrpc"    )

				# If the batch size is too big we use gradient accumulation
				lowerCamelCase_						=			1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCamelCase_						=			batch_size // MAX_GPU_BATCH_SIZE
								lowerCamelCase_						=			MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCamelCase_						=			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"	, return_dict=lowerCamelCase__    )

				# 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).
				lowerCamelCase_						=			model.to(accelerator.device    )

				# Instantiate optimizer
				lowerCamelCase_						=			AdamW(params=model.parameters()	, lr=lowerCamelCase__    )

				# Instantiate scheduler
				lowerCamelCase_						=			get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase__	, num_warmup_steps=1_0_0	, num_training_steps=(len(lowerCamelCase__    ) * num_epochs) // gradient_accumulation_steps	, )

				# 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.
				lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			accelerator.prepare(
				    lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								lowerCamelCase_						=			os.path.split(lowerCamelCase__    )[-1].split("."    )[0]
								accelerator.init_trackers(lowerCamelCase__	, lowerCamelCase__    )

				# Now we train the model
				for epoch in range(lowerCamelCase__    ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												lowerCamelCase_						=			0
								for step, batch in enumerate(lowerCamelCase__    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												lowerCamelCase_						=			model(**lowerCamelCase__    )
												lowerCamelCase_						=			outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowerCamelCase_						=			loss / gradient_accumulation_steps
												accelerator.backward(lowerCamelCase__    )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(lowerCamelCase__    ):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device    )
												with torch.no_grad():
																lowerCamelCase_						=			model(**lowerCamelCase__    )
												lowerCamelCase_						=			outputs.logits.argmax(dim=-1    )
												lowerCamelCase_	,    lowerCamelCase_						=			accelerator.gather_for_metrics((predictions, batch["labels"])    )
												metric.add_batch(
												    predictions=lowerCamelCase__	, references=lowerCamelCase__	, )

								lowerCamelCase_						=			metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'epoch {epoch}:'	, lowerCamelCase__    )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": eval_metric["accuracy"],
												        "f1": eval_metric["f1"],
												        "train_loss": total_loss.item() / len(lowerCamelCase__    ),
												        "epoch": epoch,
												    }	, step=lowerCamelCase__	, )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			argparse.ArgumentParser(description="Simple example of training script."    )
				parser.add_argument(
				    "--mixed_precision"	, type=lowerCamelCase__	, default=lowerCamelCase__	, 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("--cpu"	, action="store_true"	, help="If passed, will train on the CPU."    )
				parser.add_argument(
				    "--with_tracking"	, action="store_true"	, help="Whether to load in all available experiment trackers from the environment and use them for logging."	, )
				parser.add_argument(
				    "--project_dir"	, type=lowerCamelCase__	, default="logs"	, help="Location on where to store experiment tracking logs` and relevent project information"	, )
				lowerCamelCase_						=			parser.parse_args()
				lowerCamelCase_						=			{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
				training_function(lowerCamelCase__	, lowerCamelCase__    )


if __name__ == "__main__":
			main()


