


import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


_snake_case					:		Optional[Any]      =    16
_snake_case					:		List[Any]      =    32



def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase = 1_6     ):
							__snake_case		:      str										=       AutoTokenizer.from_pretrained("bert-base-cased"     )
							__snake_case		:      Optional[Any]										=       load_dataset("glue"							,     "mrpc"     )

							def tokenize_function(__lowerCamelCase     ):
														# max_length=None => use the model max length (it's actually the default)
														__snake_case		:      List[Any]										=       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():
														__snake_case		:      Union[str, Any]										=       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
							__snake_case		:      List[Any]										=       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.
														__snake_case		:      Optional[Any]										=       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":
																					__snake_case		:      Optional[int]										=       1_6
														elif accelerator.mixed_precision != "no":
																					__snake_case		:      Any										=       8
														else:
																					__snake_case		:      Union[str, Any]										=       None

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

							# Instantiate dataloaders.
							__snake_case		:      List[str]										=       DataLoader(
							    tokenized_datasets["train"]							,     shuffle=__lowerCamelCase							,     collate_fn=__lowerCamelCase							,     batch_size=__lowerCamelCase     )
							__snake_case		:      str										=       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

			_snake_case					:		Any      =    mocked_dataloaders  # noqa: F811



def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):



							if os.environ.get("TESTING_MOCKED_DATALOADERS"							,     __lowerCamelCase     ) == "1":
														__snake_case		:      Optional[int]										=       2
							# Initialize accelerator
							__snake_case		:      Optional[int]										=       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		:      Optional[Any]										=       config['''lr''']
							__snake_case		:      int										=       int(config["num_epochs"]     )
							__snake_case		:      Any										=       int(config["seed"]     )
							__snake_case		:      str										=       int(config["batch_size"]     )

							__snake_case		:      Dict										=       evaluate.load("glue"							,     "mrpc"     )

							# New Code #
							# We now can define an inner training loop function. It should take a batch size as the only parameter,
							# and build the dataloaders in there.
							# It also gets our decorator
							@find_executable_batch_size(starting_batch_size=__lowerCamelCase     )
							def inner_training_loop(__lowerCamelCase     ):
														# And now just move everything below under this function
														# We need to bring in the Accelerator object from earlier
														nonlocal accelerator
														# And reset all of its attributes that could hold onto any memory:
														accelerator.free_memory()

														# Then we can declare the model, optimizer, and everything else:
														set_seed(__lowerCamelCase     )

														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														__snake_case		:      Optional[int]										=       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).
														__snake_case		:      List[str]										=       model.to(accelerator.device     )

														# Instantiate optimizer
														__snake_case		:      List[Any]										=       AdamW(params=model.parameters()							,     lr=__lowerCamelCase     )
														__snake_case		:      Dict										=       get_dataloaders(__lowerCamelCase							,     __lowerCamelCase     )

														# Instantiate scheduler
														__snake_case		:      Dict										=       get_linear_schedule_with_warmup(
														    optimizer=__lowerCamelCase							,     num_warmup_steps=1_0_0							,     num_training_steps=(len(__lowerCamelCase     ) * num_epochs)							,     )

														# 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		:      Optional[int]										=       accelerator.prepare(
														    __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )

														# Now we train the model
														for epoch in range(__lowerCamelCase     ):
																					model.train()
																					for step, batch in enumerate(__lowerCamelCase     ):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device     )
																												__snake_case		:      Tuple										=       model(**__lowerCamelCase     )
																												__snake_case		:      Dict										=       outputs.loss
																												accelerator.backward(__lowerCamelCase     )
																												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`.
																												batch.to(accelerator.device     )
																												with torch.no_grad():
																																			__snake_case		:      Optional[Any]										=       model(**__lowerCamelCase     )
																												__snake_case		:      Any										=       outputs.logits.argmax(dim=-1     )
																												__snake_case		:      Union[str, Any]										=       accelerator.gather_for_metrics((predictions, batch["labels"])     )
																												metric.add_batch(
																												    predictions=__lowerCamelCase							,     references=__lowerCamelCase							,     )

																					__snake_case		:      List[str]										=       metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(F'epoch {epoch}:'							,     __lowerCamelCase     )

																					 # New Code #
																					 # And call it at the end with no arguments
																					 # Note: You could also refactor this outside of your training loop function
							inner_training_loop()



def     lowerCAmelCase_					( ):
							__snake_case		:      Any										=       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."     )
							__snake_case		:      Optional[int]										=       parser.parse_args()
							__snake_case		:      Optional[int]										=       {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
							training_function(__lowerCamelCase							,     __lowerCamelCase     )


if __name__ == "__main__":
			main()





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     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Any										=       []
							__snake_case		:      Optional[Any]										=       []
							__snake_case		:      List[Any]										=       []

							for rt in rc.restypes:
														__snake_case		:      Any										=       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]     )
														__snake_case		:      Tuple										=       {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] * 1_4     )
							restype_atomaa_to_atomaa_list.append([0] * 3_7     )
							restype_atomaa_mask_list.append([0.0] * 1_4     )

							__snake_case		:      int										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      List[str]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[Any]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.floataa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[int]										=       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
							__snake_case		:      Optional[Any]										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Tuple										=       restype_atomaa_mask[protein_aatype]

							__snake_case		:      Optional[Any]										=       residx_atomaa_mask
							__snake_case		:      Union[str, Any]										=       residx_atomaa_to_atomaa.long()

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

							# create the corresponding mask
							__snake_case		:      List[str]										=       torch.zeros([2_1, 3_7]							,     dtype=torch.floataa							,     device=protein["aatype"].device     )
							for restype, restype_letter in enumerate(rc.restypes     ):
														__snake_case		:      List[str]										=       rc.restype_atoa[restype_letter]
														__snake_case		:      List[Any]										=       rc.residue_atoms[restype_name]
														for atom_name in atom_names:
																					__snake_case		:      Union[str, Any]										=       rc.atom_order[atom_name]
																					__snake_case		:      str										=       1

							__snake_case		:      List[str]										=       restype_atomaa_mask[protein_aatype]
							__snake_case		:      List[str]										=       residx_atomaa_mask

							return protein







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      str										=       tree_map(lambda __lowerCamelCase     : torch.tensor(__lowerCamelCase							,     device=batch["aatype"].device     )							,     __lowerCamelCase							,     np.ndarray     )
							__snake_case		:      str										=       tensor_tree_map(lambda __lowerCamelCase     : np.array(__lowerCamelCase     )							,     make_atomaa_masks(__lowerCamelCase     )     )
							return out

