


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 					UpperCamelCase   (      __magic_name__	:				Dict[str, torch.Tensor]	)  ->    Dict[str, torch.Tensor]:
		"""simple docstring"""




		lowercase__	     =						[]
		lowercase__	     =						[]
		lowercase__	     =						[]

		for rt in rc.restypes:
				lowercase__	     =						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]	)
				lowercase__	     =						{name: i for i, name in enumerate(__magic_name__	)}
				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	)

		lowercase__	     =						torch.tensor(
		    __magic_name__       ,       dtype=torch.intaa       ,       device=protein["""aatype"""].device       ,       )
		lowercase__	     =						torch.tensor(
		    __magic_name__       ,       dtype=torch.intaa       ,       device=protein["""aatype"""].device       ,       )
		lowercase__	     =						torch.tensor(
		    __magic_name__       ,       dtype=torch.floataa       ,       device=protein["""aatype"""].device       ,       )
		lowercase__	     =						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
		lowercase__	     =						restype_atomaa_to_atomaa[protein_aatype]
		lowercase__	     =						restype_atomaa_mask[protein_aatype]

		lowercase__	     =						residx_atomaa_mask
		lowercase__	     =						residx_atomaa_to_atomaa.long()

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

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

		lowercase__	     =						restype_atomaa_mask[protein_aatype]
		lowercase__	     =						residx_atomaa_mask

		return protein







def 					UpperCamelCase   (      __magic_name__	:				Dict[str, torch.Tensor]	)  ->    Dict[str, np.ndarray]:
		"""simple docstring"""




		lowercase__	     =						tree_map(lambda __magic_name__	: torch.tensor(__magic_name__       ,       device=batch["""aatype"""].device	)       ,       __magic_name__       ,       np.ndarray	)
		lowercase__	     =						tensor_tree_map(lambda __magic_name__	: np.array(__magic_name__	)       ,       make_atomaa_masks(__magic_name__	)	)
		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
# and perform gradient accumulation
#
# 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 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			:						Optional[Any]        =  1_6
A			:						str        =  3_2






def 					UpperCamelCase   (      __magic_name__	:				Accelerator       ,       __magic_name__	:				int = 16	)  ->    List[str]:
		"""simple docstring"""




		lowercase__	     =						AutoTokenizer.from_pretrained("""bert-base-cased"""	)
		lowercase__	     =						load_dataset("""glue"""       ,       """mrpc"""	)

		def tokenize_function(__magic_name__	:				int	):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__	     =						tokenizer(examples["""sentence1"""]       ,       examples["""sentence2"""]       ,       truncation=__magic_name__       ,       max_length=__magic_name__	)
				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():
				lowercase__	     =						datasets.map(
				    __magic_name__       ,       batched=__magic_name__       ,       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
		lowercase__	     =						tokenized_datasets.rename_column("""label"""       ,       """labels"""	)

		def collate_fn(__magic_name__	:				Optional[int]	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowercase__	     =						128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowercase__	     =						16
				elif accelerator.mixed_precision != "no":
						lowercase__	     =						8
				else:
						lowercase__	     =						None

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

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

		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			:						Union[str, Any]        =  mocked_dataloaders  # noqa: F811






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				List[Any]	)  ->    Dict:
		"""simple docstring"""




		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,       __magic_name__	) == "1":
				lowercase__	     =						2
		# New Code #
		lowercase__	     =						int(args.gradient_accumulation_steps	)
		# Initialize accelerator
		lowercase__	     =						Accelerator(
		    cpu=args.cpu       ,       mixed_precision=args.mixed_precision       ,       gradient_accumulation_steps=__magic_name__	)
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__	     =						config["""lr"""]
		lowercase__	     =						int(config["""num_epochs"""]	)
		lowercase__	     =						int(config["""seed"""]	)
		lowercase__	     =						int(config["""batch_size"""]	)

		lowercase__	     =						evaluate.load("""glue"""       ,       """mrpc"""	)

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

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

		# Instantiate optimizer
		lowercase__	     =						AdamW(params=model.parameters()       ,       lr=__magic_name__	)

		# Instantiate scheduler
		lowercase__	     =						get_linear_schedule_with_warmup(
		    optimizer=__magic_name__       ,       num_warmup_steps=100       ,       num_training_steps=(len(__magic_name__	) * 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.
		lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__	     =						accelerator.prepare(
		    __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__	)

		# Now we train the model
		for epoch in range(__magic_name__	):
				model.train()
				for step, batch in enumerate(__magic_name__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						# New code #
						# We use the new `accumulate` context manager to perform gradient accumulation
						# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
						with accelerator.accumulate(__magic_name__	):
								lowercase__	     =						model(**__magic_name__	)
								lowercase__	     =						output.loss
								accelerator.backward(__magic_name__	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

				lowercase__	     =						metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''       ,       __magic_name__	)






def 					UpperCamelCase   (      )  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						argparse.ArgumentParser(description="""Simple example of training script."""	)
		parser.add_argument(
		    """--mixed_precision"""       ,       type=__magic_name__       ,       default=__magic_name__       ,       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."""       ,       )
		# New Code #
		parser.add_argument(
		    """--gradient_accumulation_steps"""       ,       type=__magic_name__       ,       default=1       ,       help="""The number of minibatches to be ran before gradients are accumulated."""       ,       )
		parser.add_argument("""--cpu"""       ,       action="""store_true"""       ,       help="""If passed, will train on the CPU."""	)
		lowercase__	     =						parser.parse_args()
		lowercase__	     =						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(__magic_name__       ,       __magic_name__	)


if __name__ == "__main__":
	main()

