






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
#
########################################################################


_UpperCAmelCase      :		Union[str, Any] 					=     16
_UpperCAmelCase      :		List[Any] 					=     32





def 						lowerCAmelCase_		(lowercase__    :							Accelerator  ,       lowercase__    :							int = 16							)		->     Union[str, Any]:


				'''simple docstring'''




				lowerCAmelCase__         =    AutoTokenizer.from_pretrained('''bert-base-cased'''							)
				lowerCAmelCase__         =    load_dataset('''glue'''  ,       '''mrpc'''							)

				def tokenize_function(lowercase__    :							Optional[int]							):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase__         =    tokenizer(examples['''sentence1''']  ,       examples['''sentence2''']  ,       truncation=lowercase__  ,       max_length=lowercase__							)
								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(
								    lowercase__  ,       batched=lowercase__  ,       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(lowercase__    :							List[Any]							):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCAmelCase__         =    1_28 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__         =    16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase__         =    8
								else:
												lowerCAmelCase__         =    None

								return tokenizer.pad(
								    lowercase__  ,       padding='''longest'''  ,       max_length=lowercase__  ,       pad_to_multiple_of=lowercase__  ,       return_tensors='''pt'''  ,       )

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

				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

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





def 						lowerCAmelCase_		(lowercase__    :							List[str]  ,       lowercase__    :							str							)		->     Optional[Any]:


				'''simple docstring'''




				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,       lowercase__							) == "1":
								lowerCAmelCase__         =    2
				# New Code #
				lowerCAmelCase__         =    int(args.gradient_accumulation_steps							)
				# Initialize accelerator
				lowerCAmelCase__         =    Accelerator(
				    cpu=args.cpu  ,       mixed_precision=args.mixed_precision  ,       gradient_accumulation_steps=lowercase__							)
				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
				lowerCAmelCase__         =    config['''lr''']
				lowerCAmelCase__         =    int(config['''num_epochs''']							)
				lowerCAmelCase__         =    int(config['''seed''']							)
				lowerCAmelCase__         =    int(config['''batch_size''']							)

				lowerCAmelCase__         =    evaluate.load('''glue'''  ,       '''mrpc'''							)

				set_seed(lowercase__							)
				lowerCAmelCase__      ,		lowerCAmelCase__         =    get_dataloaders(lowercase__  ,       lowercase__							)
				# 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=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).
				lowerCAmelCase__         =    model.to(accelerator.device							)

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

				# Instantiate scheduler
				lowerCAmelCase__         =    get_linear_schedule_with_warmup(
				    optimizer=lowercase__  ,       num_warmup_steps=1_00  ,       num_training_steps=(len(lowercase__							) * 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.
				lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__      ,		lowerCAmelCase__         =    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`.
												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(lowercase__							):
																lowerCAmelCase__         =    model(**lowercase__							)
																lowerCAmelCase__         =    output.loss
																accelerator.backward(lowercase__							)
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

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





def 						lowerCAmelCase_		()		->     int:


				'''simple docstring'''




				lowerCAmelCase__         =    argparse.ArgumentParser(description='''Simple example of training script.'''							)
				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.'''  ,       )
				# New Code #
				parser.add_argument(
				    '''--gradient_accumulation_steps'''  ,       type=lowercase__  ,       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.'''							)
				lowerCAmelCase__         =    parser.parse_args()
				lowerCAmelCase__         =    {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase__  ,       lowercase__							)


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 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, torch.Tensor]:
 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(a					)}
  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(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				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 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, np.ndarray]:
 a						    = tree_map(lambda a					: torch.tensor(a      ,				device=batch['''aatype'''].device					)      ,				a      ,				np.ndarray					)
 a						    = tensor_tree_map(lambda a					: np.array(a					)      ,				make_atomaa_masks(a					)					)
 return out


