



'''simple docstring'''






import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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_							: Any						 =	1_6
A_							: Optional[int]						 =	3_2




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = 16		)->		Dict:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = AutoTokenizer.from_pretrained("""bert-base-cased"""		)
       _UpperCAmelCase  :						Tuple			     = DatasetDict(
           {
               """train""": dataset["""train"""].select(lowerCAmelCase_		),
               """validation""": dataset["""train"""].select(lowerCAmelCase_		),
               """test""": dataset["""validation"""],
           }		)

       def tokenize_function(lowerCAmelCase_		):
              # max_length=None => use the model max length (it's actually the default)
              _UpperCAmelCase  :						int			     = 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():
              _UpperCAmelCase  :						Optional[int]			     = 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
       _UpperCAmelCase  :						Tuple			     = 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.
              _UpperCAmelCase  :						str			     = 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":
                     _UpperCAmelCase  :						List[Any]			     = 16
              elif accelerator.mixed_precision != "no":
                     _UpperCAmelCase  :						Optional[int]			     = 8
              else:
                     _UpperCAmelCase  :						Union[str, Any]			     = None

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

       # Instantiate dataloaders.
       _UpperCAmelCase  :						int			     = DataLoader(
           tokenized_datasets["""train"""]       ,   shuffle=lowerCAmelCase_       ,   collate_fn=lowerCAmelCase_       ,   batch_size=lowerCAmelCase_		)
       _UpperCAmelCase  :						int			     = DataLoader(
           tokenized_datasets["""validation"""]       ,   shuffle=lowerCAmelCase_       ,   collate_fn=lowerCAmelCase_       ,   batch_size=lowerCAmelCase_		)

       _UpperCAmelCase  :						List[Any]			     = DataLoader(
           tokenized_datasets["""test"""]       ,   shuffle=lowerCAmelCase_       ,   collate_fn=lowerCAmelCase_       ,   batch_size=lowerCAmelCase_		)

       return train_dataloader, eval_dataloader, test_dataloader




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		List[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Tuple			     = []
       # Download the dataset
       _UpperCAmelCase  :						Dict			     = load_dataset("""glue"""       ,   """mrpc"""		)
       # Create our splits
       _UpperCAmelCase  :						List[str]			     = StratifiedKFold(n_splits=int(args.num_folds		)		)
       # Initialize accelerator
       _UpperCAmelCase  :						Tuple			     = Accelerator(cpu=args.cpu       ,   mixed_precision=args.mixed_precision		)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _UpperCAmelCase  :						List[str]			     = config["""lr"""]
       _UpperCAmelCase  :						Optional[int]			     = int(config["""num_epochs"""]		)
       _UpperCAmelCase  :						Union[str, Any]			     = int(config["""seed"""]		)
       _UpperCAmelCase  :						Any			     = int(config["""batch_size"""]		)

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

       # If the batch size is too big we use gradient accumulation
       _UpperCAmelCase  :						Optional[int]			     = 1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              _UpperCAmelCase  :						Dict			     = batch_size // MAX_GPU_BATCH_SIZE
              _UpperCAmelCase  :						Dict			     = MAX_GPU_BATCH_SIZE

       set_seed(lowerCAmelCase_		)

       # New Code #
       # Create our folds:
       _UpperCAmelCase  :						Optional[int]			     = kfold.split(np.zeros(datasets["""train"""].num_rows		)       ,   datasets["""train"""]["""label"""]		)
       _UpperCAmelCase  :						str			     = []
       # Iterate over them
       for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase_		):
              _UpperCAmelCase  :						Any			     = get_fold_dataloaders(
                  lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   )
              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              _UpperCAmelCase  :						Any			     = 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).
              _UpperCAmelCase  :						str			     = model.to(accelerator.device		)

              # Instantiate optimizer
              _UpperCAmelCase  :						int			     = AdamW(params=model.parameters()       ,   lr=lowerCAmelCase_		)

              # Instantiate scheduler
              _UpperCAmelCase  :						str			     = get_linear_schedule_with_warmup(
                  optimizer=lowerCAmelCase_       ,   num_warmup_steps=100       ,   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.
              _UpperCAmelCase  :						str			     = 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		)
                            _UpperCAmelCase  :						List[Any]			     = model(**lowerCAmelCase_		)
                            _UpperCAmelCase  :						Dict			     = outputs.loss
                            _UpperCAmelCase  :						str			     = 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`.
                            batch.to(accelerator.device		)
                            with torch.no_grad():
                                   _UpperCAmelCase  :						str			     = model(**lowerCAmelCase_		)
                            _UpperCAmelCase  :						Dict			     = outputs.logits.argmax(dim=-1		)
                            _UpperCAmelCase  :						int			     = accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
                            metric.add_batch(
                                predictions=lowerCAmelCase_       ,   references=lowerCAmelCase_       ,   )

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

              # New Code #
              # We also run predictions on the test set at the very end
              _UpperCAmelCase  :						List[Any]			     = []
              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():
                            _UpperCAmelCase  :						int			     = model(**lowerCAmelCase_		)
                     _UpperCAmelCase  :						Tuple			     = outputs.logits
                     _UpperCAmelCase  :						Union[str, Any]			     = accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
                     fold_predictions.append(predictions.cpu()		)
                     if i == 0:
                            # We need all of the test predictions
                            test_references.append(references.cpu()		)
        # Use accelerator.print to print only on the main process.
              test_predictions.append(torch.cat(lowerCAmelCase_       ,   dim=0		)		)
              # We now need to release all our memory and get rid of the current model, optimizer, etc
              accelerator.free_memory()
       # New Code #
       # Finally we check the accuracy of our folded results:
       _UpperCAmelCase  :						List[str]			     = torch.cat(lowerCAmelCase_       ,   dim=0		)
       _UpperCAmelCase  :						Optional[int]			     = torch.stack(lowerCAmelCase_       ,   dim=0		).sum(dim=0		).div(int(args.num_folds		)		).argmax(dim=-1		)
       _UpperCAmelCase  :						Any			     = metric.compute(predictions=lowerCAmelCase_       ,   references=lowerCAmelCase_		)
       accelerator.print("""Average test metrics from all folds:"""       ,   lowerCAmelCase_		)




def     snake_case_					(     )->		List[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Tuple			     = 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."""		)
       # New Code #
       parser.add_argument("""--num_folds"""       ,   type=lowerCAmelCase_       ,   default=3       ,   help="""The number of splits to perform across the dataset"""		)
       _UpperCAmelCase  :						int			     = parser.parse_args()
       _UpperCAmelCase  :						Tuple			     = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(lowerCAmelCase_       ,   lowerCAmelCase_		)


if __name__ == "__main__":
   main()











from __future__ import annotations

import math

lowerCamelCase       :       List[Any]        =				'''2020.9.26'''
lowerCamelCase       :       str        =				'''xcodz-dot, cclaus, dhruvmanila'''
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float					):
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in locals().values()					):
														__lowercase					:							str			=   F"Input values must either be float or int: {list(locals().values()					)}"
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							List[Any]			=   ((x * distance) / (z + distance)) * scale
							__lowercase					:							Tuple			=   ((y * distance) / (z + distance)) * scale
							return projected_x, projected_y
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  float					):
							if not isinstance(lowerCAmelCase_				,				lowerCAmelCase_					):
														raise TypeError("""Axis must be a str"""					)
							__lowercase					:							Optional[int]			=   locals()
							del input_variables["axis"]
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in input_variables.values()					):
														__lowercase					:							List[str]			=   (
														    """Input values except axis must either be float or int: """
														    F"{list(input_variables.values()					)}"
														)
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							Tuple			=   (angle % 360) / 450 * 180 / math.pi
							if axis == "z":
														__lowercase					:							int			=   x * math.cos(lowerCAmelCase_					) - y * math.sin(lowerCAmelCase_					)
														__lowercase					:							Tuple			=   y * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							Union[str, Any]			=   z
							elif axis == "x":
														__lowercase					:							str			=   y * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							Dict			=   z * math.cos(lowerCAmelCase_					) + y * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   x
							elif axis == "y":
														__lowercase					:							List[str]			=   x * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   z * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[Any]			=   y
							else:
														raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'"""					)

							return new_x, new_y, new_z


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
	print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')