'''simple docstring'''





import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


SCREAMING_SNAKE_CASE_:	Tuple    =16
SCREAMING_SNAKE_CASE_:	Tuple    =32





def       lowerCAmelCase_							(   snake_case_			:					Accelerator   ,       snake_case_			:					int = 16   ,       snake_case_			:					str = "bert-base-cased"     )       -> Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  AutoTokenizer.from_pretrained(snake_case_     )
     UpperCAmelCase_			       =  load_dataset("glue"   ,       "mrpc"     )

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

     # Apply the method we just defined to all the examples in all the splits of the dataset
     UpperCAmelCase_			       =  datasets.map(
         snake_case_   ,       batched=snake_case_   ,       remove_columns=["idx", "sentence1", "sentence2"]   ,       load_from_cache_file=snake_case_     )

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

     def collate_fn(snake_case_			:					Union[str, Any]     ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(snake_case_   ,       padding="max_length"   ,       max_length=1_28   ,       return_tensors="pt"     )
          return tokenizer.pad(snake_case_   ,       padding="longest"   ,       return_tensors="pt"     )

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

     return train_dataloader, eval_dataloader





def       lowerCAmelCase_							(   snake_case_			:					int   ,       snake_case_			:					str     )       -> Union[str, Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCAmelCase_			       =  config["lr"]
     UpperCAmelCase_			       =  int(config["num_epochs"]     )
     UpperCAmelCase_			       =  int(config["seed"]     )
     UpperCAmelCase_			       =  int(config["batch_size"]     )
     UpperCAmelCase_			       =  args.model_name_or_path

     set_seed(snake_case_     )
     UpperCAmelCase_       ,					UpperCAmelCase_			       =  get_dataloaders(snake_case_   ,       snake_case_   ,       snake_case_     )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCAmelCase_			       =  AutoModelForSequenceClassification.from_pretrained(snake_case_   ,       return_dict=snake_case_     )

     # Instantiate optimizer
     UpperCAmelCase_			       =  (
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     UpperCAmelCase_			       =  optimizer_cls(params=model.parameters()   ,       lr=snake_case_     )

     if accelerator.state.deepspeed_plugin is not None:
          UpperCAmelCase_			       =  accelerator.state.deepspeed_plugin.deepspeed_config[
              "gradient_accumulation_steps"
          ]
     else:
          UpperCAmelCase_			       =  1
     UpperCAmelCase_			       =  (len(snake_case_     ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          UpperCAmelCase_			       =  get_linear_schedule_with_warmup(
              optimizer=snake_case_   ,       num_warmup_steps=0   ,       num_training_steps=snake_case_   ,       )
     else:
          UpperCAmelCase_			       =  DummyScheduler(snake_case_   ,       total_num_steps=snake_case_   ,       warmup_num_steps=0     )

     # 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_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  accelerator.prepare(
         snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_     )

     # We need to keep track of how many total steps we have iterated over
     UpperCAmelCase_			       =  0
     # We also need to keep track of the stating epoch so files are named properly
     UpperCAmelCase_			       =  0

     # Now we train the model
     UpperCAmelCase_			       =  evaluate.load("glue"   ,       "mrpc"     )
     UpperCAmelCase_			       =  0
     UpperCAmelCase_			       =  {}
     for epoch in range(snake_case_   ,       snake_case_     ):
          model.train()
          for step, batch in enumerate(snake_case_     ):
               UpperCAmelCase_			       =  model(**snake_case_     )
               UpperCAmelCase_			       =  outputs.loss
               UpperCAmelCase_			       =  loss / gradient_accumulation_steps
               accelerator.backward(snake_case_     )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          UpperCAmelCase_			       =  0
          for step, batch in enumerate(snake_case_     ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device     )
               with torch.no_grad():
                    UpperCAmelCase_			       =  model(**snake_case_     )
               UpperCAmelCase_			       =  outputs.logits.argmax(dim=-1     )
               # It is slightly faster to call this once, than multiple times
               UpperCAmelCase_       ,					UpperCAmelCase_			       =  accelerator.gather(
                   (predictions, batch["labels"])     )  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(snake_case_     ) - 1:
                         UpperCAmelCase_			       =  predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                         UpperCAmelCase_			       =  references[: len(eval_dataloader.dataset     ) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=snake_case_   ,       references=snake_case_   ,       )

          UpperCAmelCase_			       =  metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"""epoch {epoch}:"""   ,       snake_case_     )
          UpperCAmelCase_			       =  eval_metric["accuracy"]

          if best_performance < eval_metric["accuracy"]:
               UpperCAmelCase_			       =  eval_metric["accuracy"]

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir   ,       "all_results.json"     )   ,       "w"     ) as f:
               json.dump(snake_case_   ,       snake_case_     )





def       lowerCAmelCase_							(   )       -> Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."     )
     parser.add_argument(
         "--model_name_or_path"   ,       type=snake_case_   ,       default="bert-base-cased"   ,       help="Path to pretrained model or model identifier from huggingface.co/models."   ,       required=snake_case_   ,       )
     parser.add_argument(
         "--output_dir"   ,       type=snake_case_   ,       default="."   ,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."   ,       )
     parser.add_argument(
         "--performance_lower_bound"   ,       type=snake_case_   ,       default=snake_case_   ,       help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."   ,       )
     parser.add_argument(
         "--num_epochs"   ,       type=snake_case_   ,       default=3   ,       help="Number of train epochs."   ,       )
     UpperCAmelCase_			       =  parser.parse_args()
     UpperCAmelCase_			       =  {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
     training_function(snake_case_   ,       snake_case_     )


if __name__ == "__main__":
    main()








'''simple docstring'''


import unittest

import numpy as np


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = None       ,					):




     '''simple docstring'''





     __A	:       List[Any]      =							np.shape(SCREAMING_SNAKE_CASE							)
     __A	:       List[str]      =							np.shape(SCREAMING_SNAKE_CASE							)
     __A	:       Dict      =							np.shape(SCREAMING_SNAKE_CASE							)

     if shape_a[0] != shape_b[0]:
          __A	:       Tuple      =							(
              "Expected the same number of rows for A and B. "
              f"Instead found A of size {shape_a} and B of size {shape_b}"
          )
          raise ValueError(SCREAMING_SNAKE_CASE							)

     if shape_b[1] != shape_c[1]:
          __A	:       Optional[Any]      =							(
              "Expected the same number of columns for B and C. "
              f"Instead found B of size {shape_b} and C of size {shape_c}"
          )
          raise ValueError(SCREAMING_SNAKE_CASE							)

     __A	:       Tuple      =							pseudo_inv
     if a_inv is None:
          try:
               __A	:       Optional[Any]      =							np.linalg.inv(SCREAMING_SNAKE_CASE							)
          except np.linalg.LinAlgError:
               raise ValueError(
                   "Input matrix A is not invertible. Cannot compute Schur complement."							)

     return mat_c - mat_b.T @ a_inv @ mat_b







class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       Tuple      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       Tuple      =							np.array([[2, 1], [6, 3]]		)

          __A	:       Optional[Any]      =							schur_complement(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)

          __A	:       int      =							np.block([[a, b], [b.T, c]]		)

          __A	:       int      =							np.linalg.det(lowerCamelCase		)
          __A	:       List[str]      =							np.linalg.det(lowerCamelCase		)
          __A	:       Any      =							np.linalg.det(lowerCamelCase		)

          self.assertAlmostEqual(lowerCamelCase			,     det_a * det_s		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       List[Any]      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       List[str]      =							np.array([[2, 1], [6, 3]]		)

          with self.assertRaises(lowerCamelCase		):
               schur_complement(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       Union[str, Any]      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       Optional[Any]      =							np.array([[2, 1, 3], [6, 3, 5]]		)

          with self.assertRaises(lowerCamelCase		):
               schur_complement(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()

