


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


UpperCAmelCase    =					16
UpperCAmelCase    =					32






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = 16      ,      SCREAMING_SNAKE_CASE = "bert-base-cased"      )->						List[str]:




      """simple docstring"""

      snake_case_ =   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE      )
      snake_case_ =   load_dataset('''glue'''      ,      '''mrpc'''      )

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

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

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

      def collate_fn(SCREAMING_SNAKE_CASE      ):
            # 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(SCREAMING_SNAKE_CASE      ,      padding='''max_length'''      ,      max_length=128      ,      return_tensors='''pt'''      )
            return tokenizer.pad(SCREAMING_SNAKE_CASE      ,      padding='''longest'''      ,      return_tensors='''pt'''      )

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

      return train_dataloader, eval_dataloader






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

      snake_case_ =   Accelerator()

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

      set_seed(SCREAMING_SNAKE_CASE      )
      snake_case_ ,					snake_case_ =   get_dataloaders(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

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

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

      if accelerator.state.deepspeed_plugin is not None:
            snake_case_ =   accelerator.state.deepspeed_plugin.deepspeed_config[
                '''gradient_accumulation_steps'''
            ]
      else:
            snake_case_ =   1
      snake_case_ =   (len(SCREAMING_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
      ):
            snake_case_ =   get_linear_schedule_with_warmup(
                optimizer=SCREAMING_SNAKE_CASE      ,      num_warmup_steps=0      ,      num_training_steps=SCREAMING_SNAKE_CASE      ,      )
      else:
            snake_case_ =   DummyScheduler(SCREAMING_SNAKE_CASE      ,      total_num_steps=SCREAMING_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.
      snake_case_ ,					snake_case_ ,					snake_case_ ,					snake_case_ ,					snake_case_ =   accelerator.prepare(
          SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

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

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

                  overall_step += 1

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

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

            if best_performance < eval_metric["accuracy"]:
                  snake_case_ =   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(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					()->						Union[str, Any]:




      """simple docstring"""

      snake_case_ =   argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''      )
      parser.add_argument(
          '''--model_name_or_path'''      ,      type=SCREAMING_SNAKE_CASE      ,      default='''bert-base-cased'''      ,      help='''Path to pretrained model or model identifier from huggingface.co/models.'''      ,      required=SCREAMING_SNAKE_CASE      ,      )
      parser.add_argument(
          '''--output_dir'''      ,      type=SCREAMING_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=SCREAMING_SNAKE_CASE      ,      default=SCREAMING_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=SCREAMING_SNAKE_CASE      ,      default=3      ,      help='''Number of train epochs.'''      ,      )
      snake_case_ =   parser.parse_args()
      snake_case_ =   {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
      training_function(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   main()



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase    =					2_9979_2458

# Symbols
UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase    =					symbols("""ct x y z""")






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      if velocity > c:
            raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''      )
      elif velocity < 1:
            # Usually the speed should be much higher than 1 (c order of magnitude)
            raise ValueError('''Speed must be greater than or equal to 1!'''      )

      return velocity / c






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE      ) ** 2      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						np.ndarray:




      """simple docstring"""

      return np.array(
          [
              [gamma(SCREAMING_SNAKE_CASE      ), -gamma(SCREAMING_SNAKE_CASE      ) * beta(SCREAMING_SNAKE_CASE      ), 0, 0],
              [-gamma(SCREAMING_SNAKE_CASE      ) * beta(SCREAMING_SNAKE_CASE      ), gamma(SCREAMING_SNAKE_CASE      ), 0, 0],
              [0, 0, 1, 0],
              [0, 0, 0, 1],
          ]      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = None      )->						np.ndarray:




      """simple docstring"""

      if event is None:
            snake_case_ =   np.array([ct, x, y, z]      )  # Symbolic four vector
      else:
            event[0] *= c  # x0 is ct (speed of light * time)

      return transformation_matrix(SCREAMING_SNAKE_CASE      ) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   UpperCAmelCase    =					transform(2997_9245)
   print("""Example of four vector: """)
   print(f'''ct\' = {four_vector[0]}''')
   print(f'''x\' = {four_vector[1]}''')
   print(f'''y\' = {four_vector[2]}''')
   print(f'''z\' = {four_vector[3]}''')

   # Substitute symbols with numerical values
   UpperCAmelCase    =					{ct: c, x: 1, y: 1, z: 1}
   UpperCAmelCase    =					[four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')