'''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"			)    ->      Union[str, 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_       : str			):
          # 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_       : Union[str, Any]					,    snake_case_       : Optional[int]					,    snake_case_       : Union[str, Any]			)    ->      Union[str, Any]:


     '''simple docstring'''




     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()
     return eval_metric["accuracy"]





def 	lowerCAmelCase_				(					snake_case_       : Tuple					,    snake_case_       : Optional[int]			)    ->      Optional[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
     UpperCAmelCase_           =   evaluate.load("glue"					,    "mrpc"			)
     UpperCAmelCase_           =   num_epochs

     if args.partial_train_epoch is not None:
          UpperCAmelCase_           =   args.partial_train_epoch

     if args.resume_from_checkpoint:
          accelerator.load_state(args.resume_from_checkpoint			)
          UpperCAmelCase_           =   args.resume_from_checkpoint.split("epoch_"			)[1]
          UpperCAmelCase_           =   ""
          for char in epoch_string:
               if char.isdigit():
                    state_epoch_num += char
               else:
                    break
          UpperCAmelCase_           =   int(snake_case_			) + 1
          UpperCAmelCase_           =   evaluation_loop(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
          accelerator.print("resumed checkpoint performance:"					,    snake_case_			)
          accelerator.print("resumed checkpoint's scheduler's lr:"					,    lr_scheduler.get_lr()[0]			)
          accelerator.print("resumed optimizers's lr:"					,    optimizer.param_groups[0]["lr"]			)
          with open(os.path.join(args.output_dir					,    f"""state_{starting_epoch-1}.json"""			)					,    "r"			) as f:
               UpperCAmelCase_           =   json.load(snake_case_			)
               assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["lr"] == lr_scheduler.get_lr()[0]
               ), "Scheduler learning rate mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
               ), "Optimizer learning rate mismatch, loading from checkpoint failed"
               assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
               return

    # Now we train the model
     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
          UpperCAmelCase_           =   f"""epoch_{epoch}"""
          UpperCAmelCase_           =   os.path.join(args.output_dir					,    snake_case_			)
          accelerator.save_state(snake_case_			)
          UpperCAmelCase_           =   evaluation_loop(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
          UpperCAmelCase_           =   accuracy
          UpperCAmelCase_           =   lr_scheduler.get_lr()[0]
          UpperCAmelCase_           =   optimizer.param_groups[0]["lr"]
          UpperCAmelCase_           =   epoch
          UpperCAmelCase_           =   overall_step
          accelerator.print(f"""epoch {epoch}:"""					,    snake_case_			)

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





def 	lowerCAmelCase_				(					)    ->      List[str]:


     '''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(
         "--resume_from_checkpoint"					,    type=snake_case_					,    default=snake_case_					,    help="If the training should continue from a checkpoint folder."					,    )
     parser.add_argument(
         "--partial_train_epoch"					,    type=snake_case_					,    default=snake_case_					,    help="If passed, the training will stop after this number of epochs."					,    )
     parser.add_argument(
         "--num_epochs"					,    type=snake_case_					,    default=2					,    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'''





from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE_:     Optional[int]  =3_00  # TEMPERATURE (unit = K)





def 	lowerCAmelCase_				(					snake_case_       : float					,    snake_case_       : float					,    snake_case_       : float					,    )    ->      float:


     '''simple docstring'''




     if donor_conc <= 0:
          raise ValueError("Donor concentration should be positive"			)
     elif acceptor_conc <= 0:
          raise ValueError("Acceptor concentration should be positive"			)
     elif intrinsic_conc <= 0:
          raise ValueError("Intrinsic concentration should be positive"			)
     elif donor_conc <= intrinsic_conc:
          raise ValueError(
              "Donor concentration should be greater than intrinsic concentration"			)
     elif acceptor_conc <= intrinsic_conc:
          raise ValueError(
              "Acceptor concentration should be greater than intrinsic concentration"			)
     else:
          return (
              Boltzmann
              * T
              * log((donor_conc * acceptor_conc) / intrinsic_conc**2			)
              / physical_constants["electron volt"][0]
          )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




