




'''simple docstring'''



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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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_       :  List[str]      =		1_6
UpperCAmelCase_       :  Union[str, Any]      =		3_2







def 			_UpperCamelCase (_lowerCamelCase							:     str   ,	_lowerCamelCase							:     Tuple	)->   Optional[Any]:




     '''simple docstring'''





     if args.with_tracking:
          __snake_case    					=  Accelerator(
              cpu=args.cpu   ,	mixed_precision=args.mixed_precision   ,	log_with='''all'''   ,	project_dir=args.project_dir	)
     else:
          __snake_case    					=  Accelerator(cpu=args.cpu   ,	mixed_precision=args.mixed_precision	)

     if hasattr(args.checkpointing_steps   ,	'''isdigit'''	):
          if args.checkpointing_steps == "epoch":
               __snake_case    					=  args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               __snake_case    					=  int(args.checkpointing_steps	)
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
     else:
          __snake_case    					=  None
     # 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''']	)

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          __snake_case    					=  os.path.split(_lowerCamelCase	)[-1].split('''.'''	)[0]
          accelerator.init_trackers(_lowerCamelCase   ,	_lowerCamelCase	)

     __snake_case    					=  AutoTokenizer.from_pretrained('''bert-base-cased'''	)
     __snake_case    					=  load_dataset('''glue'''   ,	'''mrpc'''	)
     __snake_case    					=  evaluate.load('''glue'''   ,	'''mrpc'''	)

     def tokenize_function(_lowerCamelCase							:     Any	):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case    					=  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():
          __snake_case    					=  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
     __snake_case    					=  tokenized_datasets.rename_column('''label'''   ,	'''labels'''	)

     # If the batch size is too big we use gradient accumulation
     __snake_case    					=  1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          __snake_case    					=  batch_size // MAX_GPU_BATCH_SIZE
          __snake_case    					=  MAX_GPU_BATCH_SIZE

     def collate_fn(_lowerCamelCase							:     Dict	):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __snake_case    					=  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":
               __snake_case    					=  16
          elif accelerator.mixed_precision != "no":
               __snake_case    					=  8
          else:
               __snake_case    					=  None

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

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

     set_seed(_lowerCamelCase	)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __snake_case    					=  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).
     __snake_case    					=  model.to(accelerator.device	)

     # Instantiate optimizer
     __snake_case    					=  AdamW(params=model.parameters()   ,	lr=_lowerCamelCase	)

     # Instantiate scheduler
     __snake_case    					=  get_linear_schedule_with_warmup(
         optimizer=_lowerCamelCase   ,	num_warmup_steps=1_00   ,	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.
     __snake_case					,    __snake_case					,    __snake_case					,    __snake_case					,    __snake_case    					=  accelerator.prepare(
         _lowerCamelCase   ,	_lowerCamelCase   ,	_lowerCamelCase   ,	_lowerCamelCase   ,	_lowerCamelCase	)

     # 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

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''	)
               accelerator.load_state(args.resume_from_checkpoint	)
               __snake_case    					=  os.path.basename(args.resume_from_checkpoint	)
          else:
               # Get the most recent checkpoint
               __snake_case    					=  [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
               dirs.sort(key=os.path.getctime	)
               __snake_case    					=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          __snake_case    					=  os.path.splitext(_lowerCamelCase	)[0]

          if "epoch" in training_difference:
               __snake_case    					=  int(training_difference.replace('''epoch_'''   ,	''''''	)	) + 1
               __snake_case    					=  None
          else:
               __snake_case    					=  int(training_difference.replace('''step_'''   ,	''''''	)	)
               __snake_case    					=  resume_step // len(_lowerCamelCase	)
               resume_step -= starting_epoch * len(_lowerCamelCase	)

    # Now we train the model
     for epoch in range(_lowerCamelCase   ,	_lowerCamelCase	):
          model.train()
          if args.with_tracking:
               __snake_case    					=  0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               __snake_case    					=  accelerator.skip_first_batches(_lowerCamelCase   ,	_lowerCamelCase	)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               __snake_case    					=  train_dataloader
          for step, batch in enumerate(_lowerCamelCase	):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device	)
               __snake_case    					=  model(**_lowerCamelCase	)
               __snake_case    					=  outputs.loss
               __snake_case    					=  loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(_lowerCamelCase	)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(_lowerCamelCase   ,	_lowerCamelCase	):
                    __snake_case    					=  f'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              __snake_case    					=  os.path.join(args.output_dir   ,	_lowerCamelCase	)
                         accelerator.save_state(_lowerCamelCase	)

          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():
                    __snake_case    					=  model(**_lowerCamelCase	)
               __snake_case    					=  outputs.logits.argmax(dim=-1	)
               __snake_case					,    __snake_case    					=  accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
               metric.add_batch(
                   predictions=_lowerCamelCase   ,	references=_lowerCamelCase   ,	)

          __snake_case    					=  metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''   ,	_lowerCamelCase	)
          if args.with_tracking:
               accelerator.log(
                   {
                       '''accuracy''': eval_metric['''accuracy'''],
                       '''f1''': eval_metric['''f1'''],
                       '''train_loss''': total_loss.item() / len(_lowerCamelCase	),
                       '''epoch''': epoch,
                   }   ,	step=_lowerCamelCase   ,	)

          if checkpointing_steps == "epoch":
               __snake_case    					=  f'''epoch_{epoch}'''
               if args.output_dir is not None:
                    __snake_case    					=  os.path.join(args.output_dir   ,	_lowerCamelCase	)
               accelerator.save_state(_lowerCamelCase	)

     if args.with_tracking:
          accelerator.end_training()







def 			_UpperCamelCase ()->   Optional[Any]:




     '''simple docstring'''





     __snake_case    					=  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.'''	)
     parser.add_argument(
         '''--checkpointing_steps'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''   ,	)
     parser.add_argument(
         '''--resume_from_checkpoint'''   ,	type=_lowerCamelCase   ,	default=_lowerCamelCase   ,	help='''If the training should continue from a checkpoint folder.'''   ,	)
     parser.add_argument(
         '''--with_tracking'''   ,	action='''store_true'''   ,	help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''   ,	)
     parser.add_argument(
         '''--output_dir'''   ,	type=_lowerCamelCase   ,	default='''.'''   ,	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''   ,	)
     parser.add_argument(
         '''--project_dir'''   ,	type=_lowerCamelCase   ,	default='''logs'''   ,	help='''Location on where to store experiment tracking logs` and relevent project information'''   ,	)
     __snake_case    					=  parser.parse_args()
     __snake_case    					=  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_lowerCamelCase   ,	_lowerCamelCase	)


if __name__ == "__main__":
     main()












'''simple docstring'''



from datetime import date

import requests

UpperCAmelCase_       :  str      =		'''https://api.carbonintensity.org.uk/intensity'''







def 			_UpperCamelCase ()->   str:




     '''simple docstring'''





     __snake_case    					=  requests.get(_lowerCamelCase	).json()['''data'''][0]
     return last_half_hour["intensity"]["actual"]







def 			_UpperCamelCase (_lowerCamelCase							:     Dict   ,	_lowerCamelCase							:     List[Any]	)->   list:




     '''simple docstring'''





     return requests.get(f'''{BASE_URL}/{start}/{end}'''	).json()["data"]


if __name__ == "__main__":
     for entry in fetch_from_to(start=date(2_0_2_0, 1_0, 1), end=date(2_0_2_0, 1_0, 3)):
          print('''from {from} to {to}: {intensity[actual]}'''.format(**entry))
     print(F"""{fetch_last_half_hour() = }""")






