


import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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
#
########################################################################

__UpperCAmelCase			:			Any			      =					32


def   a						(				SCREAMING_SNAKE_CASE_   :		List[str]				,      SCREAMING_SNAKE_CASE_   :		Tuple = 1_6							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		AutoTokenizer.from_pretrained('''bert-base-cased'''							)
       UpperCamelCase     :						Union[str, Any] 						=		load_dataset('''glue'''				,      '''mrpc'''							)

       def tokenize_function(SCREAMING_SNAKE_CASE_   :		Optional[Any]							):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase     :						Any 						=		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
       # starting with the main process first:
       with accelerator.main_process_first():
              UpperCamelCase     :						str 						=		datasets.map(
                  SCREAMING_SNAKE_CASE_				,      batched=SCREAMING_SNAKE_CASE_				,      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     :						List[Any] 						=		tokenized_datasets.rename_column('''label'''				,      '''labels'''							)

       def collate_fn(SCREAMING_SNAKE_CASE_   :		str							):
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCamelCase     :						Optional[int] 						=		1_6
              elif accelerator.mixed_precision != "no":
                     UpperCamelCase     :						Any 						=		8
              else:
                     UpperCamelCase     :						Any 						=		None

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

       # Instantiate dataloaders.
       UpperCamelCase     :						Dict 						=		DataLoader(
           tokenized_datasets['''train''']				,      shuffle=SCREAMING_SNAKE_CASE_				,      collate_fn=SCREAMING_SNAKE_CASE_				,      batch_size=SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						Union[str, Any] 						=		DataLoader(
           tokenized_datasets['''validation''']				,      shuffle=SCREAMING_SNAKE_CASE_				,      collate_fn=SCREAMING_SNAKE_CASE_				,      batch_size=SCREAMING_SNAKE_CASE_							)

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  __UpperCAmelCase			:			Optional[Any]			      =					mocked_dataloaders  # noqa: F811


def   a						(				SCREAMING_SNAKE_CASE_   :		Tuple				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]							):






       """simple docstring"""





       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,      SCREAMING_SNAKE_CASE_							) == "1":
              UpperCamelCase     :						Union[str, Any] 						=		2
       # Initialize accelerator
       UpperCamelCase     :						int 						=		Accelerator(cpu=args.cpu				,      mixed_precision=args.mixed_precision							)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCamelCase     :						int 						=		config["""lr"""]
       UpperCamelCase     :						Optional[Any] 						=		int(config['''num_epochs''']							)
       UpperCamelCase     :						str 						=		int(config['''seed''']							)
       UpperCamelCase     :						str 						=		int(config['''batch_size''']							)

       UpperCamelCase     :						Union[str, Any] 						=		evaluate.load('''glue'''				,      '''mrpc'''							)

       # New Code #
       # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
       # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
       # half each time. From this, we can calculate the number of gradient accumulation steps needed
       # and modify the Accelerator object as a result
       @find_executable_batch_size(starting_batch_size=int(SCREAMING_SNAKE_CASE_							)							)
       def inner_training_loop(SCREAMING_SNAKE_CASE_   :		List[str]							):
              # Since we need to modify the outside accelerator object, we need to bring it
              # to the local scope
              nonlocal accelerator

              # We can calculate the number of gradient accumulation steps based on the current
              # batch size vs the starting batch size
              UpperCamelCase     :						Tuple 						=		observed_batch_size // batch_size

              # And then set it in the Accelerator directly:
              UpperCamelCase     :						Any 						=		num_gradient_accumulation_steps

              # Next we need to free all of the stored model references in the Accelerator each time
              accelerator.free_memory()

              # And set the seed so our results are reproducable each reset
              set_seed(SCREAMING_SNAKE_CASE_							)

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              UpperCamelCase     :						str 						=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,      return_dict=SCREAMING_SNAKE_CASE_							)

              # 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     :						List[Any] 						=		model.to(accelerator.device							)

              # Instantiate optimizer
              UpperCamelCase     :						List[Any] 						=		AdamW(params=model.parameters()				,      lr=SCREAMING_SNAKE_CASE_							)
              UpperCamelCase     :						List[str] 						=		get_dataloaders(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)

              # Instantiate scheduler
              UpperCamelCase     :						Any 						=		get_linear_schedule_with_warmup(
                  optimizer=SCREAMING_SNAKE_CASE_				,      num_warmup_steps=1_0_0				,      num_training_steps=(len(SCREAMING_SNAKE_CASE_							) * num_epochs)				,      )

              # 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     :						List[Any] 						=		accelerator.prepare(
                  SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)

              # Now we train the model
              for epoch in range(SCREAMING_SNAKE_CASE_							):
                     model.train()
                     for step, batch in enumerate(SCREAMING_SNAKE_CASE_							):
                            # And perform gradient accumulation
                            with accelerator.accumulate(SCREAMING_SNAKE_CASE_							):
                                   # We could avoid this line since we set the accelerator with `device_placement=True`.
                                   batch.to(accelerator.device							)
                                   UpperCamelCase     :						int 						=		model(**SCREAMING_SNAKE_CASE_							)
                                   UpperCamelCase     :						Optional[int] 						=		outputs.loss
                                   accelerator.backward(SCREAMING_SNAKE_CASE_							)
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                     model.eval()
                     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():
                                   UpperCamelCase     :						Optional[Any] 						=		model(**SCREAMING_SNAKE_CASE_							)
                            UpperCamelCase     :						Tuple 						=		outputs.logits.argmax(dim=-1							)
                            UpperCamelCase     :						Dict 						=		accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
                            metric.add_batch(
                                predictions=SCREAMING_SNAKE_CASE_				,      references=SCREAMING_SNAKE_CASE_				,      )

                     UpperCamelCase     :						Dict 						=		metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(F"""epoch {epoch}:"""				,      SCREAMING_SNAKE_CASE_							)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
       inner_training_loop()


def   a						(				):






       """simple docstring"""





       UpperCamelCase     :						Optional[int] 						=		argparse.ArgumentParser(description='''Simple example of training script.'''							)
       parser.add_argument(
           '''--mixed_precision'''				,      type=SCREAMING_SNAKE_CASE_				,      default=SCREAMING_SNAKE_CASE_				,      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.'''							)
       UpperCamelCase     :						Optional[Any] 						=		parser.parse_args()
       # New Code #
       # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
       UpperCamelCase     :						str 						=		{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 2_5_6}
       training_function(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
  main()









from datetime import date

import requests

a 			=							'https://api.carbonintensity.org.uk/intensity'
def    a__    (	):
   lowercase__    :						Union[str, Any]       =			requests.get(A       ).json()["""data"""][0]
   return last_half_hour["intensity"]["actual"]
def    a__    (	A		,					A       ):
   return requests.get(F'''{BASE_URL}/{start}/{end}'''       ).json()["data"]


if __name__ == "__main__":
 for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
  print('from {from} to {to}: {intensity[actual]}'.format(**entry))
 print(F'{fetch_last_half_hour() = }')


