






import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin

def 						snake_case      (UpperCAmelCase__=2			,					UpperCAmelCase__=True			,					UpperCAmelCase__=False						) ->       Optional[int]:
  UpperCamelCase_:      Any					   =       Accelerator(step_scheduler_with_optimizer=UpperCAmelCase__			,					split_batches=UpperCAmelCase__						)
  UpperCamelCase_:      Dict					   =       torch.nn.Linear(2			,					4						)
  UpperCamelCase_:      Optional[Any]					   =       torch.optim.AdamW(model.parameters()			,					lr=1.0						)
  UpperCamelCase_:      List[Any]					   =       torch.optim.lr_scheduler.OneCycleLR(UpperCAmelCase__			,					max_lr=0.01			,					steps_per_epoch=2			,					epochs=1						)
  UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      Dict					   =       accelerator.prepare(UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						)

  # Optimizer has stepped
  scheduler.step()
  if step_scheduler_with_optimizer or (num_processes == 1):
    assert (
        scheduler.scheduler.last_epoch == num_processes
    ), F'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
  else:
    assert (
        scheduler.scheduler.last_epoch != num_processes
    ), F'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''

def 						snake_case      (UpperCAmelCase__=2			,					UpperCAmelCase__=True			,					UpperCAmelCase__=False						) ->       Dict:
  UpperCamelCase_:      str					   =       Accelerator(step_scheduler_with_optimizer=UpperCAmelCase__			,					split_batches=UpperCAmelCase__						)
  UpperCamelCase_:      Optional[int]					   =       torch.nn.Linear(2			,					4						)
  UpperCamelCase_:      Any					   =       torch.optim.AdamW(model.parameters()			,					lr=1.0						)
  UpperCamelCase_:      List[Any]					   =       torch.optim.lr_scheduler.LambdaLR(UpperCAmelCase__			,					lr_lambda=lambda UpperCAmelCase__						: 1 - n / 1_0						)
  UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      str					   =       accelerator.prepare(UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						)

  # Optimizer has stepped
  UpperCamelCase_:      List[str]					   =       False
  scheduler.step()
  UpperCamelCase_:      Tuple					   =       1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), F'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

  # Optimizer has not stepped
  UpperCamelCase_:      Dict					   =       True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    UpperCamelCase_:      List[Any]					   =       1 - 2 / 1_0
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), F'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

def 						snake_case      (UpperCAmelCase__ = 2						) ->       Tuple:
  from transformers import get_linear_schedule_with_warmup

  UpperCamelCase_:      List[Any]					   =       [1, 2, 4]
  for num_steps in steps:
    UpperCamelCase_:      Dict					   =       GradientAccumulationPlugin(num_steps=UpperCAmelCase__			,					adjust_scheduler=num_steps > 1						)
    UpperCamelCase_:      Optional[Any]					   =       Accelerator(gradient_accumulation_plugin=UpperCAmelCase__						)
    UpperCamelCase_:      str					   =       torch.nn.Linear(2			,					4						)
    UpperCamelCase_:      Optional[int]					   =       torch.optim.AdamW(model.parameters()			,					lr=10.0						)
    UpperCamelCase_:      List[Any]					   =       get_linear_schedule_with_warmup(optimizer=UpperCAmelCase__			,					num_warmup_steps=0			,					num_training_steps=2_0						)

    UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      Any					   =       accelerator.prepare(UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						)

    for i in range(1_0 * num_steps						):
      with accelerator.accumulate(UpperCAmelCase__						):
        optimizer.step()
        scheduler.step()

      if i == (1_0 * num_steps - 2):
        assert (
            scheduler.get_last_lr()[0] != 0
        ), F'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
    assert (
        scheduler.get_last_lr()[0] == 0
    ), F'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
    GradientState._reset_state()




@require_cpu
class        _lowerCAmelCase( unittest.TestCase      ):





       """simple docstring"""







       def     _a					(      self							):
         debug_launcher(partial(_lowerCamelCase						,    num_processes=1							)						,    num_processes=1							)
         debug_launcher(partial(_lowerCamelCase						,    num_processes=1						,    split_batches=_lowerCamelCase							)						,    num_processes=1							)





       def     _a					(      self							):
         debug_launcher(partial(_lowerCamelCase						,    num_processes=1							)						,    num_processes=1							)
         debug_launcher(partial(_lowerCamelCase						,    num_processes=1						,    split_batches=_lowerCamelCase							)						,    num_processes=1							)





       def     _a					(      self							):
         debug_launcher(partial(_lowerCamelCase						,    num_processes=1						,    step_scheduler_with_optimizer=_lowerCamelCase							)						,    num_processes=1							)





       def     _a					(      self							):
         debug_launcher(partial(_lowerCamelCase						,    num_processes=1						,    step_scheduler_with_optimizer=_lowerCamelCase							)						,    num_processes=1							)





       def     _a					(      self							):
         AcceleratorState._reset_state(_lowerCamelCase							)
         debug_launcher(_lowerCamelCase							)
         debug_launcher(partial(_lowerCamelCase						,    num_processes=1						,    split_batches=_lowerCamelCase							)						,    num_processes=1							)





       def     _a					(      self							):
         AcceleratorState._reset_state(_lowerCamelCase							)
         debug_launcher(_lowerCamelCase							)
         debug_launcher(partial(_lowerCamelCase						,    num_processes=1						,    split_batches=_lowerCamelCase							)						,    num_processes=1							)





       def     _a					(      self							):
         AcceleratorState._reset_state(_lowerCamelCase							)
         debug_launcher(partial(_lowerCamelCase						,    step_scheduler_with_optimizer=_lowerCamelCase							)							)





       def     _a					(      self							):
         AcceleratorState._reset_state(_lowerCamelCase							)
         debug_launcher(partial(_lowerCamelCase						,    step_scheduler_with_optimizer=_lowerCamelCase							)							)





       @require_huggingface_suite
       def     _a					(      self							):
         AcceleratorState._reset_state(_lowerCamelCase							)
         debug_launcher(partial(_lowerCamelCase						,    num_processes=1							)							)
         debug_launcher(_lowerCamelCase							)







from datetime import date

import requests

A_	:	Any				=				'https://api.carbonintensity.org.uk/intensity'

def 						snake_case      () ->       str:
  UpperCamelCase_:      Any					   =       requests.get(UpperCAmelCase__						).json()['data'][0]
  return last_half_hour["intensity"]["actual"]

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       list:
  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() = }''')