



from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						for param, grad_param in zip(model_a.parameters()							,     model_b.parameters()       ):
												if not param.requires_grad:
																		continue
												if not did_step:
																		# Grads should not be in sync
																		assert (
																		    torch.allclose(param.grad							,     grad_param.grad       ) is False
																		), F'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
												else:
																		# Grads should be in sync
																		assert (
																		    torch.allclose(param.grad							,     grad_param.grad       ) is True
																		), F'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase=True       )		->		Optional[int]:







						"""simple docstring"""

						model.train()
						A__	:							Union[str, Any]						    = model(lowerCAmelCase__       )
						A__	:							List[Any]						    = F.mse_loss(lowerCAmelCase__							,     target.to(output.device       )       )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(lowerCAmelCase__       )
def 	snake_case__       (					__lowercase							,     __lowercase=False       )		->		Optional[int]:







						"""simple docstring"""

						set_seed(4_2       )
						A__	:							Union[str, Any]						    = RegressionModel()
						A__	:							Optional[Any]						    = deepcopy(lowerCAmelCase__       )
						A__	:							Tuple						    = RegressionDataset(length=8_0       )
						A__	:							str						    = DataLoader(lowerCAmelCase__							,     batch_size=1_6       )
						model.to(accelerator.device       )
						if sched:
												A__	:							Dict						    = AdamW(params=model.parameters()							,     lr=1E-3       )
												A__	:							Union[str, Any]						    = AdamW(params=ddp_model.parameters()							,     lr=1E-3       )
												A__	:							List[str]						    = LambdaLR(lowerCAmelCase__							,     lr_lambda=lambda __lowercase       : epoch**0.65       )
												A__	:							Tuple						    = LambdaLR(lowerCAmelCase__							,     lr_lambda=lambda __lowercase       : epoch**0.65       )
						# Make a copy of `model`
						if sched:
												A__	:							Optional[int]						    = accelerator.prepare(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )
						else:
												A__	:							List[str]						    = accelerator.prepare(lowerCAmelCase__							,     lowerCAmelCase__       )
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader
def 	snake_case__       (					__lowercase       )		->		List[str]:







						"""simple docstring"""

						A__	:							Optional[Any]						    = get_training_setup(lowerCAmelCase__       )
						# Use a single batch
						A__	:							Optional[Any]						    = next(iter(lowerCAmelCase__       )       ).values()
						for iteration in range(3       ):
												# Gather the distributed inputs and targs for the base model
												A__	:							int						    = accelerator.gather((ddp_input, ddp_target)       )
												A__	:							Any						    = input.to(accelerator.device       ), target.to(accelerator.device       )
												# Perform our initial ground truth step in non "DDP"
												step_model(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(lowerCAmelCase__       ):
																								step_model(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )
												else:
																		# Sync grads
																		step_model(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )

												# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
												check_model_parameters(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )
												for param, ddp_param in zip(model.parameters()							,     ddp_model.parameters()       ):
																		if not param.requires_grad:
																								continue
																		assert torch.allclose(
																		    param.grad							,     ddp_param.grad       ), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

												# Shuffle ddp_input on each iteration
												torch.manual_seed(1_3_3_7 + iteration       )
												A__	:							Optional[Any]						    = ddp_input[torch.randperm(len(lowerCAmelCase__       )       )]
def 	snake_case__       (					__lowercase       )		->		Dict:







						"""simple docstring"""

						A__	:							str						    = get_training_setup(lowerCAmelCase__       )
						# Use a single batch
						A__	:							Dict						    = next(iter(lowerCAmelCase__       )       ).values()
						for iteration in range(3       ):
												# Gather the distributed inputs and targs for the base model
												A__	:							Union[str, Any]						    = accelerator.gather((ddp_input, ddp_target)       )
												A__	:							int						    = input.to(accelerator.device       ), target.to(accelerator.device       )
												# Perform our initial ground truth step in non "DDP"
												step_model(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(lowerCAmelCase__       ):
																								step_model(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )
												else:
																		# Sync grads
																		step_model(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()							,     ddp_model.parameters()       ):
																		if not param.requires_grad:
																								continue
																		if iteration % 2 == 0:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad							,     ddp_param.grad       ) is False
																								), F'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
																		else:
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad							,     ddp_param.grad       ) is True
																								), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
												torch.manual_seed(1_3_3_7 + iteration       )
												A__	:							Optional[Any]						    = ddp_input[torch.randperm(len(lowerCAmelCase__       )       )]
def 	snake_case__       (					__lowercase=False							,     __lowercase=False       )		->		Dict:







						"""simple docstring"""

						A__	:							Optional[int]						    = Accelerator(
						    split_batches=lowerCAmelCase__							,     dispatch_batches=lowerCAmelCase__							,     gradient_accumulation_steps=2       )
						# Test that context manager behaves properly
						A__	:							Union[str, Any]						    = get_training_setup(lowerCAmelCase__       )
						for iteration, batch in enumerate(lowerCAmelCase__       ):
												A__	:							Any						    = batch.values()
												# Gather the distributed inputs and targs for the base model
												A__	:							int						    = accelerator.gather((ddp_input, ddp_target)       )
												A__	:							int						    = input.to(accelerator.device       ), target.to(accelerator.device       )
												# Perform our initial ground truth step in non "DDP"
												step_model(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )
												# Do "gradient accumulation" (noop)
												with accelerator.accumulate(lowerCAmelCase__       ):
																		step_model(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()							,     ddp_model.parameters()       ):
																		if not param.requires_grad:
																								continue
																		if ((iteration + 1) % 2 == 0) or (iteration == len(lowerCAmelCase__       ) - 1):
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad							,     ddp_param.grad       ) is True
																								), F'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
																		else:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad							,     ddp_param.grad       ) is False
																								), F'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
												torch.manual_seed(1_3_3_7 + iteration       )
												A__	:							str						    = ddp_input[torch.randperm(len(lowerCAmelCase__       )       )]
						GradientState._reset_state()
def 	snake_case__       (					__lowercase=False							,     __lowercase=False       )		->		Tuple:







						"""simple docstring"""

						A__	:							str						    = Accelerator(
						    split_batches=lowerCAmelCase__							,     dispatch_batches=lowerCAmelCase__							,     gradient_accumulation_steps=2       )
						# Test that context manager behaves properly
						A__	:							Optional[Any]						    = get_training_setup(lowerCAmelCase__							,     lowerCAmelCase__       )
						for iteration, batch in enumerate(lowerCAmelCase__       ):
												A__	:							List[Any]						    = batch.values()
												# Gather the distributed inputs and targs for the base model
												A__	:							Union[str, Any]						    = accelerator.gather((ddp_input, ddp_target)       )
												A__	:							Tuple						    = input.to(accelerator.device       ), target.to(accelerator.device       )
												# Perform our initial ground truth step in non "DDP"
												model.train()
												ddp_model.train()
												step_model(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )
												opt.step()

												if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCAmelCase__       )):
																		if split_batches:
																								sched.step()
																		else:
																								for _ in range(accelerator.num_processes       ):
																														sched.step()
												opt.zero_grad()
												# Perform gradient accumulation under wrapper
												with accelerator.accumulate(lowerCAmelCase__       ):
																		step_model(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )
																		ddp_opt.step()
																		ddp_sched.step()
																		ddp_opt.zero_grad()

												# Learning rates should be the same
												assert (
												    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
												), F'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
												A__	:							Optional[int]						    = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCAmelCase__       ))
												if accelerator.num_processes > 1:
																		check_model_parameters(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )
												# Shuffle ddp_input on each iteration
												torch.manual_seed(1_3_3_7 + iteration       )
						GradientState._reset_state()
def 	snake_case__       (					)		->		str:







						"""simple docstring"""

						A__	:							Optional[int]						    = Accelerator()

						A__	:							int						    = RegressionDataset(length=8_0       )
						A__	:							str						    = DataLoader(lowerCAmelCase__							,     batch_size=1_6       )
						A__	:							Dict						    = RegressionDataset(length=9_6       )
						A__	:							Tuple						    = DataLoader(lowerCAmelCase__							,     batch_size=1_6       )
						A__	:							Dict						    = accelerator.prepare(lowerCAmelCase__							,     lowerCAmelCase__       )
						assert accelerator.gradient_state.active_dataloader is None
						for iteration, _ in enumerate(lowerCAmelCase__       ):
												assert id(accelerator.gradient_state.active_dataloader       ) == id(lowerCAmelCase__       )
												if iteration < len(lowerCAmelCase__       ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
																		if iteration == 1:
																								for batch_num, _ in enumerate(lowerCAmelCase__       ):
																														assert id(accelerator.gradient_state.active_dataloader       ) == id(lowerCAmelCase__       )
																														if batch_num < len(lowerCAmelCase__       ) - 1:
																																				assert not accelerator.gradient_state.end_of_dataloader
																														else:
																																				assert accelerator.gradient_state.end_of_dataloader
												else:
																		assert accelerator.gradient_state.end_of_dataloader
						assert accelerator.gradient_state.active_dataloader is None
def 	snake_case__       (					)		->		str:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = Accelerator()
						A__	:							str						    = accelerator.state
						if state.local_process_index == 0:
												print("**Test `accumulate` gradient accumulation with dataloader break**"       )
						test_dataloader_break()
						if state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print("**Test NOOP `no_sync` context manager**"       )
												test_noop_sync(lowerCAmelCase__       )
						if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
												if state.local_process_index == 0:
																		print("**Test Distributed `no_sync` context manager**"       )
												test_distributed_sync(lowerCAmelCase__       )
						if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																		for dispatch_batches in [True, False]:
																								if state.local_process_index == 0:
																														print(
																														    "**Test `accumulate` gradient accumulation, "							,     F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'							,     )
																								test_gradient_accumulation(lowerCAmelCase__							,     lowerCAmelCase__       )

    # Currently will break on torch 2.0 +, need to investigate why
						if is_torch_version("<"							,     "2.0"       ) or state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print(
																		    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "							,     "`split_batches=False`, `dispatch_batches=False`**"							,     )
												test_gradient_accumulation_with_opt_and_scheduler()
												if state.distributed_type == DistributedType.MULTI_GPU:
																		for split_batch in [True, False]:
																								for dispatch_batches in [True, False]:
																														if not split_batch and not dispatch_batches:
																																				continue
																														if state.local_process_index == 0:
																																				print(
																																				    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "							,     F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'							,     )
																														test_gradient_accumulation_with_opt_and_scheduler(lowerCAmelCase__							,     lowerCAmelCase__       )
def 	snake_case__       (					__lowercase       )		->		Optional[int]:







						"""simple docstring"""

						main()


if __name__ == "__main__":
		main()







import requests

snake_case    :						int  =						''  # <-- Put your OpenWeatherMap appid here!
snake_case    :						int  =						'https://api.openweathermap.org/data/2.5/'
def 	snake_case__       (					__lowercase = "Chicago"							,     __lowercase = APPID       )		->		dict:







						"""simple docstring"""

						return requests.get(URL_BASE + "weather"							,     params=locals()       ).json()
def 	snake_case__       (					__lowercase = "Kolkata, India"							,     __lowercase = APPID       )		->		dict:







						"""simple docstring"""

						return requests.get(URL_BASE + "forecast"							,     params=locals()       ).json()
def 	snake_case__       (					__lowercase = 55.68							,     __lowercase = 12.57							,     __lowercase = APPID       )		->		dict:







						"""simple docstring"""

						return requests.get(URL_BASE + "onecall"							,     params=locals()       ).json()


if __name__ == "__main__":
		from pprint import pprint

		while True:
				snake_case    :						int  =						input('Enter a location:').strip()
				if location:
						pprint(current_weather(location))
				else:
						break