






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    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     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    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case=True							):
     model.train()
     __lowerCAmelCase             =       model(__snake_case							)
     __lowerCAmelCase             =       F.mse_loss(__snake_case       ,			target.to(output.device							)							)
     if not do_backward:
          loss /= accelerator.gradient_accumulation_steps
          loss.backward()
     else:
          accelerator.backward(__snake_case							)





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case=False							):
     set_seed(42							)
     __lowerCAmelCase             =       RegressionModel()
     __lowerCAmelCase             =       deepcopy(__snake_case							)
     __lowerCAmelCase             =       RegressionDataset(length=80							)
     __lowerCAmelCase             =       DataLoader(__snake_case       ,			batch_size=16							)
     model.to(accelerator.device							)
     if sched:
          __lowerCAmelCase             =       AdamW(params=model.parameters()       ,			lr=1E-3							)
          __lowerCAmelCase             =       AdamW(params=ddp_model.parameters()       ,			lr=1E-3							)
          __lowerCAmelCase             =       LambdaLR(__snake_case       ,			lr_lambda=lambda __snake_case							: epoch**0.65							)
          __lowerCAmelCase             =       LambdaLR(__snake_case       ,			lr_lambda=lambda __snake_case							: epoch**0.65							)
     # Make a copy of `model`
     if sched:
          __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       accelerator.prepare(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
     else:
          __lowerCAmelCase      ,			__lowerCAmelCase             =       accelerator.prepare(__snake_case       ,			__snake_case							)
     if sched:
          return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
     return model, ddp_model, dataloader





def    __lowerCAmelCase   (    __snake_case							):
     # Test when on a single CPU or GPU that the context manager does nothing
     __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       get_training_setup(__snake_case							)
     # Use a single batch
     __lowerCAmelCase      ,			__lowerCAmelCase             =       next(iter(__snake_case							)							).values()
     for iteration in range(3							):
          # Gather the distributed inputs and targs for the base model
          __lowerCAmelCase      ,			__lowerCAmelCase             =       accelerator.gather((ddp_input, ddp_target)							)
          __lowerCAmelCase      ,			__lowerCAmelCase             =       input.to(accelerator.device							), target.to(accelerator.device							)
          # Perform our initial ground truth step in non "DDP"
          step_model(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
          # Do "gradient accumulation" (noop)
          if iteration % 2 == 0:
               # Accumulate grads locally
               with accelerator.no_sync(__snake_case							):
                    step_model(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
          else:
               # Sync grads
               step_model(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)

          # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
          check_model_parameters(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
          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(1337 + iteration							)
          __lowerCAmelCase             =       ddp_input[torch.randperm(len(__snake_case							)							)]





def    __lowerCAmelCase   (    __snake_case							):
     # Test on distributed setup that context manager behaves properly
     __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       get_training_setup(__snake_case							)
     # Use a single batch
     __lowerCAmelCase      ,			__lowerCAmelCase             =       next(iter(__snake_case							)							).values()
     for iteration in range(3							):
          # Gather the distributed inputs and targs for the base model
          __lowerCAmelCase      ,			__lowerCAmelCase             =       accelerator.gather((ddp_input, ddp_target)							)
          __lowerCAmelCase      ,			__lowerCAmelCase             =       input.to(accelerator.device							), target.to(accelerator.device							)
          # Perform our initial ground truth step in non "DDP"
          step_model(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
          # Do "gradient accumulation" (noop)
          if iteration % 2 == 0:
               # Accumulate grads locally
               with accelerator.no_sync(__snake_case							):
                    step_model(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
          else:
               # Sync grads
               step_model(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)

          # 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(1337 + iteration							)
          __lowerCAmelCase             =       ddp_input[torch.randperm(len(__snake_case							)							)]





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

          # 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(__snake_case							) - 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(1337 + iteration							)
          __lowerCAmelCase             =       ddp_input[torch.randperm(len(__snake_case							)							)]
     GradientState._reset_state()





def    __lowerCAmelCase   (    __snake_case=False       ,			__snake_case=False							):
     __lowerCAmelCase             =       Accelerator(
         split_batches=__snake_case       ,			dispatch_batches=__snake_case       ,			gradient_accumulation_steps=2							)
     # Test that context manager behaves properly
     __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       get_training_setup(__snake_case       ,			__snake_case							)
     for iteration, batch in enumerate(__snake_case							):
          __lowerCAmelCase      ,			__lowerCAmelCase             =       batch.values()
          # Gather the distributed inputs and targs for the base model
          __lowerCAmelCase      ,			__lowerCAmelCase             =       accelerator.gather((ddp_input, ddp_target)							)
          __lowerCAmelCase      ,			__lowerCAmelCase             =       input.to(accelerator.device							), target.to(accelerator.device							)
          # Perform our initial ground truth step in non "DDP"
          model.train()
          ddp_model.train()
          step_model(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
          opt.step()

          if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__snake_case							)):
               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(__snake_case							):
               step_model(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
               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"""
          __lowerCAmelCase             =       (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__snake_case							))
          if accelerator.num_processes > 1:
               check_model_parameters(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
          # Shuffle ddp_input on each iteration
          torch.manual_seed(1337 + iteration							)
     GradientState._reset_state()





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       Accelerator()

     __lowerCAmelCase             =       RegressionDataset(length=80							)
     __lowerCAmelCase             =       DataLoader(__snake_case       ,			batch_size=16							)
     __lowerCAmelCase             =       RegressionDataset(length=96							)
     __lowerCAmelCase             =       DataLoader(__snake_case       ,			batch_size=16							)
     __lowerCAmelCase      ,			__lowerCAmelCase             =       accelerator.prepare(__snake_case       ,			__snake_case							)
     assert accelerator.gradient_state.active_dataloader is None
     for iteration, _ in enumerate(__snake_case							):
          assert id(accelerator.gradient_state.active_dataloader							) == id(__snake_case							)
          if iteration < len(__snake_case							) - 1:
               assert not accelerator.gradient_state.end_of_dataloader
               if iteration == 1:
                    for batch_num, _ in enumerate(__snake_case							):
                         assert id(accelerator.gradient_state.active_dataloader							) == id(__snake_case							)
                         if batch_num < len(__snake_case							) - 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    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       Accelerator()
     __lowerCAmelCase             =       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(__snake_case							)
     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(__snake_case							)
     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(__snake_case       ,			__snake_case							)

    # 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(__snake_case       ,			__snake_case							)





def    __lowerCAmelCase   (    __snake_case							):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()














from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup





def    __lowerCAmelCase   (    __snake_case = ""							):
     __lowerCAmelCase             =       url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
     __lowerCAmelCase             =       BeautifulSoup(requests.get(__snake_case							).text       ,			"html.parser"							)
     __lowerCAmelCase             =       soup.find_all("td"       ,			attrs="titleColumn"							)
     __lowerCAmelCase             =       soup.find_all("td"       ,			class_="ratingColumn imdbRating"							)
     return {
         title.a.text: float(rating.strong.text							)
         for title, rating in zip(__snake_case       ,			__snake_case							)
     }





def    __lowerCAmelCase   (    __snake_case = "IMDb_Top_250_Movies.csv"							):
     __lowerCAmelCase             =       get_imdb_top_aaa_movies()
     with open(__snake_case       ,			"w"       ,			newline=""							) as out_file:
          __lowerCAmelCase             =       csv.writer(__snake_case							)
          writer.writerow(["Movie title", "IMDb rating"]							)
          for title, rating in movies.items():
               writer.writerow([title, rating]							)


if __name__ == "__main__":
 write_movies()






