






import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


__A      =				16
__A      =				32




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase = 16 ):

					'''simple docstring'''







					_A								= AutoTokenizer.from_pretrained('''bert-base-cased''' )
					_A								= DatasetDict(
					    {
					        '''train''': dataset['''train'''].select(_lowercase ),
					        '''validation''': dataset['''train'''].select(_lowercase ),
					        '''test''': dataset['''validation'''],
					    } )

					def tokenize_function(_lowercase ):
										# max_length=None => use the model max length (it's actually the default)
										_A								= tokenizer(examples['''sentence1''']					,			examples['''sentence2''']					,			truncation=_lowercase					,			max_length=_lowercase )
										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():
										_A								= datasets.map(
										    _lowercase					,			batched=_lowercase					,			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
					_A								= tokenized_datasets.rename_column('''label'''					,			'''labels''' )

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

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

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

					_A								= DataLoader(
					    tokenized_datasets['''test''']					,			shuffle=_lowercase					,			collate_fn=_lowercase					,			batch_size=_lowercase )

					return train_dataloader, eval_dataloader, test_dataloader




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= []
					# Download the dataset
					_A								= load_dataset('''glue'''					,			'''mrpc''' )
					# Create our splits
					_A								= StratifiedKFold(n_splits=int(args.num_folds ) )
					# Initialize accelerator
					_A								= Accelerator(cpu=args.cpu					,			mixed_precision=args.mixed_precision )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_A								= config['''lr''']
					_A								= int(config['''num_epochs'''] )
					_A								= int(config['''seed'''] )
					_A								= int(config['''batch_size'''] )

					_A								= evaluate.load('''glue'''					,			'''mrpc''' )

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

					set_seed(_lowercase )

					# New Code #
					# Create our folds:
					_A								= kfold.split(np.zeros(datasets['''train'''].num_rows )					,			datasets['''train''']['''label'''] )
					_A								= []
					# Iterate over them
					for i, (train_idxs, valid_idxs) in enumerate(_lowercase ):
										_A							,_A							,_A								= get_fold_dataloaders(
										    _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			)
										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										_A								= AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''					,			return_dict=_lowercase )

										# 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).
										_A								= model.to(accelerator.device )

										# Instantiate optimizer
										_A								= AdamW(params=model.parameters()					,			lr=_lowercase )

										# Instantiate scheduler
										_A								= get_linear_schedule_with_warmup(
										    optimizer=_lowercase					,			num_warmup_steps=1_00					,			num_training_steps=(len(_lowercase ) * 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.
										_A							,_A							,_A							,_A							,_A								= accelerator.prepare(
										    _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase )

										# Now we train the model
										for epoch in range(_lowercase ):
															model.train()
															for step, batch in enumerate(_lowercase ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device )
																				_A								= model(**_lowercase )
																				_A								= outputs.loss
																				_A								= loss / gradient_accumulation_steps
																				accelerator.backward(_lowercase )
																				if step % gradient_accumulation_steps == 0:
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

															model.eval()
															for step, batch in enumerate(_lowercase ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device )
																				with torch.no_grad():
																									_A								= model(**_lowercase )
																				_A								= outputs.logits.argmax(dim=-1 )
																				_A							,_A								= accelerator.gather_for_metrics((predictions, batch['''labels''']) )
																				metric.add_batch(
																				    predictions=_lowercase					,			references=_lowercase					,			)

															_A								= metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(f"""epoch {epoch}:"""					,			_lowercase )

										# New Code #
										# We also run predictions on the test set at the very end
										_A								= []
										for step, batch in enumerate(_lowercase ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device )
															with torch.no_grad():
																				_A								= model(**_lowercase )
															_A								= outputs.logits
															_A							,_A								= accelerator.gather_for_metrics((predictions, batch['''labels''']) )
															fold_predictions.append(predictions.cpu() )
															if i == 0:
																				# We need all of the test predictions
																				test_references.append(references.cpu() )
        # Use accelerator.print to print only on the main process.
										test_predictions.append(torch.cat(_lowercase					,			dim=0 ) )
										# We now need to release all our memory and get rid of the current model, optimizer, etc
										accelerator.free_memory()
					# New Code #
					# Finally we check the accuracy of our folded results:
					_A								= torch.cat(_lowercase					,			dim=0 )
					_A								= torch.stack(_lowercase					,			dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
					_A								= metric.compute(predictions=_lowercase					,			references=_lowercase )
					accelerator.print('''Average test metrics from all folds:'''					,			_lowercase )




def      __A					(     ):

					'''simple docstring'''







					_A								= argparse.ArgumentParser(description='''Simple example of training script.''' )
					parser.add_argument(
					    '''--mixed_precision'''					,			type=_lowercase					,			default=_lowercase					,			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.''' )
					# New Code #
					parser.add_argument('''--num_folds'''					,			type=_lowercase					,			default=3					,			help='''The number of splits to perform across the dataset''' )
					_A								= parser.parse_args()
					_A								= {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(_lowercase					,			_lowercase )


if __name__ == "__main__":
				main()











from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def      __A					(     _lowercase = "" ):

					'''simple docstring'''







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




def      __A					(     _lowercase = "IMDb_Top_250_Movies.csv" ):

					'''simple docstring'''







					_A								= get_imdb_top_aaa_movies()
					with open(_lowercase					,			'''w'''					,			newline='''''' ) as out_file:
										_A								= csv.writer(_lowercase )
										writer.writerow(['''Movie title''', '''IMDb rating'''] )
										for title, rating in movies.items():
															writer.writerow([title, rating] )


if __name__ == "__main__":
				write_movies()



