

import argparse
import json
import os

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, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a__												=						16
a__												=						32



def   __UpperCAmelCase					(      __a :     Accelerator						,__a :     int = 16						,__a :     str = "bert-base-cased"      )     ->	Optional[int]:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						AutoTokenizer.from_pretrained(__a      )
					_a      :       Dict   =						load_dataset('''glue'''						,'''mrpc'''      )

					def tokenize_function(__a :     Optional[Any]      ):
										# max_length=None => use the model max length (it's actually the default)
										_a      :       List[Any]   =						tokenizer(examples['''sentence1''']						,examples['''sentence2''']						,truncation=__a						,max_length=__a      )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					_a      :       Any   =						datasets.map(
					    __a						,batched=__a						,remove_columns=['''idx''', '''sentence1''', '''sentence2''']						,load_from_cache_file=__a      )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					_a      :       Optional[Any]   =						tokenized_datasets.rename_column('''label'''						,'''labels'''      )

					def collate_fn(__a :     List[str]      ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(__a						,padding='''max_length'''						,max_length=128						,return_tensors='''pt'''      )
										return tokenizer.pad(__a						,padding='''longest'''						,return_tensors='''pt'''      )

					# Instantiate dataloaders.
					_a      :       str   =						DataLoader(
					    tokenized_datasets['''train''']						,shuffle=__a						,collate_fn=__a						,batch_size=__a      )
					_a      :       int   =						DataLoader(
					    tokenized_datasets['''validation''']						,shuffle=__a						,collate_fn=__a						,batch_size=__a      )

					return train_dataloader, eval_dataloader



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     Union[str, Any]						,__a :     Any						,__a :     Optional[int]      )     ->	Tuple:

					"""simple docstring"""

					model.eval()
					_a      :       List[Any]   =						0
					for step, batch in enumerate(__a      ):
										# We could avoid this line since we set the accelerator with `device_placement=True`.
										batch.to(accelerator.device      )
										with torch.no_grad():
															_a      :       Any   =						model(**__a      )
										_a      :       Optional[Any]   =						outputs.logits.argmax(dim=-1      )
										# It is slightly faster to call this once, than multiple times
										_a ,   _a      :       Any   =						accelerator.gather(
										    (predictions, batch['''labels'''])      )  # If we are in a multiprocess environment, the last batch has duplicates
										if accelerator.use_distributed:
															if step == len(__a      ) - 1:
																				_a      :       Optional[int]   =						predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																				_a      :       Dict   =						references[: len(eval_dataloader.dataset      ) - samples_seen]
															else:
																				samples_seen += references.shape[0]
										metric.add_batch(
										    predictions=__a						,references=__a						,)

					_a      :       List[str]   =						metric.compute()
					return eval_metric["accuracy"]



def   __UpperCAmelCase					(      __a :     int						,__a :     Any      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       int   =						Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_a      :       Any   =						config['''lr''']
					_a      :       List[Any]   =						int(config['''num_epochs''']      )
					_a      :       List[Any]   =						int(config['''seed''']      )
					_a      :       Tuple   =						int(config['''batch_size''']      )
					_a      :       int   =						args.model_name_or_path

					set_seed(__a      )
					_a ,   _a      :       Optional[int]   =						get_dataloaders(__a						,__a						,__a      )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					_a      :       Dict   =						AutoModelForSequenceClassification.from_pretrained(__a						,return_dict=__a      )

					# Instantiate optimizer
					_a      :       Any   =						(
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					_a      :       Any   =						optimizer_cls(params=model.parameters()						,lr=__a      )

					if accelerator.state.deepspeed_plugin is not None:
										_a      :       List[Any]   =						accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										_a      :       Dict   =						1
					_a      :       Optional[int]   =						(len(__a      ) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										_a      :       int   =						get_linear_schedule_with_warmup(
										    optimizer=__a						,num_warmup_steps=0						,num_training_steps=__a						,)
					else:
										_a      :       List[str]   =						DummyScheduler(__a						,total_num_steps=__a						,warmup_num_steps=0      )

					# 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      :       str   =						accelerator.prepare(
					    __a						,__a						,__a						,__a						,__a      )

					# We need to keep track of how many total steps we have iterated over
					_a      :       Any   =						0
					# We also need to keep track of the stating epoch so files are named properly
					_a      :       Tuple   =						0
					_a      :       int   =						evaluate.load('''glue'''						,'''mrpc'''      )
					_a      :       Dict   =						num_epochs

					if args.partial_train_epoch is not None:
										_a      :       Union[str, Any]   =						args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint      )
										_a      :       List[str]   =						args.resume_from_checkpoint.split('''epoch_'''      )[1]
										_a      :       Tuple   =						''''''
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										_a      :       Optional[Any]   =						int(__a      ) + 1
										_a      :       List[Any]   =						evaluation_loop(__a						,__a						,__a						,__a      )
										accelerator.print('''resumed checkpoint performance:'''						,__a      )
										accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''						,lr_scheduler.get_lr()[0]      )
										accelerator.print('''resumed optimizers\'s lr:'''						,optimizer.param_groups[0]['''lr''']      )
										with open(os.path.join(args.output_dir						,F"""state_{starting_epoch-1}.json"""      )						,'''r'''      ) as f:
															_a      :       int   =						json.load(__a      )
															assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["lr"] == lr_scheduler.get_lr()[0]
															), "Scheduler learning rate mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
															), "Optimizer learning rate mismatch, loading from checkpoint failed"
															assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
															return

    # Now we train the model
					_a      :       Dict   =						{}
					for epoch in range(__a						,__a      ):
										model.train()
										for step, batch in enumerate(__a      ):
															_a      :       List[Any]   =						model(**__a      )
															_a      :       int   =						outputs.loss
															_a      :       Union[str, Any]   =						loss / gradient_accumulation_steps
															accelerator.backward(__a      )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1
										_a      :       Tuple   =						F"""epoch_{epoch}"""
										_a      :       str   =						os.path.join(args.output_dir						,__a      )
										accelerator.save_state(__a      )
										_a      :       Any   =						evaluation_loop(__a						,__a						,__a						,__a      )
										_a      :       Tuple   =						accuracy
										_a      :       Optional[Any]   =						lr_scheduler.get_lr()[0]
										_a      :       Dict   =						optimizer.param_groups[0]['''lr''']
										_a      :       Dict   =						epoch
										_a      :       List[Any]   =						overall_step
										accelerator.print(F"""epoch {epoch}:"""						,__a      )

										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															with open(os.path.join(args.output_dir						,F"""state_{epoch}.json"""      )						,'''w'''      ) as f:
																				json.dump(__a						,__a      )



def   __UpperCAmelCase					(      )     ->	Dict:

					"""simple docstring"""

					_a      :       List[str]   =						argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''      )
					parser.add_argument(
					    '''--model_name_or_path'''						,type=__a						,default='''bert-base-cased'''						,help='''Path to pretrained model or model identifier from huggingface.co/models.'''						,required=__a						,)
					parser.add_argument(
					    '''--output_dir'''						,type=__a						,default='''.'''						,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''						,)
					parser.add_argument(
					    '''--resume_from_checkpoint'''						,type=__a						,default=__a						,help='''If the training should continue from a checkpoint folder.'''						,)
					parser.add_argument(
					    '''--partial_train_epoch'''						,type=__a						,default=__a						,help='''If passed, the training will stop after this number of epochs.'''						,)
					parser.add_argument(
					    '''--num_epochs'''						,type=__a						,default=2						,help='''Number of train epochs.'''						,)
					_a      :       Optional[int]   =						parser.parse_args()
					_a      :       str   =						{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

					training_function(__a						,__a      )


if __name__ == "__main__":
			main()








from datetime import datetime

import requests

def        __a			(   __lowerCAmelCase   )     ->     bytes:
					SCREAMING_SNAKE_CASE							:     int						=				'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
					SCREAMING_SNAKE_CASE							:     Any						=				requests.get(base_url + url   ).json()[0]['urls'][0]['src']
					return requests.get(__lowerCAmelCase   ).content


if __name__ == "__main__":
			_lowerCamelCase    :		List[Any]		      =    input("""Enter Video/IGTV url: """).strip()
			_lowerCamelCase    :		int		      =    f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
			with open(file_name, """wb""") as fp:
						fp.write(download_video(url))
			print(f"""Done. Video saved to disk as {file_name}.""")