



import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# 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
#
########################################################################

__snake_case							:						List[str]      =1_6
__snake_case							:						List[Any]      =3_2






def       lowerCAmelCase__						(       lowerCamelCase_						:						Union[str, Any]			,lowerCamelCase_						:						Dict = 16):





				'''simple docstring'''

				lowerCAmelCase__	:							Dict				=	AutoTokenizer.from_pretrained('''bert-base-cased''')
				lowerCAmelCase__	:							List[Any]				=	load_dataset('''glue'''			,'''mrpc''')

				def tokenize_function(lowerCamelCase_						:						Optional[int]):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase__	:							Optional[int]				=	tokenizer(examples['''sentence1''']			,examples['''sentence2''']			,truncation=UpperCAmelCase__			,max_length=UpperCAmelCase__)
								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():
								lowerCAmelCase__	:							str				=	datasets.map(
								    UpperCAmelCase__			,batched=UpperCAmelCase__			,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
				lowerCAmelCase__	:							List[Any]				=	tokenized_datasets.rename_column('''label'''			,'''labels''')

				def collate_fn(lowerCamelCase_						:						Optional[Any]):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCAmelCase__	:							List[str]				=	128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCAmelCase__	:							str				=	16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase__	:							Optional[int]				=	8
								else:
												lowerCAmelCase__	:							Any				=	None

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

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

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							__snake_case							:						Dict      =mocked_dataloaders  # noqa: F811






def       lowerCAmelCase__						(       lowerCamelCase_						:						int			,lowerCamelCase_						:						Union[str, Any]):





				'''simple docstring'''

				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,UpperCAmelCase__) == "1":
								lowerCAmelCase__	:							Tuple				=	2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								lowerCAmelCase__	:							Optional[int]				=	Accelerator(
								    cpu=args.cpu			,mixed_precision=args.mixed_precision			,log_with='''all'''			,project_dir=args.project_dir)
				else:
								lowerCAmelCase__	:							Optional[int]				=	Accelerator(cpu=args.cpu			,mixed_precision=args.mixed_precision)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase__	:							Dict				=	config['''lr''']
				lowerCAmelCase__	:							Optional[Any]				=	int(config['''num_epochs'''])
				lowerCAmelCase__	:							Tuple				=	int(config['''seed'''])
				lowerCAmelCase__	:							str				=	int(config['''batch_size'''])
				set_seed(UpperCAmelCase__)

				lowerCAmelCase__		,						lowerCAmelCase__	:							Union[str, Any]				=	get_dataloaders(UpperCAmelCase__			,UpperCAmelCase__)
				lowerCAmelCase__	:							Any				=	evaluate.load('''glue'''			,'''mrpc''')

				# If the batch size is too big we use gradient accumulation
				lowerCAmelCase__	:							List[Any]				=	1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCAmelCase__	:							Tuple				=	batch_size // MAX_GPU_BATCH_SIZE
								lowerCAmelCase__	:							Optional[Any]				=	MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCAmelCase__	:							List[str]				=	AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,return_dict=UpperCAmelCase__)

				# 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).
				lowerCAmelCase__	:							Optional[int]				=	model.to(accelerator.device)

				# Instantiate optimizer
				lowerCAmelCase__	:							Tuple				=	AdamW(params=model.parameters()			,lr=UpperCAmelCase__)

				# Instantiate scheduler
				lowerCAmelCase__	:							Dict				=	get_linear_schedule_with_warmup(
				    optimizer=UpperCAmelCase__			,num_warmup_steps=100			,num_training_steps=(len(UpperCAmelCase__) * 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.
				lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__	:							Optional[Any]				=	accelerator.prepare(
				    UpperCAmelCase__			,UpperCAmelCase__			,UpperCAmelCase__			,UpperCAmelCase__			,UpperCAmelCase__)

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								lowerCAmelCase__	:							Optional[Any]				=	os.path.split(UpperCAmelCase__)[-1].split('''.''')[0]
								accelerator.init_trackers(UpperCAmelCase__			,UpperCAmelCase__)

				# Now we train the model
				for epoch in range(UpperCAmelCase__):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												lowerCAmelCase__	:							Any				=	0
								for step, batch in enumerate(UpperCAmelCase__):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device)
												lowerCAmelCase__	:							Optional[Any]				=	model(**UpperCAmelCase__)
												lowerCAmelCase__	:							List[str]				=	outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowerCAmelCase__	:							List[Any]				=	loss / gradient_accumulation_steps
												accelerator.backward(UpperCAmelCase__)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

								lowerCAmelCase__	:							Optional[int]				=	metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"""epoch {epoch}:"""			,UpperCAmelCase__)

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        '''accuracy''': eval_metric['''accuracy'''],
												        '''f1''': eval_metric['''f1'''],
												        '''train_loss''': total_loss.item() / len(UpperCAmelCase__),
												        '''epoch''': epoch,
												    }			,step=UpperCAmelCase__			,)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()






def       lowerCAmelCase__						(       ):





				'''simple docstring'''

				lowerCAmelCase__	:							Dict				=	argparse.ArgumentParser(description='''Simple example of training script.''')
				parser.add_argument(
				    '''--mixed_precision'''			,type=UpperCAmelCase__			,default=UpperCAmelCase__			,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.''')
				parser.add_argument(
				    '''--with_tracking'''			,action='''store_true'''			,help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''			,)
				parser.add_argument(
				    '''--project_dir'''			,type=UpperCAmelCase__			,default='''logs'''			,help='''Location on where to store experiment tracking logs` and relevent project information'''			,)
				lowerCAmelCase__	:							List[str]				=	parser.parse_args()
				lowerCAmelCase__	:							Dict				=	{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(UpperCAmelCase__			,UpperCAmelCase__)


if __name__ == "__main__":
							main()




'''simple docstring'''







import csv

import tweepy

# Twitter API credentials
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     ''''''
__lowerCamelCase									=     ''''''
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      None:
       # authorize twitter, initialize tweepy
       A_						       =   tweepy.OAuthHandler(UpperCAmelCase__,							UpperCAmelCase__					)
       auth.set_access_token(UpperCAmelCase__,							UpperCAmelCase__					)
       A_						       =   tweepy.API(UpperCAmelCase__					)

       # initialize a list to hold all the tweepy Tweets
       A_						       =   []

       # make initial request for most recent tweets (200 is the maximum allowed count)
       A_						       =   api.user_timeline(screen_name=UpperCAmelCase__,							count=2_00					)

       # save most recent tweets
       alltweets.extend(UpperCAmelCase__					)

       # save the id of the oldest tweet less one
       A_						       =   alltweets[-1].id - 1

       # keep grabbing tweets until there are no tweets left to grab
       while len(UpperCAmelCase__					) > 0:
              print(F'''getting tweets before {oldest}'''					)

              # all subsequent requests use the max_id param to prevent duplicates
              A_						       =   api.user_timeline(
                  screen_name=UpperCAmelCase__,							count=2_00,							max_id=UpperCAmelCase__					)

              # save most recent tweets
              alltweets.extend(UpperCAmelCase__					)

              # update the id of the oldest tweet less one
              A_						       =   alltweets[-1].id - 1

              print(F'''...{len(UpperCAmelCase__					)} tweets downloaded so far'''					)

       # transform the tweepy tweets into a 2D array that will populate the csv
       A_						       =   [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

       # write the csv
       with open(F'''new_{screen_name}_tweets.csv''',							"""w"""					) as f:
              A_						       =   csv.writer(UpperCAmelCase__					)
              writer.writerow(["""id""", """created_at""", """text"""]					)
              writer.writerows(UpperCAmelCase__					)


if __name__ == "__main__":
      # pass in the username of the account you want to download
      get_all_tweets('''FirePing32''')
