

'''simple docstring'''





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
#
########################################################################


UpperCamelCase_    						=       16
UpperCamelCase_    						=       32






def 				_lowerCAmelCase			(   __magic_name__						:     Accelerator   , __magic_name__						:     DatasetDict   , __magic_name__						:     List[int]   , __magic_name__						:     List[int]   , __magic_name__						:     int = 16						)				->  Any:
						lowercase :							int    		=AutoTokenizer.from_pretrained('''bert-base-cased'''						)
						lowercase :							Optional[Any]    		=DatasetDict(
						    {
						        '''train''': dataset['''train'''].select(__magic_name__						),
						        '''validation''': dataset['''train'''].select(__magic_name__						),
						        '''test''': dataset['''validation'''],
						    }						)

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

						def collate_fn(__magic_name__						:     Dict						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowercase :							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":
																		lowercase :							Optional[Any]    		=16
												elif accelerator.mixed_precision != "no":
																		lowercase :							Union[str, Any]    		=8
												else:
																		lowercase :							str    		=None

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

						# Instantiate dataloaders.
						lowercase :							str    		=DataLoader(
						    tokenized_datasets['''train''']   , shuffle=__magic_name__   , collate_fn=__magic_name__   , batch_size=__magic_name__						)
						lowercase :							Tuple    		=DataLoader(
						    tokenized_datasets['''validation''']   , shuffle=__magic_name__   , collate_fn=__magic_name__   , batch_size=__magic_name__						)

						lowercase :							int    		=DataLoader(
						    tokenized_datasets['''test''']   , shuffle=__magic_name__   , collate_fn=__magic_name__   , batch_size=__magic_name__						)

						return train_dataloader, eval_dataloader, test_dataloader






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[int]   , __magic_name__						:     Dict						)				->  Optional[Any]:
						# New Code #
						lowercase :							Any    		=[]
						# Download the dataset
						lowercase :							List[Any]    		=load_dataset('''glue'''   , '''mrpc'''						)
						# Create our splits
						lowercase :							List[str]    		=StratifiedKFold(n_splits=int(args.num_folds						)						)
						# Initialize accelerator
						lowercase :							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
						lowercase :							Optional[int]    		=config['''lr''']
						lowercase :							Any    		=int(config['''num_epochs''']						)
						lowercase :							List[Any]    		=int(config['''seed''']						)
						lowercase :							Optional[Any]    		=int(config['''batch_size''']						)

						lowercase :							int    		=evaluate.load('''glue'''   , '''mrpc'''						)

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

						set_seed(__magic_name__						)

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

												# 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).
												lowercase :							Dict    		=model.to(accelerator.device						)

												# Instantiate optimizer
												lowercase :							List[str]    		=AdamW(params=model.parameters()   , lr=__magic_name__						)

												# Instantiate scheduler
												lowercase :							Dict    		=get_linear_schedule_with_warmup(
												    optimizer=__magic_name__   , num_warmup_steps=100   , num_training_steps=(len(__magic_name__						) * 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.
												lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase :							Optional[int]    		=accelerator.prepare(
												    __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__						)

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

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

																		lowercase :							str    		=metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(f'''epoch {epoch}:'''   , __magic_name__						)

												# New Code #
												# We also run predictions on the test set at the very end
												lowercase :							int    		=[]
												for step, batch in enumerate(__magic_name__						):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device						)
																		with torch.no_grad():
																								lowercase :							int    		=model(**__magic_name__						)
																		lowercase :							int    		=outputs.logits
																		lowercase		,	lowercase :							Dict    		=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(__magic_name__   , 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:
						lowercase :							List[Any]    		=torch.cat(__magic_name__   , dim=0						)
						lowercase :							Tuple    		=torch.stack(__magic_name__   , dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
						lowercase :							Optional[Any]    		=metric.compute(predictions=__magic_name__   , references=__magic_name__						)
						accelerator.print('''Average test metrics from all folds:'''   , __magic_name__						)






def 				_lowerCAmelCase			(   )				->  List[Any]:
						lowercase :							Any    		=argparse.ArgumentParser(description='''Simple example of training script.'''						)
						parser.add_argument(
						    '''--mixed_precision'''   , type=__magic_name__   , default=__magic_name__   , 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=__magic_name__   , default=3   , help='''The number of splits to perform across the dataset'''						)
						lowercase :							List[Any]    		=parser.parse_args()
						lowercase :							Any    		={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(__magic_name__   , __magic_name__						)


if __name__ == "__main__":
							main()












import requests

a__      :     Any         =		'YOUR API KEY'

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:str = giphy_api_key				)	->				list:
     '''simple docstring'''


     A_          =       '''+'''.join(query.split()				)
     A_          =       f'https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'
     A_          =       requests.get(_UpperCAmelCase				).json()['''data''']
     return [gif["url"] for gif in gifs]


if __name__ == "__main__":
      print('\n'.join(get_gifs('space ship')))



