





import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


__lowerCAmelCase :Any     =    16
__lowerCAmelCase :List[Any]     =    32







def    A				(							UpperCAmelCase  ,  UpperCAmelCase = 16				):
					_snake_case       :					List[str]				       =   AutoTokenizer.from_pretrained("bert-base-cased"				)
					_snake_case       :					Dict				       =   load_dataset("glue"  ,  "mrpc"				)

					def tokenize_function(UpperCAmelCase				):
										# max_length=None => use the model max length (it's actually the default)
										_snake_case       :					Dict				       =   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():
										_snake_case       :					List[Any]				       =   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
					_snake_case       :					Optional[int]				       =   tokenized_datasets.rename_column("label"  ,  "labels"				)

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

										return tokenizer.pad(
										    UpperCAmelCase  ,  padding="longest"  ,  max_length=UpperCAmelCase  ,  pad_to_multiple_of=UpperCAmelCase  ,  return_tensors="pt"  ,  )

					# Instantiate dataloaders.
					_snake_case       :					List[Any]				       =   DataLoader(
					    tokenized_datasets["train"]  ,  shuffle=UpperCAmelCase  ,  collate_fn=UpperCAmelCase  ,  batch_size=UpperCAmelCase				)
					_snake_case       :					Optional[Any]				       =   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

							__lowerCAmelCase :Optional[Any]     =    mocked_dataloaders  # noqa: F811







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					# For testing only
					if os.environ.get("TESTING_MOCKED_DATALOADERS"  ,  UpperCAmelCase				) == "1":
										_snake_case       :					Tuple				       =   2
					# Initialize accelerator
					_snake_case       :					Tuple				       =   Accelerator(cpu=args.cpu  ,  mixed_precision=args.mixed_precision				)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_snake_case       :					int				       =   config["lr"]
					_snake_case       :					Optional[int]				       =   int(config["num_epochs"]				)
					_snake_case       :					Any				       =   int(config["seed"]				)
					_snake_case       :					str				       =   int(config["batch_size"]				)

					_snake_case       :					int				       =   evaluate.load("glue"  ,  "mrpc"				)

					# New Code #
					# We now can define an inner training loop function. It should take a batch size as the only parameter,
					# and build the dataloaders in there.
					# It also gets our decorator
					@find_executable_batch_size(starting_batch_size=UpperCAmelCase				)
					def inner_training_loop(UpperCAmelCase				):
										# And now just move everything below under this function
										# We need to bring in the Accelerator object from earlier
										nonlocal accelerator
										# And reset all of its attributes that could hold onto any memory:
										accelerator.free_memory()

										# Then we can declare the model, optimizer, and everything else:
										set_seed(UpperCAmelCase				)

										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										_snake_case       :					Optional[Any]				       =   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).
										_snake_case       :					Any				       =   model.to(accelerator.device				)

										# Instantiate optimizer
										_snake_case       :					Optional[Any]				       =   AdamW(params=model.parameters()  ,  lr=UpperCAmelCase				)
										_snake_case       , _snake_case       :					List[str]				       =   get_dataloaders(UpperCAmelCase  ,  UpperCAmelCase				)

										# Instantiate scheduler
										_snake_case       :					str				       =   get_linear_schedule_with_warmup(
										    optimizer=UpperCAmelCase  ,  num_warmup_steps=100  ,  num_training_steps=(len(UpperCAmelCase				) * num_epochs)  ,  )

										# 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.
										_snake_case       , _snake_case       , _snake_case       , _snake_case       , _snake_case       :					List[str]				       =   accelerator.prepare(
										    UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)

										# Now we train the model
										for epoch in range(UpperCAmelCase				):
															model.train()
															for step, batch in enumerate(UpperCAmelCase				):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device				)
																				_snake_case       :					str				       =   model(**UpperCAmelCase				)
																				_snake_case       :					Optional[int]				       =   outputs.loss
																				accelerator.backward(UpperCAmelCase				)
																				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`.
																				batch.to(accelerator.device				)
																				with torch.no_grad():
																									_snake_case       :					List[str]				       =   model(**UpperCAmelCase				)
																				_snake_case       :					Tuple				       =   outputs.logits.argmax(dim=-1				)
																				_snake_case       , _snake_case       :					int				       =   accelerator.gather_for_metrics((predictions, batch["labels"])				)
																				metric.add_batch(
																				    predictions=UpperCAmelCase  ,  references=UpperCAmelCase  ,  )

															_snake_case       :					str				       =   metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(F"""epoch {epoch}:"""  ,  UpperCAmelCase				)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
					inner_training_loop()







def    A				(							):
					_snake_case       :					int				       =   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."				)
					_snake_case       :					int				       =   parser.parse_args()
					_snake_case       :					Any				       =   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(UpperCAmelCase  ,  UpperCAmelCase				)


if __name__ == "__main__":
							main()






import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   1.5
					_snake_case       :					Union[str, Any]				       =   int(factor * num_class_images				)
					_snake_case       :					Dict				       =   ClipClient(
					    url="https://knn.laion.ai/knn-service"  ,  indice_name="laion_400m"  ,  num_images=UpperCAmelCase  ,  aesthetic_weight=0.1				)

					os.makedirs(F"""{class_data_dir}/images"""  ,  exist_ok=UpperCAmelCase				)
					if len(list(Path(F"""{class_data_dir}/images"""				).iterdir()				)				) >= num_class_images:
										return

					while True:
										_snake_case       :					Optional[int]				       =   client.query(text=UpperCAmelCase				)
										if len(UpperCAmelCase				) >= factor * num_class_images or num_images > 1e4:
															break
										else:
															_snake_case       :					str				       =   int(factor * num_images				)
															_snake_case       :					Union[str, Any]				       =   ClipClient(
															    url="https://knn.laion.ai/knn-service"  ,  indice_name="laion_400m"  ,  num_images=UpperCAmelCase  ,  aesthetic_weight=0.1  ,  )

					_snake_case       :					List[Any]				       =   0
					_snake_case       :					Any				       =   0
					_snake_case       :					List[Any]				       =   tqdm(desc="downloading real regularization images"  ,  total=UpperCAmelCase				)

					with open(F"""{class_data_dir}/caption.txt"""  ,  "w"				) as fa, open(F"""{class_data_dir}/urls.txt"""  ,  "w"				) as fa, open(
					    F"""{class_data_dir}/images.txt"""  ,  "w"				) as fa:
										while total < num_class_images:
															_snake_case       :					Optional[Any]				       =   class_images[count]
															count += 1
															try:
																				_snake_case       :					Tuple				       =   requests.get(images["url"]				)
																				if img.status_code == 200:
																									_snake_case       :					int				       =   Image.open(BytesIO(img.content				)				)
																									with open(F"""{class_data_dir}/images/{total}.jpg"""  ,  "wb"				) as f:
																														f.write(img.content				)
																									fa.write(images["caption"] + "\n"				)
																									fa.write(images["url"] + "\n"				)
																									fa.write(F"""{class_data_dir}/images/{total}.jpg""" + "\n"				)
																									total += 1
																									pbar.update(1				)
																				else:
																									continue
															except Exception:
																				continue
					return







def    A				(							):
					_snake_case       :					Any				       =   argparse.ArgumentParser(""  ,  add_help=UpperCAmelCase				)
					parser.add_argument("--class_prompt"  ,  help="text prompt to retrieve images"  ,  required=UpperCAmelCase  ,  type=UpperCAmelCase				)
					parser.add_argument("--class_data_dir"  ,  help="path to save images"  ,  required=UpperCAmelCase  ,  type=UpperCAmelCase				)
					parser.add_argument("--num_class_images"  ,  help="number of images to download"  ,  default=200  ,  type=UpperCAmelCase				)
					return parser.parse_args()


if __name__ == "__main__":
							__lowerCAmelCase :List[Any]     =    parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)