




import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


__lowerCAmelCase	:       Optional[Any]     =	'\\n\n'

__lowerCAmelCase	:       Optional[Any]     =	'\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

__lowerCAmelCase	:       Any     =	'\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					, _KWARGS_DESCRIPTION     )
class 					snake_case__							(datasets.Metric     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Union[str, Any]:
							return datasets.MetricInfo(
							    description=_DESCRIPTION      ,   citation=_CITATION      ,   inputs_description=_KWARGS_DESCRIPTION      ,   features=datasets.Features(
							        {
							            "input_texts": datasets.Value("string"							),
							        }							)      ,   reference_urls=["https://huggingface.co/docs/transformers/perplexity"]      ,   )





				def 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  int = 16      ,   __lowerCamelCase       :  bool = True      ,   __lowerCamelCase       :  Union[str, Any]=None							)      ->  Dict:
							if device is not None:
										assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
										if device == "gpu":
													a					=    "cuda"
							else:
										a					=    "cuda" if torch.cuda.is_available() else "cpu"

							a					=    AutoModelForCausalLM.from_pretrained(__lowerCamelCase							)
							a					=    model.to(__lowerCamelCase							)

							a					=    AutoTokenizer.from_pretrained(__lowerCamelCase							)

							# if batch_size > 1 (which generally leads to padding being required), and
							# if there is not an already assigned pad_token, assign an existing
							# special token to also be the padding token
							if tokenizer.pad_token is None and batch_size > 1:
										a					=    list(tokenizer.special_tokens_map_extended.values()							)
										# check that the model already has at least one special token defined
										assert (
										    len(__lowerCamelCase							) > 0
										), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
										# assign one of the special tokens to also be the pad token
										tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]}							)

							if add_start_token:
										# leave room for <BOS> token to be added:
										assert (
										    tokenizer.bos_token is not None
										), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
										a					=    model.config.max_length - 1
							else:
										a					=    model.config.max_length

							a					=    tokenizer(
							    __lowerCamelCase      ,   add_special_tokens=__lowerCamelCase      ,   padding=__lowerCamelCase      ,   truncation=__lowerCamelCase      ,   max_length=__lowerCamelCase      ,   return_tensors="pt"      ,   return_attention_mask=__lowerCamelCase      ,   ).to(__lowerCamelCase							)

							a					=    encodings["input_ids"]
							a					=    encodings["attention_mask"]

							# check that each input is long enough:
							if add_start_token:
										assert torch.all(torch.ge(attn_masks.sum(1							)      ,   1							)							), "Each input text must be at least one token long."
							else:
										assert torch.all(
										    torch.ge(attn_masks.sum(1							)      ,   2							)							), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

							a					=    []
							a					=    CrossEntropyLoss(reduction="none"							)

							for start_index in logging.tqdm(range(0      ,   len(__lowerCamelCase							)      ,   __lowerCamelCase							)							):
										a					=    min(start_index + batch_size      ,   len(__lowerCamelCase							)							)
										a					=    encoded_texts[start_index:end_index]
										a					=    attn_masks[start_index:end_index]

										if add_start_token:
													a					=    torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0							)							).to(__lowerCamelCase							)
													a					=    torch.cat([bos_tokens_tensor, encoded_batch]      ,   dim=1							)
													a					=    torch.cat(
													    [torch.ones(bos_tokens_tensor.size()      ,   dtype=torch.intaa							).to(__lowerCamelCase							), attn_mask]      ,   dim=1							)

										a					=    encoded_batch

										with torch.no_grad():
													a					=    model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase							).logits

										a					=    out_logits[..., :-1, :].contiguous()
										a					=    labels[..., 1:].contiguous()
										a					=    attn_mask[..., 1:].contiguous()

										a					=    torch.expa(
										    (loss_fct(shift_logits.transpose(1      ,   2							)      ,   __lowerCamelCase							) * shift_attention_mask_batch).sum(1							)
										    / shift_attention_mask_batch.sum(1							)							)

										ppls += perplexity_batch.tolist()

							return {"perplexities": ppls, "mean_perplexity": np.mean(__lowerCamelCase							)}












import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator




def     __magic_name__       (				A	:      Union[str, Any]     ):






			'''simple docstring'''







			a					=    fname.split(os.path.sep     )[-1]
			return re.search(R"^(.*)_\d+\.jpg$",						A     ).groups()[0]




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  str      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict=None      ,   __lowerCamelCase       :  Union[str, Any]=None							)      ->  Tuple:
							a					=    file_names
							a					=    image_transform
							a					=    label_to_id





				def __len__(   self       :  Any							)      ->  Tuple:
							return len(self.file_names							)





				def __getitem__(   self       :  List[Any]      ,   __lowerCamelCase       :  List[Any]							)      ->  int:
							a					=    self.file_names[idx]
							a					=    PIL.Image.open(__lowerCamelCase							)
							a					=    raw_image.convert("RGB"							)
							if self.image_transform is not None:
										a					=    self.image_transform(__lowerCamelCase							)
							a					=    extract_label(__lowerCamelCase							)
							if self.label_to_id is not None:
										a					=    self.label_to_id[label]
							return {"image": image, "label": label}




def     __magic_name__       (				A	:      str,						A	:      int     ):






			'''simple docstring'''







			if args.with_tracking:
						a					=    Accelerator(
						    cpu=args.cpu,						mixed_precision=args.mixed_precision,						log_with="all",						project_dir=args.project_dir     )
			else:
						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					=    config["image_size"]
			if not isinstance(A,						(list, tuple)     ):
						a					=    (image_size, image_size)

			# Parse out whether we are saving every epoch or after a certain number of batches
			if hasattr(args.checkpointing_steps,						"isdigit"     ):
						if args.checkpointing_steps == "epoch":
									a					=    args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									a					=    int(args.checkpointing_steps     )
						else:
									raise ValueError(
									    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
			else:
						a					=    None

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						a					=    os.path.split(A     )[-1].split("."     )[0]
						accelerator.init_trackers(A,						A     )

			# Grab all the image filenames
			a					=    [os.path.join(args.data_dir,						A     ) for fname in os.listdir(args.data_dir     ) if fname.endswith(".jpg"     )]

			# Build the label correspondences
			a					=    [extract_label(A     ) for fname in file_names]
			a					=    list(set(A     )     )
			id_to_label.sort()
			a					=    {lbl: i for i, lbl in enumerate(A     )}

			# Set the seed before splitting the data.
			np.random.seed(A     )
			torch.manual_seed(A     )
			torch.cuda.manual_seed_all(A     )

			# Split our filenames between train and validation
			a					=    np.random.permutation(len(A     )     )
			a					=    int(0.8 * len(A     )     )
			a					=    random_perm[:cut]
			a					=    random_perm[cut:]

			# For training we use a simple RandomResizedCrop
			a					=    Compose([RandomResizedCrop(A,						scale=(0.5, 1.0)     ), ToTensor()]     )
			a					=    PetsDataset(
			    [file_names[i] for i in train_split],						image_transform=A,						label_to_id=A     )

			# For evaluation, we use a deterministic Resize
			a					=    Compose([Resize(A     ), ToTensor()]     )
			a					=    PetsDataset([file_names[i] for i in eval_split],						image_transform=A,						label_to_id=A     )

			# Instantiate dataloaders.
			a					=    DataLoader(A,						shuffle=A,						batch_size=A,						num_workers=4     )
			a					=    DataLoader(A,						shuffle=A,						batch_size=A,						num_workers=4     )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a					=    create_model("resnet50d",						pretrained=A,						num_classes=len(A     )     )

			# 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     )

			# Freezing the base model
			for param in model.parameters():
						a					=    False
			for param in model.get_classifier().parameters():
						a					=    True

			# We normalize the batches of images to be a bit faster.
			a					=    torch.tensor(model.default_cfg["mean"]     )[None, :, None, None].to(accelerator.device     )
			a					=    torch.tensor(model.default_cfg["std"]     )[None, :, None, None].to(accelerator.device     )

			# Instantiate optimizer
			a					=    torch.optim.Adam(params=model.parameters(),						lr=lr / 25     )

			# Instantiate learning rate scheduler
			a					=    OneCycleLR(optimizer=A,						max_lr=A,						epochs=A,						steps_per_epoch=len(A     )     )

			# 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(
			    A,						A,						A,						A,						A     )
			# We need to keep track of how many total steps we have iterated over
			a					=    0
			# We also need to keep track of the starting epoch so files are named properly
			a					=    0

			# Potentially load in the weights and states from a previous save
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""     )
									accelerator.load_state(args.resume_from_checkpoint     )
									a					=    os.path.basename(args.resume_from_checkpoint     )
						else:
									# Get the most recent checkpoint
									a					=    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
									dirs.sort(key=os.path.getctime     )
									a					=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						a					=    os.path.splitext(A     )[0]

						if "epoch" in training_difference:
									a					=    int(training_difference.replace("epoch_",						""     )     ) + 1
									a					=    None
						else:
									a					=    int(training_difference.replace("step_",						""     )     )
									a					=    resume_step // len(A     )
									resume_step -= starting_epoch * len(A     )

    # Now we train the model
			for epoch in range(A,						A     ):
						model.train()
						if args.with_tracking:
									a					=    0
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									a					=    accelerator.skip_first_batches(A,						A     )
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									a					=    train_dataloader
						for batch in active_dataloader:
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									a					=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
									a					=    (batch["image"] - mean) / std
									a					=    model(A     )
									a					=    torch.nn.functional.cross_entropy(A,						batch["label"]     )
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(A     )
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()
									overall_step += 1
									if isinstance(A,						A     ):
												a					=    F"""step_{overall_step}"""
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		a					=    os.path.join(args.output_dir,						A     )
															accelerator.save_state(A     )
						model.eval()
						a					=    0
						a					=    0
						for step, batch in enumerate(A     ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									a					=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
									a					=    (batch["image"] - mean) / std
									with torch.no_grad():
												a					=    model(A     )
									a					=    outputs.argmax(dim=-1     )
									a					,	a					=    accelerator.gather_for_metrics((predictions, batch["label"])     )
									a					=    predictions == references
									num_elems += accurate_preds.shape[0]
									accurate += accurate_preds.long().sum()

						a					=    accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""     )
						if args.with_tracking:
									accelerator.log(
									    {
									        "accuracy": 100 * eval_metric,
									        "train_loss": total_loss.item() / len(A     ),
									        "epoch": epoch,
									    },						step=A,						)
						if checkpointing_steps == "epoch":
									a					=    F"""epoch_{epoch}"""
									if args.output_dir is not None:
												a					=    os.path.join(args.output_dir,						A     )
									accelerator.save_state(A     )

			if args.with_tracking:
						accelerator.end_training()




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    argparse.ArgumentParser(description="Simple example of training script."     )
			parser.add_argument("--data_dir",						required=A,						help="The data folder on disk."     )
			parser.add_argument("--fp16",						action="store_true",						help="If passed, will use FP16 training."     )
			parser.add_argument(
			    "--mixed_precision",						type=A,						default=A,						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(
			    "--checkpointing_steps",						type=A,						default=A,						help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",						)
			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(
			    "--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=A,						default="logs",						help="Location on where to store experiment tracking logs` and relevent project information",						)
			a					=    parser.parse_args()
			a					=    {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
			training_function(A,						A     )


if __name__ == "__main__":
		main()






