


"""simple docstring"""






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_lowerCamelCase     =  logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')

_lowerCamelCase     =  list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_lowerCamelCase     =  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


def __lowercase     (      lowerCamelCase_  :      str  ):
				with open(lowerCamelCase_						,					"rb"  ) as f:
								SCREAMING_SNAKE_CASE__     							=				Image.open(lowerCamelCase_  )
								return im.convert("RGB"  )


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      Optional[str]      							=					field(
				    default=lowercase       ,  metadata={
				        "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
				    }       ,  )
				_lowerCAmelCase      :      Optional[str]      							=					field(
				    default=lowercase       ,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
				_lowerCAmelCase      :      Optional[str]      							=					field(default=lowercase       ,  metadata={"help": "A folder containing the training data."}   )
				_lowerCAmelCase      :      Optional[str]      							=					field(default=lowercase       ,  metadata={"help": "A folder containing the validation data."}   )
				_lowerCAmelCase      :      Optional[float]      							=					field(
				    default=0.15       ,  metadata={"help": "Percent to split off of train for validation."}   )
				_lowerCAmelCase      :      Optional[int]      							=					field(
				    default=lowercase       ,  metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of training examples to this "
				            "value if set."
				        )
				    }       ,  )
				_lowerCAmelCase      :      Optional[int]      							=					field(
				    default=lowercase       ,  metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
				            "value if set."
				        )
				    }       ,  )



				def   lowerCAmelCase__		(						self     ):
								if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
												raise ValueError(
												    "You must specify either a dataset name from the hub or a train and/or validation directory."     )


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      str      							=					field(
				    default="google/vit-base-patch16-224-in21k"       ,  metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       ,  )
				_lowerCAmelCase      :      Optional[str]      							=					field(
				    default=lowercase       ,  metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowercase   )}       ,  )
				_lowerCAmelCase      :      Optional[str]      							=					field(
				    default=lowercase       ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
				_lowerCAmelCase      :      Optional[str]      							=					field(
				    default=lowercase       ,  metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}   )
				_lowerCAmelCase      :      str      							=					field(
				    default="main"       ,  metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}       ,  )
				_lowerCAmelCase      :      str      							=					field(default=lowercase       ,  metadata={"help": "Name or path of preprocessor config."}   )
				_lowerCAmelCase      :      bool      							=					field(
				    default=lowercase       ,  metadata={
				        "help": (
				            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
				            "with private models)."
				        )
				    }       ,  )
				_lowerCAmelCase      :      bool      							=					field(
				    default=lowercase       ,  metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}       ,  )


def __lowercase     (      lowerCamelCase_  :      Optional[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				torch.stack([example["pixel_values"] for example in examples]  )
				SCREAMING_SNAKE_CASE__     							=				torch.tensor([example["labels"] for example in examples]  )
				return {"pixel_values": pixel_values, "labels": labels}


def __lowercase     (      ):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				SCREAMING_SNAKE_CASE__     							=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
				if len(sys.argv  ) == 2 and sys.argv[1].endswith(".json"  ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
				else:
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry("run_image_classification"						,					lowerCamelCase_						,					lowerCamelCase_  )

				# Setup logging
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"						,					datefmt="%m/%d/%Y %H:%M:%S"						,					handlers=[logging.StreamHandler(sys.stdout  )]						,					)

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				SCREAMING_SNAKE_CASE__     							=				training_args.get_process_log_level()
				logger.setLevel(lowerCamelCase_  )
				transformers.utils.logging.set_verbosity(lowerCamelCase_  )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + F'''distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}'''  )
				logger.info(F'''Training/evaluation parameters {training_args}'''  )

				# Detecting last checkpoint.
				SCREAMING_SNAKE_CASE__     							=				None
				if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
								SCREAMING_SNAKE_CASE__     							=				get_last_checkpoint(training_args.output_dir  )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
												raise ValueError(
												    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    "Use --overwrite_output_dir to overcome."  )
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."  )

    # Set seed before initializing model.
				set_seed(training_args.seed  )

				# Initialize our dataset and prepare it for the 'image-classification' task.
				if data_args.dataset_name is not None:
								SCREAMING_SNAKE_CASE__     							=				load_dataset(
								    data_args.dataset_name						,					data_args.dataset_config_name						,					cache_dir=model_args.cache_dir						,					task="image-classification"						,					use_auth_token=True if model_args.use_auth_token else None						,					)
				else:
								SCREAMING_SNAKE_CASE__     							=				{}
								if data_args.train_dir is not None:
												SCREAMING_SNAKE_CASE__     							=				os.path.join(data_args.train_dir						,					"**"  )
								if data_args.validation_dir is not None:
												SCREAMING_SNAKE_CASE__     							=				os.path.join(data_args.validation_dir						,					"**"  )
								SCREAMING_SNAKE_CASE__     							=				load_dataset(
								    "imagefolder"						,					data_files=lowerCamelCase_						,					cache_dir=model_args.cache_dir						,					task="image-classification"						,					)

				# If we don't have a validation split, split off a percentage of train as validation.
				SCREAMING_SNAKE_CASE__     							=				None if "validation" in dataset.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split						,					lowerCamelCase_  ) and data_args.train_val_split > 0.0:
								SCREAMING_SNAKE_CASE__     							=				dataset["train"].train_test_split(data_args.train_val_split  )
								SCREAMING_SNAKE_CASE__     							=				split["train"]
								SCREAMING_SNAKE_CASE__     							=				split["test"]

				# Prepare label mappings.
				# We'll include these in the model's config to get human readable labels in the Inference API.
				SCREAMING_SNAKE_CASE__     							=				dataset["train"].features["labels"].names
				SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				{}, {}
				for i, label in enumerate(lowerCamelCase_  ):
								SCREAMING_SNAKE_CASE__     							=				str(lowerCamelCase_  )
								SCREAMING_SNAKE_CASE__     							=				label

				# Load the accuracy metric from the datasets package
				SCREAMING_SNAKE_CASE__     							=				evaluate.load("accuracy"  )

				# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(lowerCamelCase_  :      str  ):
								return metric.compute(predictions=np.argmax(p.predictions						,					axis=1  )						,					references=p.label_ids  )

				SCREAMING_SNAKE_CASE__     							=				AutoConfig.from_pretrained(
				    model_args.config_name or model_args.model_name_or_path						,					num_labels=len(lowerCamelCase_  )						,					labelaid=lowerCamelCase_						,					idalabel=lowerCamelCase_						,					finetuning_task="image-classification"						,					cache_dir=model_args.cache_dir						,					revision=model_args.model_revision						,					use_auth_token=True if model_args.use_auth_token else None						,					)
				SCREAMING_SNAKE_CASE__     							=				AutoModelForImageClassification.from_pretrained(
				    model_args.model_name_or_path						,					from_tf=bool(".ckpt" in model_args.model_name_or_path  )						,					config=lowerCamelCase_						,					cache_dir=model_args.cache_dir						,					revision=model_args.model_revision						,					use_auth_token=True if model_args.use_auth_token else None						,					ignore_mismatched_sizes=model_args.ignore_mismatched_sizes						,					)
				SCREAMING_SNAKE_CASE__     							=				AutoImageProcessor.from_pretrained(
				    model_args.image_processor_name or model_args.model_name_or_path						,					cache_dir=model_args.cache_dir						,					revision=model_args.model_revision						,					use_auth_token=True if model_args.use_auth_token else None						,					)

				# Define torchvision transforms to be applied to each image.
				if "shortest_edge" in image_processor.size:
								SCREAMING_SNAKE_CASE__     							=				image_processor.size["shortest_edge"]
				else:
								SCREAMING_SNAKE_CASE__     							=				(image_processor.size["height"], image_processor.size["width"])
				SCREAMING_SNAKE_CASE__     							=				Normalize(mean=image_processor.image_mean						,					std=image_processor.image_std  )
				SCREAMING_SNAKE_CASE__     							=				Compose(
				    [
				        RandomResizedCrop(lowerCamelCase_  ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        normalize,
				    ]  )
				SCREAMING_SNAKE_CASE__     							=				Compose(
				    [
				        Resize(lowerCamelCase_  ),
				        CenterCrop(lowerCamelCase_  ),
				        ToTensor(),
				        normalize,
				    ]  )

				def train_transforms(lowerCamelCase_  :      Any  ):
								SCREAMING_SNAKE_CASE__     							=				[
								    _train_transforms(pil_img.convert("RGB"  )  ) for pil_img in example_batch["image"]
								]
								return example_batch

				def val_transforms(lowerCamelCase_  :      str  ):
								SCREAMING_SNAKE_CASE__     							=				[_val_transforms(pil_img.convert("RGB"  )  ) for pil_img in example_batch["image"]]
								return example_batch

				if training_args.do_train:
								if "train" not in dataset:
												raise ValueError("--do_train requires a train dataset"  )
								if data_args.max_train_samples is not None:
												SCREAMING_SNAKE_CASE__     							=				(
												    dataset["train"].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
												)
								# Set the training transforms
								dataset["train"].set_transform(lowerCamelCase_  )

				if training_args.do_eval:
								if "validation" not in dataset:
												raise ValueError("--do_eval requires a validation dataset"  )
								if data_args.max_eval_samples is not None:
												SCREAMING_SNAKE_CASE__     							=				(
												    dataset["validation"].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
												)
								# Set the validation transforms
								dataset["validation"].set_transform(lowerCamelCase_  )

				# Initalize our trainer
				SCREAMING_SNAKE_CASE__     							=				Trainer(
				    model=lowerCamelCase_						,					args=lowerCamelCase_						,					train_dataset=dataset["train"] if training_args.do_train else None						,					eval_dataset=dataset["validation"] if training_args.do_eval else None						,					compute_metrics=lowerCamelCase_						,					tokenizer=lowerCamelCase_						,					data_collator=lowerCamelCase_						,					)

				# Training
				if training_args.do_train:
								SCREAMING_SNAKE_CASE__     							=				None
								if training_args.resume_from_checkpoint is not None:
												SCREAMING_SNAKE_CASE__     							=				training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												SCREAMING_SNAKE_CASE__     							=				last_checkpoint
								SCREAMING_SNAKE_CASE__     							=				trainer.train(resume_from_checkpoint=lowerCamelCase_  )
								trainer.save_model()
								trainer.log_metrics("train"						,					train_result.metrics  )
								trainer.save_metrics("train"						,					train_result.metrics  )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								SCREAMING_SNAKE_CASE__     							=				trainer.evaluate()
								trainer.log_metrics("eval"						,					lowerCamelCase_  )
								trainer.save_metrics("eval"						,					lowerCamelCase_  )

				# Write model card and (optionally) push to hub
				SCREAMING_SNAKE_CASE__     							=				{
				    "finetuned_from": model_args.model_name_or_path,
				    "tasks": "image-classification",
				    "dataset": data_args.dataset_name,
				    "tags": ["image-classification", "vision"],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**lowerCamelCase_  )
				else:
								trainer.create_model_card(**lowerCamelCase_  )


if __name__ == "__main__":
	main()




"""simple docstring"""






import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
	# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
	# setup instructions, if using on-demand hardware

	# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
	# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
	# Throw an error if user passes both BYO and on-demand cluster args
	# Otherwise, use default values
	_lowerCamelCase     =  argparse.ArgumentParser()
	parser.add_argument('--user', type=str, default='ubuntu')
	parser.add_argument('--host', type=str, default='localhost')
	parser.add_argument('--key_path', type=str, default=None)
	parser.add_argument('--instance', type=str, default='V100:1')
	parser.add_argument('--provider', type=str, default='cheapest')
	parser.add_argument('--use_spot', type=bool, default=False)
	parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
	_lowerCamelCase     ,     _lowerCamelCase     =  parser.parse_known_args()
	if args.host != "localhost":
		if args.instance != "V100:1" or args.provider != "cheapest":
			raise ValueError('Cannot specify both BYO and on-demand cluster args')
		_lowerCamelCase     =  rh.cluster(
		    name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
		)
	else:
		_lowerCamelCase     =  rh.cluster(
		    name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
		)
	_lowerCamelCase     =  args.example.rsplit('/', 1)[0]

	# Set up remote environment
	cluster.install_packages(['pip:./'])  # Installs transformers from local source
	# Note transformers is copied into the home directory on the remote machine, so we can install from there
	cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
	cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])

	# Run example. You can bypass the CLI wrapper and paste your own code here.
	cluster.run([f"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])

	# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
	# from my_script... import train
	# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
	# launch_train_gpu = rh.function(fn=train,
	#                                system=gpu,
	#                                reqs=reqs,
	#                                name='train_bert_glue')
	#
	# We can pass in arguments just like we would to a function:
	# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
	#                  stream_logs=True)
