


'''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
__UpperCAmelCase					       =							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''')

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


def       _snake_case     (  A )		->					str:
							with open(snake_case_				,					'''rb''' ) as f:
														lowerCAmelCase__      =			Image.open(snake_case_ )
														return im.convert('''RGB''' )




@dataclass
class 	a__       :





							'''simple docstring'''






							lowercase__    :     List[Any]					=   field(
							    default=UpperCamelCase_       ,       metadata={
							        "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
							    }       ,       )
							lowercase__    :     Any					=   field(
							    default=UpperCamelCase_       ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
							lowercase__    :     str					=   field(default=UpperCamelCase_       ,       metadata={"help": "A folder containing the training data."}						)
							lowercase__    :     Dict					=   field(default=UpperCamelCase_       ,       metadata={"help": "A folder containing the validation data."}						)
							lowercase__    :     Tuple					=   field(
							    default=0.1_5       ,       metadata={"help": "Percent to split off of train for validation."}						)
							lowercase__    :     Any					=   field(
							    default=UpperCamelCase_       ,       metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }       ,       )
							lowercase__    :     Optional[int]					=   field(
							    default=UpperCamelCase_       ,       metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }       ,       )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														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 	a__       :





							'''simple docstring'''






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


def       _snake_case     (  A )		->					Optional[Any]:
							lowerCAmelCase__      =			torch.stack([example['''pixel_values'''] for example in examples] )
							lowerCAmelCase__      =			torch.tensor([example['''labels'''] for example in examples] )
							return {"pixel_values": pixel_values, "labels": labels}


def       _snake_case     (  )		->					Union[str, Any]:
							# 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.

							lowerCAmelCase__      =			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.
														lowerCAmelCase__      =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
							else:
														lowerCAmelCase__      =			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'''				,					snake_case_				,					snake_case_ )

							# 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()

							lowerCAmelCase__      =			training_args.get_process_log_level()
							logger.setLevel(snake_case_ )
							transformers.utils.logging.set_verbosity(snake_case_ )
							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.
							lowerCAmelCase__      =			None
							if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
														lowerCAmelCase__      =			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:
														lowerCAmelCase__      =			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:
														lowerCAmelCase__      =			{}
														if data_args.train_dir is not None:
																					lowerCAmelCase__      =			os.path.join(data_args.train_dir				,					'''**''' )
														if data_args.validation_dir is not None:
																					lowerCAmelCase__      =			os.path.join(data_args.validation_dir				,					'''**''' )
														lowerCAmelCase__      =			load_dataset(
														    '''imagefolder'''				,					data_files=snake_case_				,					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.
							lowerCAmelCase__      =			None if '''validation''' in dataset.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split				,					snake_case_ ) and data_args.train_val_split > 0.0:
														lowerCAmelCase__      =			dataset['''train'''].train_test_split(data_args.train_val_split )
														lowerCAmelCase__      =			split['''train''']
														lowerCAmelCase__      =			split['''test''']

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

							# Load the accuracy metric from the datasets package
							lowerCAmelCase__      =			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(A ):
														return metric.compute(predictions=np.argmax(p.predictions				,					axis=1 )				,					references=p.label_ids )

							lowerCAmelCase__      =			AutoConfig.from_pretrained(
							    model_args.config_name or model_args.model_name_or_path				,					num_labels=len(snake_case_ )				,					labelaid=snake_case_				,					idalabel=snake_case_				,					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				,					)
							lowerCAmelCase__      =			AutoModelForImageClassification.from_pretrained(
							    model_args.model_name_or_path				,					from_tf=bool('''.ckpt''' in model_args.model_name_or_path )				,					config=snake_case_				,					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				,					)
							lowerCAmelCase__      =			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:
														lowerCAmelCase__      =			image_processor.size['''shortest_edge''']
							else:
														lowerCAmelCase__      =			(image_processor.size['''height'''], image_processor.size['''width'''])
							lowerCAmelCase__      =			Normalize(mean=image_processor.image_mean				,					std=image_processor.image_std )
							lowerCAmelCase__      =			Compose(
							    [
							        RandomResizedCrop(snake_case_ ),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ] )
							lowerCAmelCase__      =			Compose(
							    [
							        Resize(snake_case_ ),
							        CenterCrop(snake_case_ ),
							        ToTensor(),
							        normalize,
							    ] )

							def train_transforms(A ):
														lowerCAmelCase__      =			[
														    _train_transforms(pil_img.convert('''RGB''' ) ) for pil_img in example_batch['''image''']
														]
														return example_batch

							def val_transforms(A ):
														lowerCAmelCase__      =			[_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:
																					lowerCAmelCase__      =			(
																					    dataset['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
																					)
														# Set the training transforms
														dataset["train"].set_transform(snake_case_ )

							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:
																					lowerCAmelCase__      =			(
																					    dataset['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
																					)
														# Set the validation transforms
														dataset["validation"].set_transform(snake_case_ )

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

							# Training
							if training_args.do_train:
														lowerCAmelCase__      =			None
														if training_args.resume_from_checkpoint is not None:
																					lowerCAmelCase__      =			training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					lowerCAmelCase__      =			last_checkpoint
														lowerCAmelCase__      =			trainer.train(resume_from_checkpoint=snake_case_ )
														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:
														lowerCAmelCase__      =			trainer.evaluate()
														trainer.log_metrics('''eval'''				,					snake_case_ )
														trainer.save_metrics('''eval'''				,					snake_case_ )

							# Write model card and (optionally) push to hub
							lowerCAmelCase__      =			{
							    '''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(**snake_case_ )
							else:
														trainer.create_model_card(**snake_case_ )


if __name__ == "__main__":
							main()



'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase					       =							False




class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							pass




@slow
@require_torch_gpu
class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''     )
														pipe.to(lowerCamelCase_     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

														lowerCAmelCase__      =			load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''     )
														lowerCAmelCase__      =			torch.manual_seed(0     )
														lowerCAmelCase__      =			pipe(
														    image=lowerCamelCase_      ,					generator=lowerCamelCase_      ,					guidance_scale=7.5      ,					num_inference_steps=50      ,					output_type='''numpy'''      ,					).images

														lowerCAmelCase__      =			image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__      =			np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

														assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2