



"""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
__a      =				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")

__a      =				list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__a      =				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def    A_  (			_lowercase	):







						'''simple docstring'''


						with open(_lowercase,					"""rb"""	) as f:
												snake_case_ :str						=			Image.open(_lowercase	)
												return im.convert("""RGB"""	)
@dataclass
class 				lowerCamelCase  :






					'''simple docstring'''





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






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Any:
											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'''





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

def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			torch.stack([example["""pixel_values"""] for example in examples]	)
						snake_case_ :Union[str, Any]						=			torch.tensor([example["""labels"""] for example in examples]	)
						return {"pixel_values": pixel_values, "labels": labels}
def    A_  (			):







						'''simple docstring'''


						snake_case_ :Any						=			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.
												snake_case_,       snake_case_,       snake_case_ :int						=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
						else:
												snake_case_,       snake_case_,       snake_case_ :Tuple						=			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""",					_lowercase,					_lowercase	)

						# 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()

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

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

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

						snake_case_ :Tuple						=			AutoConfig.from_pretrained(
						    model_args.config_name or model_args.model_name_or_path,					num_labels=len(_lowercase	),					labelaid=_lowercase,					idalabel=_lowercase,					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,					)
						snake_case_ :Optional[int]						=			AutoModelForImageClassification.from_pretrained(
						    model_args.model_name_or_path,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path	),					config=_lowercase,					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,					)
						snake_case_ :Union[str, Any]						=			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:
												snake_case_ :Union[str, Any]						=			image_processor.size["""shortest_edge"""]
						else:
												snake_case_ :Optional[Any]						=			(image_processor.size["""height"""], image_processor.size["""width"""])
						snake_case_ :List[Any]						=			Normalize(mean=image_processor.image_mean,					std=image_processor.image_std	)
						snake_case_ :Optional[Any]						=			Compose(
						    [
						        RandomResizedCrop(_lowercase	),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        normalize,
						    ]	)
						snake_case_ :Any						=			Compose(
						    [
						        Resize(_lowercase	),
						        CenterCrop(_lowercase	),
						        ToTensor(),
						        normalize,
						    ]	)

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

						def val_transforms(_lowercase	):
												snake_case_ :List[str]						=			[_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:
																		snake_case_ :Any						=			(
																		    dataset["""train"""].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
																		)
												# Set the training transforms
												dataset["train"].set_transform(_lowercase	)

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

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

						# Training
						if training_args.do_train:
												snake_case_ :Optional[Any]						=			None
												if training_args.resume_from_checkpoint is not None:
																		snake_case_ :Optional[int]						=			training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		snake_case_ :Optional[Any]						=			last_checkpoint
												snake_case_ :Tuple						=			trainer.train(resume_from_checkpoint=_lowercase	)
												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:
												snake_case_ :Optional[int]						=			trainer.evaluate()
												trainer.log_metrics("""eval""",					_lowercase	)
												trainer.save_metrics("""eval""",					_lowercase	)

						# Write model card and (optionally) push to hub
						snake_case_ :Union[str, Any]						=			{
						    """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(**_lowercase	)
						else:
												trainer.create_model_card(**_lowercase	)


if __name__ == "__main__":
						main()





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						super().__init__()
						self.register_modules(unet=lowerCamelCase_		,       scheduler=lowerCamelCase_		)




	@torch.no_grad()
	def __call__(self		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = None		,       lowerCamelCase_ = 50		,       lowerCamelCase_ = "pil"		,       lowerCamelCase_ = True		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,       generator=lowerCamelCase_		,       )
						a					     =   image.to(self.device		)

						# set step values
						self.scheduler.set_timesteps(lowerCamelCase_		)

						for t in self.progress_bar(self.scheduler.timesteps		):
											# 1. predict noise model_output
											a					     =   self.unet(lowerCamelCase_		,       lowerCamelCase_		).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											a					     =   self.scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		).prev_sample

						a					     =   (image / 2 + 0.5).clamp(0		,       1		)
						a					     =   image.cpu().permute(0		,       2		,       3		,       1		).numpy()
						if output_type == "pil":
											a					     =   self.numpy_to_pil(lowerCamelCase_		)

						if not return_dict:
											return (image,), "This is a local test"

						return ImagePipelineOutput(images=lowerCamelCase_		), "This is a local test"
