






"""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__	:    Optional[Any]			 = 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[str]			 = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
A__	:    Tuple			 = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def 			_snake_case    (					lowerCamelCase__     :   str  )    ->     List[Any]:
							with open(lowerCamelCase__    ,						"rb"  ) as f:
														lowerCamelCase_	:							List[Any]									=Image.open(lowerCamelCase__  )
														return im.convert("RGB"  )







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


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


def 			_snake_case    (					lowerCamelCase__     :   Union[str, Any]  )    ->     str:
							lowerCamelCase_	:							Optional[int]									=torch.stack([example["pixel_values"] for example in examples]  )
							lowerCamelCase_	:							Tuple									=torch.tensor([example["labels"] for example in examples]  )
							return {"pixel_values": pixel_values, "labels": labels}

def 			_snake_case    (					)    ->     Tuple:
							# 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_	:							Union[str, 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.
														lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							int									=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
							else:
														lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							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"    ,						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()

							lowerCamelCase_	:							Dict									=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.
							lowerCamelCase_	:							List[Any]									=None
							if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
														lowerCamelCase_	:							List[str]									=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_	:							List[str]									=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_	:							Optional[Any]									={}
														if data_args.train_dir is not None:
																					lowerCamelCase_	:							Dict									=os.path.join(data_args.train_dir    ,						"**"  )
														if data_args.validation_dir is not None:
																					lowerCamelCase_	:							Optional[int]									=os.path.join(data_args.validation_dir    ,						"**"  )
														lowerCamelCase_	:							int									=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.
							lowerCamelCase_	:							Dict									=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:
														lowerCamelCase_	:							str									=dataset["train"].train_test_split(data_args.train_val_split  )
														lowerCamelCase_	:							Any									=split["train"]
														lowerCamelCase_	:							List[Any]									=split["test"]

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

							# Load the accuracy metric from the datasets package
							lowerCamelCase_	:							List[str]									=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__     :   Dict  ):
														return metric.compute(predictions=np.argmax(p.predictions    ,						axis=1  )    ,						references=p.label_ids  )

							lowerCamelCase_	:							Union[str, Any]									=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    ,						)
							lowerCamelCase_	:							List[str]									=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    ,						)
							lowerCamelCase_	:							List[str]									=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_	:							List[str]									=image_processor.size["shortest_edge"]
							else:
														lowerCamelCase_	:							Optional[Any]									=(image_processor.size["height"], image_processor.size["width"])
							lowerCamelCase_	:							int									=Normalize(mean=image_processor.image_mean    ,						std=image_processor.image_std  )
							lowerCamelCase_	:							List[Any]									=Compose(
							    [
							        RandomResizedCrop(lowerCamelCase__  ),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ]  )
							lowerCamelCase_	:							int									=Compose(
							    [
							        Resize(lowerCamelCase__  ),
							        CenterCrop(lowerCamelCase__  ),
							        ToTensor(),
							        normalize,
							    ]  )

							def train_transforms(lowerCamelCase__     :   Union[str, Any]  ):
														lowerCamelCase_	:							int									=[
														    _train_transforms(pil_img.convert("RGB"  )  ) for pil_img in example_batch["image"]
														]
														return example_batch

							def val_transforms(lowerCamelCase__     :   List[Any]  ):
														lowerCamelCase_	:							Dict									=[_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_	:							str									=(
																					    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:
																					lowerCamelCase_	:							Any									=(
																					    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
							lowerCamelCase_	:							Optional[Any]									=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:
														lowerCamelCase_	:							Any									=None
														if training_args.resume_from_checkpoint is not None:
																					lowerCamelCase_	:							Union[str, Any]									=training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					lowerCamelCase_	:							Optional[Any]									=last_checkpoint
														lowerCamelCase_	:							Any									=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:
														lowerCamelCase_	:							str									=trainer.evaluate()
														trainer.log_metrics("eval"    ,						lowerCamelCase__  )
														trainer.save_metrics("eval"    ,						lowerCamelCase__  )

							# Write model card and (optionally) push to hub
							lowerCamelCase_	:							Dict									={
							    "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 warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


A__	:    str			 = logging.get_logger(__name__)







class        lowercase__      (							snake_case__       ):


			def __init__(				self					:  Optional[Any]       ,	*snake_case__					:  int       ,	**snake_case__					:  Any ):
										warnings.warn(
										    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use PerceiverImageProcessor instead."       ,	snake_case__       ,	)
										super().__init__(*snake_case__       ,	**snake_case__ )



