
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 		lowercase_    (     A__	)							->							Union[str, Any]:
				"""simple docstring"""

				with open(A__				,	"rb"	) as f:
								snake_case				  =      Image.open(A__	)
								return im.convert("RGB"	)







@dataclass
class   lowerCamelCase	:
					UpperCAmelCase__       :							Optional[str]     =	field(
					    default=A_							,				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=A_							,				metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
					UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "A folder containing the training data."}						)
					UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				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=A_							,				metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of training examples to this "
					            "value if set."
					        )
					    }							,				)
					UpperCAmelCase__       :							Optional[int]     =	field(
					    default=A_							,				metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
					            "value if set."
					        )
					    }							,				)





					def 	UpperCAmelCase(self   :					str     )		->		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   lowerCamelCase	:
					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=A_							,				metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(A_						)}							,				)
					UpperCAmelCase__       :							Optional[str]     =	field(
					    default=A_							,				metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
					UpperCAmelCase__       :							Optional[str]     =	field(
					    default=A_							,				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=A_							,				metadata={"help": "Name or path of preprocessor config."}						)
					UpperCAmelCase__       :							bool     =	field(
					    default=A_							,				metadata={
					        "help": (
					            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
					            "with private models)."
					        )
					    }							,				)
					UpperCAmelCase__       :							bool     =	field(
					    default=A_							,				metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}							,				)



def 		lowercase_    (     A__	)							->							Dict:
				"""simple docstring"""

				snake_case				  =      torch.stack([example["pixel_values"] for example in examples]	)
				snake_case				  =      torch.tensor([example["labels"] for example in examples]	)
				return {"pixel_values": pixel_values, "labels": labels}



def 		lowercase_    (     )							->							List[str]:
				"""simple docstring"""

				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.
								snake_case  ,							snake_case  ,							snake_case				  =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
				else:
								snake_case  ,							snake_case  ,							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"				,	A__				,	A__	)

				# 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				  =      training_args.get_process_log_level()
				logger.setLevel(A__	)
				transformers.utils.logging.set_verbosity(A__	)
				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				  =      None
				if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
								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:
								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:
								snake_case				  =      {}
								if data_args.train_dir is not None:
												snake_case				  =      os.path.join(data_args.train_dir				,	"**"	)
								if data_args.validation_dir is not None:
												snake_case				  =      os.path.join(data_args.validation_dir				,	"**"	)
								snake_case				  =      load_dataset(
								    "imagefolder"				,	data_files=A__				,	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				  =      None if "validation" in dataset.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split				,	A__	) and data_args.train_val_split > 0.0:
								snake_case				  =      dataset["train"].train_test_split(data_args.train_val_split	)
								snake_case				  =      split["train"]
								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.
				snake_case				  =      dataset["train"].features["labels"].names
				snake_case  ,							snake_case				  =      {}, {}
				for i, label in enumerate(A__	):
								snake_case				  =      str(A__	)
								snake_case				  =      label

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

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

				def train_transforms(A__	):
								snake_case				  =      [
								    _train_transforms(pil_img.convert("RGB"	)	) for pil_img in example_batch["image"]
								]
								return example_batch

				def val_transforms(A__	):
								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:
												snake_case				  =      (
												    dataset["train"].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
												)
								# Set the training transforms
								dataset["train"].set_transform(A__	)

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

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

				# Training
				if training_args.do_train:
								snake_case				  =      None
								if training_args.resume_from_checkpoint is not None:
												snake_case				  =      training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												snake_case				  =      last_checkpoint
								snake_case				  =      trainer.train(resume_from_checkpoint=A__	)
								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				  =      trainer.evaluate()
								trainer.log_metrics("eval"				,	A__	)
								trainer.save_metrics("eval"				,	A__	)

				# Write model card and (optionally) push to hub
				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(**A__	)
				else:
								trainer.create_model_card(**A__	)


if __name__ == "__main__":
			main()



def 		lowercase_    (     A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      [0 for i in range(r + 1	)]
    # nc0 = 1
    snake_case				  =      1
    for i in range(1				,	n + 1	):
        # to compute current row from previous row.
        snake_case				  =      min(A__				,	A__	)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]





print(binomial_coefficient(n=10, r=5))

