
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 :			Tuple              =	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 :			str              =	list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_UpperCAmelCase :			Optional[int]              =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


def 				A	( lowercase			)       ->  List[Any]:
		'''simple docstring'''

		with open(lowercase   ,	'rb'			) as f:
				UpperCamelCase          =					Image.open(lowercase			)
				return im.convert('RGB'			)


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



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






def 				A	( lowercase			)       ->  str:
		'''simple docstring'''

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


def 				A	( )       ->  str:
		'''simple docstring'''

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

		UpperCamelCase          =					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.
		UpperCamelCase          =					None
		if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
				UpperCamelCase          =					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:
				UpperCamelCase          =					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:
				UpperCamelCase          =					{}
				if data_args.train_dir is not None:
						UpperCamelCase          =					os.path.join(data_args.train_dir   ,	'**'			)
				if data_args.validation_dir is not None:
						UpperCamelCase          =					os.path.join(data_args.validation_dir   ,	'**'			)
				UpperCamelCase          =					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.
		UpperCamelCase          =					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:
				UpperCamelCase          =					dataset['train'].train_test_split(data_args.train_val_split			)
				UpperCamelCase          =					split['train']
				UpperCamelCase          =					split['test']

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

		# Load the accuracy metric from the datasets package
		UpperCamelCase          =					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			)

		UpperCamelCase          =					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   ,	)
		UpperCamelCase          =					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   ,	)
		UpperCamelCase          =					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:
				UpperCamelCase          =					image_processor.size['shortest_edge']
		else:
				UpperCamelCase          =					(image_processor.size['height'], image_processor.size['width'])
		UpperCamelCase          =					Normalize(mean=image_processor.image_mean   ,	std=image_processor.image_std			)
		UpperCamelCase          =					Compose(
		    [
		        RandomResizedCrop(lowercase			),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        normalize,
		    ]			)
		UpperCamelCase          =					Compose(
		    [
		        Resize(lowercase			),
		        CenterCrop(lowercase			),
		        ToTensor(),
		        normalize,
		    ]			)

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

		def val_transforms(lowercase			):
				UpperCamelCase          =					[_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:
						UpperCamelCase          =					(
						    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:
						UpperCamelCase          =					(
						    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
		UpperCamelCase          =					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:
				UpperCamelCase          =					None
				if training_args.resume_from_checkpoint is not None:
						UpperCamelCase          =					training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						UpperCamelCase          =					last_checkpoint
				UpperCamelCase          =					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:
				UpperCamelCase          =					trainer.evaluate()
				trainer.log_metrics('eval'   ,	lowercase			)
				trainer.save_metrics('eval'   ,	lowercase			)

		# Write model card and (optionally) push to hub
		UpperCamelCase          =					{
		    '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()


import os
import sys
import unittest


_UpperCAmelCase :			str              =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_UpperCAmelCase :			Tuple              =	os.path.join(git_repo_path, "src", "diffusers")


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					find_backend('    if not is_torch_available():'	)
				self.assertEqual(A_    ,				'torch'	)

				# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
				# self.assertEqual(backend_with_underscore, "tensorflow_text")

				UpperCamelCase          =					find_backend('    if not (is_torch_available() and is_transformers_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers'	)

				# double_backend_with_underscore = find_backend(
				#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
				# )
				# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

				UpperCamelCase          =					find_backend(
				    '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers_and_onnx'	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					read_init()
				# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
				self.assertIn('torch'    ,				A_	)
				self.assertIn('torch_and_transformers'    ,				A_	)
				self.assertIn('flax_and_transformers'    ,				A_	)
				self.assertIn('torch_and_transformers_and_onnx'    ,				A_	)

				# Likewise, we can't assert on the exact content of a key
				self.assertIn('UNet2DModel'    ,				objects['torch']	)
				self.assertIn('FlaxUNet2DConditionModel'    ,				objects['flax']	)
				self.assertIn('StableDiffusionPipeline'    ,				objects['torch_and_transformers']	)
				self.assertIn('FlaxStableDiffusionPipeline'    ,				objects['flax_and_transformers']	)
				self.assertIn('LMSDiscreteScheduler'    ,				objects['torch_and_scipy']	)
				self.assertIn('OnnxStableDiffusionPipeline'    ,				objects['torch_and_transformers_and_onnx']	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					create_dummy_object('CONSTANT'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				'\nCONSTANT = None\n'	)

				UpperCamelCase          =					create_dummy_object('function'    ,				'\'torch\''	)
				self.assertEqual(
				    A_    ,				'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'	)

				UpperCamelCase          =					'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
				UpperCamelCase          =					create_dummy_object('FakeClass'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n'
				UpperCamelCase          =					create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}	)
				self.assertEqual(dummy_files['torch']    ,				A_	)
