



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
__SCREAMING_SNAKE_CASE           =  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""")

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




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			with open(_lowerCamelCase ,    "rb"    ) as f:
						A							:					str              =						Image.open(_lowerCamelCase    )
						return im.convert("RGB"    )

@dataclass
class 	lowerCamelCase_    :






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







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







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




def 	UpperCAmelCase   (						):
			# 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.

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

			A							:					int              =						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.
			A							:					Optional[int]              =						None
			if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
						A							:					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:
						A							:					Union[str, 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:
						A							:					Union[str, Any]              =						{}
						if data_args.train_dir is not None:
									A							:					Union[str, Any]              =						os.path.join(data_args.train_dir ,    "**"    )
						if data_args.validation_dir is not None:
									A							:					Optional[Any]              =						os.path.join(data_args.validation_dir ,    "**"    )
						A							:					Any              =						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.
			A							:					Union[str, Any]              =						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:
						A							:					int              =						dataset["train"].train_test_split(data_args.train_val_split    )
						A							:					Tuple              =						split["train"]
						A							:					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.
			A							:					str              =						dataset["train"].features["labels"].names
			A       , A							:					int              =						{}, {}
			for i, label in enumerate(_lowerCamelCase    ):
						A							:					Union[str, Any]              =						str(_lowerCamelCase    )
						A							:					Dict              =						label

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

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

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

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

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


if __name__ == "__main__":
							main()




import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer

@require_tf
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Optional[int]:
									self.assertEqual(len(__lowerCamelCase )	,	len(__lowerCamelCase ) )
									for a, b in zip(__lowerCamelCase	,	__lowerCamelCase ):
												self.assertAlmostEqual(__lowerCamelCase	,	__lowerCamelCase	,	delta=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[Any]:
									A							:					List[Any]              =						GradientAccumulator()
									accumulator([tf.constant([1.0, 2.0] )] )
									accumulator([tf.constant([-2.0, 1.0] )] )
									accumulator([tf.constant([-1.0, 2.0] )] )
									with self.assertRaises(__lowerCamelCase ):
												accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
									self.assertEqual(accumulator.step	,	3 )
									self.assertEqual(len(accumulator.gradients )	,	1 )
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()	,	[-2.0, 5.0]	,	tol=1e-2 )
									accumulator.reset()
									self.assertEqual(accumulator.step	,	0 )
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()	,	[0.0, 0.0]	,	tol=1e-2 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									A							:					Union[str, Any]              =						None
									ops.enable_eager_execution_internal()
									A							:					Tuple              =						tf.config.list_physical_devices("CPU" )
									if len(__lowerCamelCase ) == 1:
												tf.config.set_logical_device_configuration(
												    physical_devices[0]	,	[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
									A							:					Dict              =						tf.config.list_logical_devices(device_type="CPU" )
									A							:					List[str]              =						tf.distribute.MirroredStrategy(devices=devices[:2] )

									with strategy.scope():
												A							:					Optional[int]              =						GradientAccumulator()
												A							:					Tuple              =						tf.Variable([4.0, 3.0] )
												A       , A							:					List[Any]              =						create_optimizer(5e-5	,	10	,	5 )
												A							:					List[str]              =						tf.Variable([0.0, 0.0]	,	trainable=__lowerCamelCase )

									def accumulate_on_replica(__lowerCamelCase      :			Tuple ):
												accumulator([gradient] )

									def apply_on_replica():
												optimizer.apply_gradients(list(zip(accumulator.gradients	,	[variable] ) ) )

									@tf.function
									def accumulate(__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Optional[int] ):
												with strategy.scope():
															A							:					int              =						strategy.experimental_local_results(__lowerCamelCase )
															local_variables[0].assign(__lowerCamelCase )
															local_variables[1].assign(__lowerCamelCase )
															strategy.run(__lowerCamelCase	,	args=(gradient_placeholder,) )

									@tf.function
									def apply_grad():
												with strategy.scope():
															strategy.run(__lowerCamelCase )

									def _check_local_values(__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			List[Any] ):
												A							:					Optional[int]              =						strategy.experimental_local_results(accumulator._gradients[0] )
												self.assertListAlmostEqual(values[0].value()	,	__lowerCamelCase	,	tol=1e-2 )
												self.assertListAlmostEqual(values[1].value()	,	__lowerCamelCase	,	tol=1e-2 )

									accumulate([1.0, 2.0]	,	[-1.0, 1.0] )
									accumulate([3.0, -1.0]	,	[-1.0, -1.0] )
									accumulate([-2.0, 2.0]	,	[3.0, -2.0] )
									self.assertEqual(accumulator.step	,	3 )
									_check_local_values([2.0, 3.0]	,	[1.0, -2.0] )
									apply_grad()
									self.assertListAlmostEqual(variable.value()	,	[4.0, 3.0]	,	tol=1e-2 )
									accumulator.reset()
									self.assertEqual(accumulator.step	,	0 )
									_check_local_values([0.0, 0.0]	,	[0.0, 0.0] )