
"""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
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  lowercase     (_lowerCAmelCase						):
						with open(_lowerCAmelCase ,    """rb"""						) as f:
												__lowerCAmelCase     =  Image.open(_lowerCAmelCase						)
												return im.convert("""RGB"""						)
@dataclass
class       lowerCAmelCase_		:



							'''simple docstring'''





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




							def    A__						( self				)       ->  Dict:
													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'''





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

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

def  lowercase     ():
						# 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     =  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     =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
						else:
												__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  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     =  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     =  None
						if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
												__lowerCAmelCase     =  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     =  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     =  {}
												if data_args.train_dir is not None:
																		__lowerCAmelCase     =  os.path.join(data_args.train_dir ,    """**"""						)
												if data_args.validation_dir is not None:
																		__lowerCAmelCase     =  os.path.join(data_args.validation_dir ,    """**"""						)
												__lowerCAmelCase     =  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     =  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     =  dataset["""train"""].train_test_split(data_args.train_val_split						)
												__lowerCAmelCase     =  split["""train"""]
												__lowerCAmelCase     =  split["""test"""]

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

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

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

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

						def val_transforms(_lowerCAmelCase						):
												__lowerCAmelCase     =  [_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     =  (
																		    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     =  (
																		    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     =  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     =  None
												if training_args.resume_from_checkpoint is not None:
																		__lowerCAmelCase     =  training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		__lowerCAmelCase     =  last_checkpoint
												__lowerCAmelCase     =  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     =  trainer.evaluate()
												trainer.log_metrics("""eval""" ,    _lowerCAmelCase						)
												trainer.save_metrics("""eval""" ,    _lowerCAmelCase						)

						# Write model card and (optionally) push to hub
						__lowerCAmelCase     =  {
						    """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"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_					     =	{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''RagModel''',
			    '''RagPreTrainedModel''',
			    '''RagSequenceForGeneration''',
			    '''RagTokenForGeneration''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''TFRagModel''',
			    '''TFRagPreTrainedModel''',
			    '''TFRagSequenceForGeneration''',
			    '''TFRagTokenForGeneration''',
			]


if TYPE_CHECKING:
			from .configuration_rag import RagConfig
			from .retrieval_rag import RagRetriever
			from .tokenization_rag import RagTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_rag import (
						    TFRagModel,
						    TFRagPreTrainedModel,
						    TFRagSequenceForGeneration,
						    TFRagTokenForGeneration,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE_					     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
