





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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-pretraining/requirements.txt""")


@dataclass
class       __lowerCAmelCase					:


	'''simple docstring'''





	_A													=				field(
	    default="cifar10"   ,    metadata={"help": "Name of a dataset from the datasets package"}      )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}      )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "The column name of the images in the files."}      )
	_A													=				field(default=_UpperCamelCase   ,    metadata={"help": "A folder containing the training data."}      )
	_A													=				field(default=_UpperCamelCase   ,    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=_UpperCamelCase   ,    metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    }   ,    )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
	            "value if set."
	        )
	    }   ,    )

	def    snake_case__(						self:						Any			):
			lowercase__					:      str      =		{}
			if self.train_dir is not None:
					lowercase__					:      Dict      =		self.train_dir
			if self.validation_dir is not None:
					lowercase__					:      str      =		self.validation_dir
			lowercase__					:      Optional[int]      =		data_files if data_files else None


@dataclass
class       __lowerCAmelCase					:


	'''simple docstring'''





	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={
	        "help": (
	            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
	        )
	    }   ,    )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}      )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={
	        "help": (
	            "Override some existing default config settings when a model is trained from scratch. Example: "
	            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
	        )
	    }   ,    )
	_A													=				field(
	    default=_UpperCamelCase   ,    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=_UpperCamelCase   ,    metadata={"help": "Name or path of preprocessor config."}      )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    }   ,    )
	_A													=				field(
	    default=0.75   ,    metadata={"help": "The ratio of the number of masked tokens in the input sequence."}      )
	_A													=				field(
	    default=_UpperCamelCase   ,    metadata={"help": "Whether or not to train with normalized pixel values as target."}      )


@dataclass
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				field(
	    default=1e-3   ,    metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}      )


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]			)   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		torch.stack([example['pixel_values'] for example in examples]			)
		return {"pixel_values": pixel_values}


def       SCREAMING_SNAKE_CASE__  (       )   ->							List[str]:




		'''simple docstring'''
		# 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.

		lowercase__					:      Optional[Any]      =		HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)			)
		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.
				lowercase__   ,				lowercase__   ,				lowercase__					:      Optional[int]      =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
		else:
				lowercase__   ,				lowercase__   ,				lowercase__					:      Optional[Any]      =		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_mae'   ,    _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()

		lowercase__					:      str      =		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.
		lowercase__					:      Tuple      =		None
		if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
				lowercase__					:      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.'			)

    # Initialize our dataset.
		lowercase__					:      str      =		load_dataset(
		    data_args.dataset_name   ,    data_args.dataset_config_name   ,    data_files=data_args.data_files   ,    cache_dir=model_args.cache_dir   ,    use_auth_token=True if model_args.use_auth_token else None   ,    )

		# If we don't have a validation split, split off a percentage of train as validation.
		lowercase__					:      str      =		None if 'validation' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split   ,    _lowercase			) and data_args.train_val_split > 0.0:
				lowercase__					:      Union[str, Any]      =		ds['train'].train_test_split(data_args.train_val_split			)
				lowercase__					:      Union[str, Any]      =		split['train']
				lowercase__					:      str      =		split['test']

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		lowercase__					:      Optional[Any]      =		{
		    'cache_dir': model_args.cache_dir,
		    'revision': model_args.model_revision,
		    'use_auth_token': True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				lowercase__					:      List[Any]      =		ViTMAEConfig.from_pretrained(model_args.config_name   ,    **_lowercase			)
		elif model_args.model_name_or_path:
				lowercase__					:      List[Any]      =		ViTMAEConfig.from_pretrained(model_args.model_name_or_path   ,    **_lowercase			)
		else:
				lowercase__					:      Dict      =		ViTMAEConfig()
				logger.warning('You are instantiating a new config instance from scratch.'			)
				if model_args.config_overrides is not None:
						logger.info(f"""Overriding config: {model_args.config_overrides}"""			)
						config.update_from_string(model_args.config_overrides			)
						logger.info(f"""New config: {config}"""			)

    # adapt config
		config.update(
		    {
		        'mask_ratio': model_args.mask_ratio,
		        'norm_pix_loss': model_args.norm_pix_loss,
		    }			)

		# create image processor
		if model_args.image_processor_name:
				lowercase__					:      Any      =		ViTImageProcessor.from_pretrained(model_args.image_processor_name   ,    **_lowercase			)
		elif model_args.model_name_or_path:
				lowercase__					:      Optional[int]      =		ViTImageProcessor.from_pretrained(model_args.model_name_or_path   ,    **_lowercase			)
		else:
				lowercase__					:      Any      =		ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				lowercase__					:      Optional[int]      =		ViTMAEForPreTraining.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   ,    )
		else:
				logger.info('Training new model from scratch'			)
				lowercase__					:      Optional[Any]      =		ViTMAEForPreTraining(_lowercase			)

		if training_args.do_train:
				lowercase__					:      Optional[Any]      =		ds['train'].column_names
		else:
				lowercase__					:      List[Any]      =		ds['validation'].column_names

		if data_args.image_column_name is not None:
				lowercase__					:      List[str]      =		data_args.image_column_name
		elif "image" in column_names:
				lowercase__					:      Any      =		'image'
		elif "img" in column_names:
				lowercase__					:      Any      =		'img'
		else:
				lowercase__					:      List[str]      =		column_names[0]

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				lowercase__					:      Union[str, Any]      =		image_processor.size['shortest_edge']
		else:
				lowercase__					:      str      =		(image_processor.size['height'], image_processor.size['width'])
		lowercase__					:      Dict      =		Compose(
		    [
		        Lambda(lambda _lowercase			: img.convert('RGB'			) if img.mode != "RGB" else img			),
		        RandomResizedCrop(_lowercase   ,    scale=(0.2, 1.0)   ,    interpolation=InterpolationMode.BICUBIC			),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean   ,    std=image_processor.image_std			),
		    ]			)

		def preprocess_images(_lowercase     : Union[str, Any]			):
				lowercase__					:      Optional[int]      =		[transforms(_lowercase			) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError('--do_train requires a train dataset'			)
				if data_args.max_train_samples is not None:
						lowercase__					:      str      =		ds['train'].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
				# Set the training transforms
				ds["train"].set_transform(_lowercase			)

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError('--do_eval requires a validation dataset'			)
				if data_args.max_eval_samples is not None:
						lowercase__					:      List[str]      =		(
						    ds['validation'].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
						)
				# Set the validation transforms
				ds["validation"].set_transform(_lowercase			)

		# Compute absolute learning rate
		lowercase__					:      str      =		(
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				lowercase__					:      Dict      =		training_args.base_learning_rate * total_train_batch_size / 256

		# Initialize our trainer
		lowercase__					:      Optional[int]      =		Trainer(
		    model=_lowercase   ,    args=_lowercase   ,    train_dataset=ds['train'] if training_args.do_train else None   ,    eval_dataset=ds['validation'] if training_args.do_eval else None   ,    tokenizer=_lowercase   ,    data_collator=_lowercase   ,    )

		# Training
		if training_args.do_train:
				lowercase__					:      List[Any]      =		None
				if training_args.resume_from_checkpoint is not None:
						lowercase__					:      Optional[int]      =		training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						lowercase__					:      Dict      =		last_checkpoint
				lowercase__					:      Dict      =		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:
				lowercase__					:      Any      =		trainer.evaluate()
				trainer.log_metrics('eval'   ,    _lowercase			)
				trainer.save_metrics('eval'   ,    _lowercase			)

		# Write model card and (optionally) push to hub
		lowercase__					:      List[Any]      =		{
		    'tasks': 'masked-auto-encoding',
		    'dataset': data_args.dataset_name,
		    'tags': ['masked-auto-encoding'],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**_lowercase			)
		else:
				trainer.create_model_card(**_lowercase			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[Any]			)   ->							List[str]:




		'''simple docstring'''
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
			main()









import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY


@is_pipeline_test
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	_A													=				MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
	_A													=				TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Optional[Any]			):
			lowercase__					:      int      =		AudioClassificationPipeline(model=lowerCamelCase_,					feature_extractor=lowerCamelCase_			)

			# test with a raw waveform
			lowercase__					:      Dict      =		np.zeros((34000,)			)
			lowercase__					:      Optional[int]      =		np.zeros((14000,)			)
			return audio_classifier, [audioa, audio]

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						str			):
			lowercase__   ,				lowercase__					:      List[str]      =		examples
			lowercase__					:      int      =		audio_classifier(lowerCamelCase_			)
			# by default a model is initialized with num_labels=2
			self.assertEqual(
			    lowerCamelCase_,					[
			        {'score': ANY(lowerCamelCase_			), 'label': ANY(lowerCamelCase_			)},
			        {'score': ANY(lowerCamelCase_			), 'label': ANY(lowerCamelCase_			)},
			    ],					)
			lowercase__					:      List[Any]      =		audio_classifier(lowerCamelCase_,					top_k=1			)
			self.assertEqual(
			    lowerCamelCase_,					[
			        {'score': ANY(lowerCamelCase_			), 'label': ANY(lowerCamelCase_			)},
			    ],					)

			self.run_torchaudio(lowerCamelCase_			)

	@require_torchaudio
	def    snake_case__(						self:						Dict,					lowerCamelCase_:						str			):
			import datasets

			# test with a local file
			lowercase__					:      Optional[Any]      =		datasets.load_dataset('hf-internal-testing/librispeech_asr_dummy',					'clean',					split='validation'			)
			lowercase__					:      int      =		dataset[0]['audio']['array']
			lowercase__					:      Tuple      =		audio_classifier(lowerCamelCase_			)
			self.assertEqual(
			    lowerCamelCase_,					[
			        {'score': ANY(lowerCamelCase_			), 'label': ANY(lowerCamelCase_			)},
			        {'score': ANY(lowerCamelCase_			), 'label': ANY(lowerCamelCase_			)},
			    ],					)

	@require_torch
	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      List[str]      =		'anton-l/wav2vec2-random-tiny-classifier'

			lowercase__					:      Tuple      =		pipeline('audio-classification',					model=lowerCamelCase_			)

			lowercase__					:      List[str]      =		np.ones((8000,)			)
			lowercase__					:      List[Any]      =		audio_classifier(lowerCamelCase_,					top_k=4			)

			lowercase__					:      List[str]      =		[
			    {'score': 0.0_8_4_2, 'label': 'no'},
			    {'score': 0.0_8_3_8, 'label': 'up'},
			    {'score': 0.0_8_3_7, 'label': 'go'},
			    {'score': 0.0_8_3_4, 'label': 'right'},
			]
			lowercase__					:      Optional[int]      =		[
			    {'score': 0.0_8_4_5, 'label': 'stop'},
			    {'score': 0.0_8_4_4, 'label': 'on'},
			    {'score': 0.0_8_4_1, 'label': 'right'},
			    {'score': 0.0_8_3_4, 'label': 'left'},
			]
			self.assertIn(nested_simplify(lowerCamelCase_,					decimals=4			),					[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]			)

			lowercase__					:      Tuple      =		{'array': np.ones((8000,)			), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
			lowercase__					:      Any      =		audio_classifier(lowerCamelCase_,					top_k=4			)
			self.assertIn(nested_simplify(lowerCamelCase_,					decimals=4			),					[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]			)

	@require_torch
	@slow
	def    snake_case__(						self:						Optional[int]			):
			import datasets

			lowercase__					:      str      =		'superb/wav2vec2-base-superb-ks'

			lowercase__					:      Optional[int]      =		pipeline('audio-classification',					model=lowerCamelCase_			)
			lowercase__					:      List[Any]      =		datasets.load_dataset('anton-l/superb_dummy',					'ks',					split='test'			)

			lowercase__					:      Optional[Any]      =		np.array(dataset[3]['speech'],					dtype=np.floataa			)
			lowercase__					:      Optional[int]      =		audio_classifier(lowerCamelCase_,					top_k=4			)
			self.assertEqual(
			    nested_simplify(lowerCamelCase_,					decimals=3			),					[
			        {'score': 0.9_8_1, 'label': 'go'},
			        {'score': 0.0_0_7, 'label': 'up'},
			        {'score': 0.0_0_6, 'label': '_unknown_'},
			        {'score': 0.0_0_1, 'label': 'down'},
			    ],					)





	@require_tf
	@unittest.skip('Audio classification is not implemented for TF'			)
	def    snake_case__(						self:						int			):
			pass


