



"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}
class 			_snake_case     (   _UpperCamelCase):
				UpperCamelCase__			:   str       						='audio-spectrogram-transformer'




				def __init__(  self   :  Dict,   __lowercase   :  Dict=768,   __lowercase   :  Optional[Any]=12,   __lowercase   :  int=12,   __lowercase   :  Tuple=3072,   __lowercase   :  Dict="gelu",   __lowercase   :  List[str]=0.0,   __lowercase   :  str=0.0,   __lowercase   :  Optional[Any]=0.02,   __lowercase   :  Optional[Any]=1e-1_2,   __lowercase   :  str=16,   __lowercase   :  Dict=True,   __lowercase   :  Any=10,   __lowercase   :  Tuple=10,   __lowercase   :  Any=1024,   __lowercase   :  Tuple=128,   **__lowercase   :  List[Any],   ):
							super().__init__(**_UpperCAmelCase  )

							lowercase__   					=			hidden_size
							lowercase__   					=			num_hidden_layers
							lowercase__   					=			num_attention_heads
							lowercase__   					=			intermediate_size
							lowercase__   					=			hidden_act
							lowercase__   					=			hidden_dropout_prob
							lowercase__   					=			attention_probs_dropout_prob
							lowercase__   					=			initializer_range
							lowercase__   					=			layer_norm_eps
							lowercase__   					=			patch_size
							lowercase__   					=			qkv_bias
							lowercase__   					=			frequency_stride
							lowercase__   					=			time_stride
							lowercase__   					=			max_length
							lowercase__   					=			num_mel_bins









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




				def 							A__ (  self   :  Tuple  ):
							lowercase__   					=			{}
							if self.train_dir is not None:
										lowercase__   					=			self.train_dir
							if self.validation_dir is not None:
										lowercase__   					=			self.validation_dir
							lowercase__   					=			data_files if data_files else None
@dataclass
class 			_snake_case     :
				UpperCamelCase__			:   str       						=field(
				    default=lowercase__      ,		metadata={
				        """help""": (
				            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
				        )
				    }      ,		)
				UpperCamelCase__			:   Optional[str]       						=field(
				    default=lowercase__      ,		metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""})
				UpperCamelCase__			:   Optional[str]       						=field(
				    default=lowercase__      ,		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"""
				        )
				    }      ,		)
				UpperCamelCase__			:   Optional[str]       						=field(
				    default=lowercase__      ,		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=lowercase__      ,		metadata={"""help""": """Name or path of preprocessor config."""})
				UpperCamelCase__			:   bool       						=field(
				    default=lowercase__      ,		metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }      ,		)
				UpperCamelCase__			:   float       						=field(
				    default=0.75      ,		metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""})
				UpperCamelCase__			:   bool       						=field(
				    default=lowercase__      ,		metadata={"""help""": """Whether or not to train with normalized pixel values as target."""})
@dataclass
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   float       						=field(
				    default=1E-3      ,		metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""})



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			torch.stack([example["pixel_values"] for example in examples]					)
			return {"pixel_values": pixel_values}



def __lowerCAmelCase	(       ):
			# 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__   					=			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__   					=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
			else:
						lowercase__					,    lowercase__					,    lowercase__   					=			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"  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			# 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__   					=			training_args.get_process_log_level()
			logger.setLevel(SCREAMING_SNAKE_CASE_					)
			transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE_					)
			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__   					=			None
			if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
						lowercase__   					=			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__   					=			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__   					=			None if "validation" in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split  ,					SCREAMING_SNAKE_CASE_					) and data_args.train_val_split > 0.0:
						lowercase__   					=			ds["train"].train_test_split(data_args.train_val_split					)
						lowercase__   					=			split["train"]
						lowercase__   					=			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__   					=			{
			    "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__   					=			ViTMAEConfig.from_pretrained(model_args.config_name  ,					**SCREAMING_SNAKE_CASE_					)
			elif model_args.model_name_or_path:
						lowercase__   					=			ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,					**SCREAMING_SNAKE_CASE_					)
			else:
						lowercase__   					=			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__   					=			ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,					**SCREAMING_SNAKE_CASE_					)
			elif model_args.model_name_or_path:
						lowercase__   					=			ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,					**SCREAMING_SNAKE_CASE_					)
			else:
						lowercase__   					=			ViTImageProcessor()

			# create model
			if model_args.model_name_or_path:
						lowercase__   					=			ViTMAEForPreTraining.from_pretrained(
						    model_args.model_name_or_path  ,					from_tf=bool(".ckpt" in model_args.model_name_or_path					)  ,					config=SCREAMING_SNAKE_CASE_  ,					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__   					=			ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_					)

			if training_args.do_train:
						lowercase__   					=			ds["train"].column_names
			else:
						lowercase__   					=			ds["validation"].column_names

			if data_args.image_column_name is not None:
						lowercase__   					=			data_args.image_column_name
			elif "image" in column_names:
						lowercase__   					=			"image"
			elif "img" in column_names:
						lowercase__   					=			"img"
			else:
						lowercase__   					=			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__   					=			image_processor.size["shortest_edge"]
			else:
						lowercase__   					=			(image_processor.size["height"], image_processor.size["width"])
			lowercase__   					=			Compose(
			    [
			        Lambda(lambda SCREAMING_SNAKE_CASE_					: img.convert("RGB"					) if img.mode != "RGB" else img					),
			        RandomResizedCrop(SCREAMING_SNAKE_CASE_  ,					scale=(0.2, 1.0)  ,					interpolation=InterpolationMode.BICUBIC					),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean  ,					std=image_processor.image_std					),
			    ]					)

			def preprocess_images(SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			[transforms(SCREAMING_SNAKE_CASE_					) 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__   					=			ds["train"].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
						# Set the training transforms
						ds["train"].set_transform(SCREAMING_SNAKE_CASE_					)

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

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

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

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

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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()




