


"""simple docstring"""


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
A  :     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 						_UpperCamelCase						:
							'''simple docstring'''







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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    {}
										if self.train_dir is not None:
													__lowerCAmelCase       					=    self.train_dir
										if self.validation_dir is not None:
													__lowerCAmelCase       					=    self.validation_dir
										__lowerCAmelCase       					=    data_files if data_files else None
@dataclass
class 						_UpperCamelCase						:
							'''simple docstring'''







							__UpperCAmelCase		:					str          =field(
							    default=lowerCAmelCase__ ,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=lowerCAmelCase__ ,metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}	)
							__UpperCAmelCase		:					Optional[str]          =field(
							    default=lowerCAmelCase__ ,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=lowerCAmelCase__ ,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=lowerCAmelCase__ ,metadata={"""help""": """Name or path of preprocessor config."""}	)
							__UpperCAmelCase		:					bool          =field(
							    default=lowerCAmelCase__ ,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=lowerCAmelCase__ ,metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}	)
@dataclass
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







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


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




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


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    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.
						__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_mae"					,				_UpperCamelCase					,				_UpperCamelCase   )

			# 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(_UpperCamelCase   )
			transformers.utils.logging.set_verbosity(_UpperCamelCase   )
			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."   )

    # Initialize our dataset.
			__lowerCAmelCase       					=    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.
			__lowerCAmelCase       					=    None if "validation" in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split					,				_UpperCamelCase   ) and data_args.train_val_split > 0.0:
						__lowerCAmelCase       					=    ds["train"].train_test_split(data_args.train_val_split   )
						__lowerCAmelCase       					=    split["train"]
						__lowerCAmelCase       					=    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.
			__lowerCAmelCase       					=    {
			    "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:
						__lowerCAmelCase       					=    ViTMAEConfig.from_pretrained(model_args.config_name					,				**_UpperCamelCase   )
			elif model_args.model_name_or_path:
						__lowerCAmelCase       					=    ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,				**_UpperCamelCase   )
			else:
						__lowerCAmelCase       					=    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:
						__lowerCAmelCase       					=    ViTImageProcessor.from_pretrained(model_args.image_processor_name					,				**_UpperCamelCase   )
			elif model_args.model_name_or_path:
						__lowerCAmelCase       					=    ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,				**_UpperCamelCase   )
			else:
						__lowerCAmelCase       					=    ViTImageProcessor()

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

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

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

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

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

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

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

			# 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=_UpperCamelCase   )
						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"					,				_UpperCamelCase   )
						trainer.save_metrics("eval"					,				_UpperCamelCase   )

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


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			main()


if __name__ == "__main__":
	main()








"""simple docstring"""


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self					,     __a			):
										for model_result in results.values():
													for batch_size, sequence_length in zip(model_result["bs"]					,     model_result["ss"]			):
																__lowerCAmelCase       					=    model_result["result"][batch_size][sequence_length]
																self.assertIsNotNone(__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sgugger/tiny-distilbert-classification"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     only_pretrain_model=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     eager_mode=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result			)
										self.check_results_dict_not_empty(results.memory_train_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     [config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result			)
										self.check_results_dict_not_empty(results.memory_train_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "patrickvonplaten/t5-tiny-random"
										__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a			)
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a					,     configs=[config]			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"			)			) == 0					,     "Cannot do xla on CPU."			)
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]					,     training=__a					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     use_xla=__a					,     multi_process=__a					,     )
										__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
										__lowerCAmelCase       					=    benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result			)
										self.check_results_dict_not_empty(results.memory_inference_result			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"
										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]					,     inference=__a					,     save_to_csv=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     inference_time_csv_file=os.path.join(__a					,     "inf_time.csv"			)					,     inference_memory_csv_file=os.path.join(__a					,     "inf_mem.csv"			)					,     env_info_csv_file=os.path.join(__a					,     "env.csv"			)					,     multi_process=__a					,     )
													__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
													benchmark.run()
													self.assertTrue(Path(os.path.join(__a					,     "inf_time.csv"			)			).exists()			)
													self.assertTrue(Path(os.path.join(__a					,     "inf_mem.csv"			)			).exists()			)
													self.assertTrue(Path(os.path.join(__a					,     "env.csv"			)			).exists()			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "sshleifer/tiny-gpt2"

										def _check_summary_is_not_empty(__a			):
													self.assertTrue(hasattr(__a					,     "sequential"			)			)
													self.assertTrue(hasattr(__a					,     "cumulative"			)			)
													self.assertTrue(hasattr(__a					,     "current"			)			)
													self.assertTrue(hasattr(__a					,     "total"			)			)

										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase       					=    TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]					,     inference=__a					,     sequence_lengths=[8]					,     batch_sizes=[1]					,     log_filename=os.path.join(__a					,     "log.txt"			)					,     log_print=__a					,     trace_memory_line_by_line=__a					,     eager_mode=__a					,     multi_process=__a					,     )
													__lowerCAmelCase       					=    TensorFlowBenchmark(__a			)
													__lowerCAmelCase       					=    benchmark.run()
													_check_summary_is_not_empty(result.inference_summary			)
													self.assertTrue(Path(os.path.join(__a					,     "log.txt"			)			).exists()			)




