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__           =							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		:
							_UpperCAmelCase						    =     field(
							    default='cifar10'      ,   metadata={'help': 'Name of a dataset from the datasets package'}				)
							_UpperCAmelCase						    =     field(
							    default=UpperCamelCase_      ,   metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
							_UpperCAmelCase						    =     field(
							    default=UpperCamelCase_      ,   metadata={'help': 'The column name of the images in the files.'}				)
							_UpperCAmelCase						    =     field(default=UpperCamelCase_      ,   metadata={'help': 'A folder containing the training data.'}				)
							_UpperCAmelCase						    =     field(default=UpperCamelCase_      ,   metadata={'help': 'A folder containing the validation data.'}				)
							_UpperCAmelCase						    =     field(
							    default=0.15      ,   metadata={'help': 'Percent to split off of train for validation.'}				)
							_UpperCAmelCase						    =     field(
							    default=UpperCamelCase_      ,   metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of training examples to this '
							            'value if set.'
							        )
							    }      ,   )
							_UpperCAmelCase						    =     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     :    Tuple		):
										lowerCamelCase						:Optional[Any]						  =     {}
										if self.train_dir is not None:
													lowerCamelCase						:List[str]						  =     self.train_dir
										if self.validation_dir is not None:
													lowerCamelCase						:str						  =     self.validation_dir
										lowerCamelCase						:Union[str, Any]						  =     data_files if data_files else None


@dataclass
class   _lowerCAmelCase		:
							_UpperCAmelCase						    =     field(
							    default=UpperCamelCase_      ,   metadata={
							        'help': (
							            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
							        )
							    }      ,   )
							_UpperCAmelCase						    =     field(
							    default=UpperCamelCase_      ,   metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}				)
							_UpperCAmelCase						    =     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'
							        )
							    }      ,   )
							_UpperCAmelCase						    =     field(
							    default=UpperCamelCase_      ,   metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}				)
							_UpperCAmelCase						    =     field(
							    default='main'      ,   metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}      ,   )
							_UpperCAmelCase						    =     field(default=UpperCamelCase_      ,   metadata={'help': 'Name or path of preprocessor config.'}				)
							_UpperCAmelCase						    =     field(
							    default=UpperCamelCase_      ,   metadata={
							        'help': (
							            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
							            'with private models).'
							        )
							    }      ,   )
							_UpperCAmelCase						    =     field(
							    default=0.75      ,   metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}				)
							_UpperCAmelCase						    =     field(
							    default=UpperCamelCase_      ,   metadata={'help': 'Whether or not to train with normalized pixel values as target.'}				)


@dataclass
class   _lowerCAmelCase		(     UpperCamelCase_				):
							_UpperCAmelCase						    =     field(
							    default=1E-3      ,   metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}				)





def  _lowerCamelCase					(   a_				: int):
			lowerCamelCase						:Optional[int]						  =     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.

			lowerCamelCase						:List[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.
						lowerCamelCase						:Optional[int]						  =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
			else:
						lowerCamelCase						:Dict						  =     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'''						,			snake_case_						,			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()

			lowerCamelCase						:Optional[Any]						  =     training_args.get_process_log_level()
			logger.setLevel(snake_case_)
			transformers.utils.logging.set_verbosity(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.
			lowerCamelCase						:List[str]						  =     None
			if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
						lowerCamelCase						:Tuple						  =     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						: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.
			lowerCamelCase						:Any						  =     None if '''validation''' in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split						,			snake_case_) and data_args.train_val_split > 0.0:
						lowerCamelCase						:List[Any]						  =     ds['''train'''].train_test_split(data_args.train_val_split)
						lowerCamelCase						:int						  =     split['''train''']
						lowerCamelCase						:Optional[int]						  =     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						:Optional[int]						  =     {
			    '''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						:Dict						  =     ViTMAEConfig.from_pretrained(model_args.config_name						,			**snake_case_)
			elif model_args.model_name_or_path:
						lowerCamelCase						:Any						  =     ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,			**snake_case_)
			else:
						lowerCamelCase						:Any						  =     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						:List[str]						  =     ViTImageProcessor.from_pretrained(model_args.image_processor_name						,			**snake_case_)
			elif model_args.model_name_or_path:
						lowerCamelCase						:Optional[Any]						  =     ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,			**snake_case_)
			else:
						lowerCamelCase						:Any						  =     ViTImageProcessor()

			# create model
			if model_args.model_name_or_path:
						lowerCamelCase						:int						  =     ViTMAEForPreTraining.from_pretrained(
						    model_args.model_name_or_path						,			from_tf=bool('''.ckpt''' in model_args.model_name_or_path)						,			config=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''')
						lowerCamelCase						:Union[str, Any]						  =     ViTMAEForPreTraining(snake_case_)

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

			if data_args.image_column_name is not None:
						lowerCamelCase						:Optional[int]						  =     data_args.image_column_name
			elif "image" in column_names:
						lowerCamelCase						:List[Any]						  =     '''image'''
			elif "img" in column_names:
						lowerCamelCase						:Dict						  =     '''img'''
			else:
						lowerCamelCase						: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:
						lowerCamelCase						:Optional[int]						  =     image_processor.size['''shortest_edge''']
			else:
						lowerCamelCase						:Union[str, Any]						  =     (image_processor.size['''height'''], image_processor.size['''width'''])
			lowerCamelCase						:Union[str, Any]						  =     Compose(
			    [
			        Lambda(lambda a_: img.convert('''RGB''') if img.mode != "RGB" else img),
			        RandomResizedCrop(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(a_				: List[Any]):
						lowerCamelCase						:Any						  =     [transforms(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:
									lowerCamelCase						:Any						  =     ds['''train'''].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
						# Set the training transforms
						ds["train"].set_transform(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:
									lowerCamelCase						:Union[str, Any]						  =     (
									    ds['''validation'''].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
									)
						# Set the validation transforms
						ds["validation"].set_transform(snake_case_)

			# Compute absolute learning rate
			lowerCamelCase						:Dict						  =     (
			    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
			)
			if training_args.base_learning_rate is not None:
						lowerCamelCase						:int						  =     training_args.base_learning_rate * total_train_batch_size / 2_56

			# Initialize our trainer
			lowerCamelCase						:Dict						  =     Trainer(
			    model=snake_case_						,			args=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=snake_case_						,			data_collator=snake_case_						,			)

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

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





def  _lowerCamelCase					(   a_				: Tuple):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
			main()




import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:
			class   _lowerCAmelCase		:







										@staticmethod
										def 				snake_case  (				*__snake_case     :    str	,					**__snake_case     :    str		):
													pass
@is_pipeline_test
@require_vision
class   _lowerCAmelCase		(     unittest.TestCase				):







							@require_torch
							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Optional[int]						  =     pipeline(
										    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''	,					)
										lowerCamelCase						:List[str]						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Dict						  =     image_classifier(__snake_case	,					candidate_labels=['''a''', '''b''', '''c''']		)

										# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
										# python and torch versions.
										self.assertIn(
										    nested_simplify(__snake_case		)	,					[
										        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}],
										        [{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}],
										    ]	,					)

										lowerCamelCase						:Tuple						  =     image_classifier([image] * 5	,					candidate_labels=['''A''', '''B''', '''C''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										    ]	,					)







							@require_tf
							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Tuple						  =     pipeline(
										    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''	,					framework='''tf'''		)
										lowerCamelCase						:List[str]						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Optional[Any]						  =     image_classifier(__snake_case	,					candidate_labels=['''a''', '''b''', '''c''']		)

										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[{'''score''': 0.3_3_3, '''label''': '''a'''}, {'''score''': 0.3_3_3, '''label''': '''b'''}, {'''score''': 0.3_3_3, '''label''': '''c'''}]	,					)

										lowerCamelCase						:int						  =     image_classifier([image] * 5	,					candidate_labels=['''A''', '''B''', '''C''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										        [
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										            {'''score''': 0.3_3_3, '''label''': ANY(__snake_case		)},
										        ],
										    ]	,					)







							@slow
							@require_torch
							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:str						  =     pipeline(
										    task='''zero-shot-image-classification'''	,					model='''openai/clip-vit-base-patch32'''	,					)
										# This is an image of 2 cats with remotes and no planes
										lowerCamelCase						:List[str]						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Optional[Any]						  =     image_classifier(__snake_case	,					candidate_labels=['''cat''', '''plane''', '''remote''']		)

										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        {'''score''': 0.5_1_1, '''label''': '''remote'''},
										        {'''score''': 0.4_8_5, '''label''': '''cat'''},
										        {'''score''': 0.0_0_4, '''label''': '''plane'''},
										    ]	,					)

										lowerCamelCase						:Any						  =     image_classifier([image] * 5	,					candidate_labels=['''cat''', '''plane''', '''remote''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.5_1_1, '''label''': '''remote'''},
										            {'''score''': 0.4_8_5, '''label''': '''cat'''},
										            {'''score''': 0.0_0_4, '''label''': '''plane'''},
										        ],
										    ]
										    * 5	,					)







							@slow
							@require_tf
							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Union[str, Any]						  =     pipeline(
										    task='''zero-shot-image-classification'''	,					model='''openai/clip-vit-base-patch32'''	,					framework='''tf'''		)
										# This is an image of 2 cats with remotes and no planes
										lowerCamelCase						:Dict						  =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
										lowerCamelCase						:Dict						  =     image_classifier(__snake_case	,					candidate_labels=['''cat''', '''plane''', '''remote''']		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        {'''score''': 0.5_1_1, '''label''': '''remote'''},
										        {'''score''': 0.4_8_5, '''label''': '''cat'''},
										        {'''score''': 0.0_0_4, '''label''': '''plane'''},
										    ]	,					)

										lowerCamelCase						:Any						  =     image_classifier([image] * 5	,					candidate_labels=['''cat''', '''plane''', '''remote''']	,					batch_size=2		)
										self.assertEqual(
										    nested_simplify(__snake_case		)	,					[
										        [
										            {'''score''': 0.5_1_1, '''label''': '''remote'''},
										            {'''score''': 0.4_8_5, '''label''': '''cat'''},
										            {'''score''': 0.0_0_4, '''label''': '''plane'''},
										        ],
										    ]
										    * 5	,					)



