"""simple docstring"""




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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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
__lowerCamelCase							 =     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")

__lowerCamelCase							 =     list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__lowerCamelCase							 =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 			UpperCamelCase__:
						lowerCAmelCase__     :  Optional[str]  							=						field(
						    default='cifar10'						,  metadata={'help': 'Name of a dataset from the datasets package'}       )
						lowerCAmelCase__     :  Optional[str]  							=						field(
						    default=__A						,  metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}       )
						lowerCAmelCase__     :  Optional[str]  							=						field(
						    default=__A						,  metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}						,  )
						lowerCAmelCase__     :  Optional[str]  							=						field(default=__A						,  metadata={'help': 'A folder containing the training data.'}       )
						lowerCAmelCase__     :  Optional[str]  							=						field(default=__A						,  metadata={'help': 'A folder containing the validation data.'}       )
						lowerCAmelCase__     :  Optional[float]  							=						field(
						    default=0.15						,  metadata={'help': 'Percent to split off of train for validation.'}       )
						lowerCAmelCase__     :  int  							=						field(default=32						,  metadata={'help': 'The size of the square patches to use for masking.'}       )
						lowerCAmelCase__     :  float  							=						field(
						    default=0.6						,  metadata={'help': 'Percentage of patches to mask.'}						,  )
						lowerCAmelCase__     :  Optional[int]  							=						field(
						    default=__A						,  metadata={
						        'help': (
						            'For debugging purposes or quicker training, truncate the number of training examples to this '
						            'value if set.'
						        )
						    }						,  )
						lowerCAmelCase__     :  Optional[int]  							=						field(
						    default=__A						,  metadata={
						        'help': (
						            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
						            'value if set.'
						        )
						    }						,  )





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							{}
													if self.train_dir is not None:
																				A__		   =							self.train_dir
													if self.validation_dir is not None:
																				A__		   =							self.validation_dir
													A__		   =							data_files if data_files else None






@dataclass
class 			UpperCamelCase__:
						lowerCAmelCase__     :  str  							=						field(
						    default=__A						,  metadata={
						        'help': (
						            'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
						            'checkpoint identifier on the hub. '
						            'Don\'t set if you want to train a model from scratch.'
						        )
						    }						,  )
						lowerCAmelCase__     :  Optional[str]  							=						field(
						    default=__A						,  metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__A       )}						,  )
						lowerCAmelCase__     :  Optional[str]  							=						field(
						    default=__A						,  metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
						lowerCAmelCase__     :  Optional[str]  							=						field(
						    default=__A						,  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'
						        )
						    }						,  )
						lowerCAmelCase__     :  Optional[str]  							=						field(
						    default=__A						,  metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}						,  )
						lowerCAmelCase__     :  str  							=						field(
						    default='main'						,  metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}						,  )
						lowerCAmelCase__     :  str  							=						field(default=__A						,  metadata={'help': 'Name or path of preprocessor config.'}       )
						lowerCAmelCase__     :  bool  							=						field(
						    default=__A						,  metadata={
						        'help': (
						            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
						            'with private models).'
						        )
						    }						,  )
						lowerCAmelCase__     :  Optional[int]  							=						field(
						    default=__A						,  metadata={
						        'help': (
						            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
						        )
						    }						,  )
						lowerCAmelCase__     :  Optional[int]  							=						field(
						    default=__A						,  metadata={
						        'help': (
						            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
						        )
						    }						,  )
						lowerCAmelCase__     :  Optional[int]  							=						field(
						    default=__A						,  metadata={'help': 'Stride to use for the encoder.'}						,  )






class 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase=1_92						,__UpperCAmelCase=32						,__UpperCAmelCase=4						,__UpperCAmelCase=0.6   )    -> str:
													A__		   =							input_size
													A__		   =							mask_patch_size
													A__		   =							model_patch_size
													A__		   =							mask_ratio

													if self.input_size % self.mask_patch_size != 0:
																				raise ValueError('Input size must be divisible by mask patch size'   )
													if self.mask_patch_size % self.model_patch_size != 0:
																				raise ValueError('Mask patch size must be divisible by model patch size'   )

													A__		   =							self.input_size // self.mask_patch_size
													A__		   =							self.mask_patch_size // self.model_patch_size

													A__		   =							self.rand_size**2
													A__		   =							int(np.ceil(self.token_count * self.mask_ratio   )   )





						def __call__(				self   )    -> Optional[Any]:
													A__		   =							np.random.permutation(self.token_count   )[: self.mask_count]
													A__		   =							np.zeros(self.token_count						,dtype=__UpperCAmelCase   )
													A__		   =							1

													A__		   =							mask.reshape((self.rand_size, self.rand_size)   )
													A__		   =							mask.repeat(self.scale						,axis=0   ).repeat(self.scale						,axis=1   )

													return torch.tensor(mask.flatten()   )






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							torch.stack([example['pixel_values'] for example in examples] )
							A__		   =							torch.stack([example['mask'] for example in examples] )
							return {"pixel_values": pixel_values, "bool_masked_pos": mask}






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							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.
														A__   ,     A__   ,     A__		   =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
							else:
														A__   ,     A__   ,     A__		   =							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_mim'				,			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()

							A__		   =							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.
							A__		   =							None
							if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
														A__		   =							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.
							A__		   =							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.
							A__		   =							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:
														A__		   =							ds['train'].train_test_split(data_args.train_val_split )
														A__		   =							split['train']
														A__		   =							split['test']

							# Create config
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							A__		   =							{
							    '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_or_path:
														A__		   =							AutoConfig.from_pretrained(model_args.config_name_or_path				,			**UpperCamelCase__ )
							elif model_args.model_name_or_path:
														A__		   =							AutoConfig.from_pretrained(model_args.model_name_or_path				,			**UpperCamelCase__ )
							else:
														A__		   =							CONFIG_MAPPING[model_args.model_type]()
														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}''' )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
							if hasattr(UpperCamelCase__				,			'decoder_type' ):
														A__		   =							'simmim'

							# adapt config
							A__		   =							model_args.image_size if model_args.image_size is not None else config.image_size
							A__		   =							model_args.patch_size if model_args.patch_size is not None else config.patch_size
							A__		   =							(
							    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
							)

							config.update(
							    {
							        'image_size': model_args.image_size,
							        'patch_size': model_args.patch_size,
							        'encoder_stride': model_args.encoder_stride,
							    } )

							# create image processor
							if model_args.image_processor_name:
														A__		   =							AutoImageProcessor.from_pretrained(model_args.image_processor_name				,			**UpperCamelCase__ )
							elif model_args.model_name_or_path:
														A__		   =							AutoImageProcessor.from_pretrained(model_args.model_name_or_path				,			**UpperCamelCase__ )
							else:
														A__		   =							{
														    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
														}
														A__		   =							IMAGE_PROCESSOR_TYPES[model_args.model_type]()

							# create model
							if model_args.model_name_or_path:
														A__		   =							AutoModelForMaskedImageModeling.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' )
														A__		   =							AutoModelForMaskedImageModeling.from_config(UpperCamelCase__ )

							if training_args.do_train:
														A__		   =							ds['train'].column_names
							else:
														A__		   =							ds['validation'].column_names

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

							# transformations as done in original SimMIM paper
							# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
							A__		   =							Compose(
							    [
							        Lambda(lambda UpperCamelCase__ : img.convert('RGB' ) if img.mode != "RGB" else img ),
							        RandomResizedCrop(model_args.image_size				,			scale=(0.6_7, 1.0)				,			ratio=(3.0 / 4.0, 4.0 / 3.0) ),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        Normalize(mean=image_processor.image_mean				,			std=image_processor.image_std ),
							    ] )

							# create mask generator
							A__		   =							MaskGenerator(
							    input_size=model_args.image_size				,			mask_patch_size=data_args.mask_patch_size				,			model_patch_size=model_args.patch_size				,			mask_ratio=data_args.mask_ratio				,			)

							def preprocess_images(UpperCamelCase__ ):
														A__		   =							[transforms(UpperCamelCase__ ) for image in examples[image_column_name]]
														A__		   =							[mask_generator() for i in range(len(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:
																					A__		   =							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:
																					A__		   =							(
																					    ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
																					)
														# Set the validation transforms
														ds["validation"].set_transform(UpperCamelCase__ )

							# Initialize our trainer
							A__		   =							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:
														A__		   =							None
														if training_args.resume_from_checkpoint is not None:
																					A__		   =							training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					A__		   =							last_checkpoint
														A__		   =							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:
														A__		   =							trainer.evaluate()
														trainer.log_metrics('eval'				,			UpperCamelCase__ )
														trainer.save_metrics('eval'				,			UpperCamelCase__ )

							# Write model card and (optionally) push to hub
							A__		   =							{
							    'finetuned_from': model_args.model_name_or_path,
							    'tasks': 'masked-image-modeling',
							    'dataset': data_args.dataset_name,
							    'tags': ['masked-image-modeling'],
							}
							if training_args.push_to_hub:
														trainer.push_to_hub(**UpperCamelCase__ )
							else:
														trainer.create_model_card(**UpperCamelCase__ )


if __name__ == "__main__":
				main()






"""simple docstring"""




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


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Union[str, Any]  							=						'transfo-xl'
						lowerCAmelCase__     :  Any  							=						['mems']
						lowerCAmelCase__     :  Tuple  							=						{
						    'n_token': 'vocab_size',
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}





						def __init__(				self						,__UpperCAmelCase=26_77_35						,__UpperCAmelCase=[2_00_00, 4_00_00, 20_00_00]						,__UpperCAmelCase=10_24						,__UpperCAmelCase=10_24						,__UpperCAmelCase=16						,__UpperCAmelCase=64						,__UpperCAmelCase=40_96						,__UpperCAmelCase=4						,__UpperCAmelCase=False						,__UpperCAmelCase=18						,__UpperCAmelCase=16_00						,__UpperCAmelCase=10_00						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=0						,__UpperCAmelCase=-1						,__UpperCAmelCase=True						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.0						,__UpperCAmelCase=True						,__UpperCAmelCase="normal"						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_1						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=1e-5						,__UpperCAmelCase=0						,**__UpperCAmelCase						,)    -> Tuple:
													A__		   =							vocab_size
													A__		   =							[]
													self.cutoffs.extend(__UpperCAmelCase   )
													if proj_share_all_but_first:
																				A__		   =							[False] + [True] * len(self.cutoffs   )
													else:
																				A__		   =							[False] + [False] * len(self.cutoffs   )
													A__		   =							d_model
													A__		   =							d_embed
													A__		   =							d_head
													A__		   =							d_inner
													A__		   =							div_val
													A__		   =							pre_lnorm
													A__		   =							n_layer
													A__		   =							n_head
													A__		   =							mem_len
													A__		   =							same_length
													A__		   =							attn_type
													A__		   =							clamp_len
													A__		   =							sample_softmax
													A__		   =							adaptive
													A__		   =							dropout
													A__		   =							dropatt
													A__		   =							untie_r
													A__		   =							init
													A__		   =							init_range
													A__		   =							proj_init_std
													A__		   =							init_std
													A__		   =							layer_norm_epsilon
													super().__init__(eos_token_id=__UpperCAmelCase						,**__UpperCAmelCase   )





						@property
						def  snake_case__		(				self   )    -> Optional[Any]:
													# Message copied from Transformer-XL documentation
													logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
													return -1





						@max_position_embeddings.setter
						def  snake_case__		(				self						,__UpperCAmelCase   )    -> int:
													# Message copied from Transformer-XL documentation
													raise NotImplementedError(
													    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )





