


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

							def       lowerCamelCase_ (							self:	Tuple							)			->   Tuple:





									"""simple docstring"""







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


class        _a							:

							def __init__(							self:	Optional[int]    ,						UpperCamelCase_:	Optional[Any]=192    ,						UpperCamelCase_:	Optional[int]=32    ,						UpperCamelCase_:	int=4    ,						UpperCamelCase_:	List[Any]=0.6							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      input_size
									lowercase__            =      mask_patch_size
									lowercase__            =      model_patch_size
									lowercase__            =      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'''							)

									lowercase__            =      self.input_size // self.mask_patch_size
									lowercase__            =      self.mask_patch_size // self.model_patch_size

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


							def __call__(							self:	Dict							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      np.random.permutation(self.token_count							)[: self.mask_count]
									lowercase__            =      np.zeros(self.token_count    ,						dtype=UpperCamelCase_							)
									lowercase__            =      1

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

									return torch.tensor(mask.flatten()							)
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




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


		"""simple docstring"""




		lowercase__            =      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.
				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_mim'''						,				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''']

		# Create config
		# 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_or_path:
				lowercase__            =      AutoConfig.from_pretrained(model_args.config_name_or_path						,				**SCREAMING_SNAKE_CASE     )
		elif model_args.model_name_or_path:
				lowercase__            =      AutoConfig.from_pretrained(model_args.model_name_or_path						,				**SCREAMING_SNAKE_CASE     )
		else:
				lowercase__            =      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(SCREAMING_SNAKE_CASE						,				'''decoder_type'''     ):
				lowercase__            =      '''simmim'''

		# adapt config
		lowercase__            =      model_args.image_size if model_args.image_size is not None else config.image_size
		lowercase__            =      model_args.patch_size if model_args.patch_size is not None else config.patch_size
		lowercase__            =      (
		    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:
				lowercase__            =      AutoImageProcessor.from_pretrained(model_args.image_processor_name						,				**SCREAMING_SNAKE_CASE     )
		elif model_args.model_name_or_path:
				lowercase__            =      AutoImageProcessor.from_pretrained(model_args.model_name_or_path						,				**SCREAMING_SNAKE_CASE     )
		else:
				lowercase__            =      {
				    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
				}
				lowercase__            =      IMAGE_PROCESSOR_TYPES[model_args.model_type]()

		# create model
		if model_args.model_name_or_path:
				lowercase__            =      AutoModelForMaskedImageModeling.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__            =      AutoModelForMaskedImageModeling.from_config(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 SimMIM paper
		# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
		lowercase__            =      Compose(
		    [
		        Lambda(lambda SCREAMING_SNAKE_CASE     : img.convert('''RGB'''     ) if img.mode != "RGB" else img     ),
		        RandomResizedCrop(model_args.image_size						,				scale=(0.67, 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
		lowercase__            =      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(SCREAMING_SNAKE_CASE     ):
				lowercase__            =      [transforms(SCREAMING_SNAKE_CASE     ) for image in examples[image_column_name]]
				lowercase__            =      [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:
						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     )

		# 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__            =      {
		    '''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(**SCREAMING_SNAKE_CASE     )
		else:
				trainer.create_model_card(**SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			main()









import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Optional[int]					 =		[
        'encoder.version',
        'decoder.version',
        'model.encoder.version',
        'model.decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'encoder.embed_positions._float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_UpperCAmelCase						,	_UpperCAmelCase )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[Any]:
    lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		emb.weight.shape
    lowerCamelCase__    :      Tuple					 =		nn.Linear(_UpperCAmelCase						,	_UpperCAmelCase						,	bias=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		emb.weight.data
    return lin_layer


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )
    lowerCamelCase__    :      List[str]					 =		mam_aaa['args'] or mam_aaa['cfg']['model']
    lowerCamelCase__    :      Optional[int]					 =		mam_aaa['model']
    remove_ignore_keys_(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		state_dict['encoder.embed_tokens.weight'].shape[0]

    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaConfig(
        vocab_size=_UpperCAmelCase						,	max_position_embeddings=1024						,	encoder_layers=args.encoder_layers						,	decoder_layers=args.decoder_layers						,	encoder_attention_heads=args.encoder_attention_heads						,	decoder_attention_heads=args.decoder_attention_heads						,	encoder_ffn_dim=args.encoder_ffn_embed_dim						,	decoder_ffn_dim=args.decoder_ffn_embed_dim						,	d_model=args.encoder_embed_dim						,	encoder_layerdrop=args.encoder_layerdrop						,	decoder_layerdrop=args.decoder_layerdrop						,	dropout=args.dropout						,	attention_dropout=args.attention_dropout						,	activation_dropout=args.activation_dropout						,	activation_function='relu'						,	)

    lowerCamelCase__    :      Optional[Any]					 =		state_dict['decoder.embed_tokens.weight']
    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaForConditionalGeneration(_UpperCAmelCase )
    model.model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    lowerCamelCase__    :      List[str]					 =		make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
    parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    _UpperCAmelCase				:       str	   =				parser.parse_args()
    _UpperCAmelCase				:       Optional[Any]	   =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





