



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
snake_case    :						Union[str, Any]  =						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')

snake_case    :						Union[str, Any]  =						list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
snake_case    :						str  =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 							lowerCAmelCase__  :
					__A     :    Optional[str]           =						field(
					    default='cifar10'					,							metadata={'help': 'Name of a dataset from the datasets package'}						)
					__A     :    Optional[str]           =						field(
					    default=UpperCamelCase					,							metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}						)
					__A     :    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\'.'}					,							)
					__A     :    Optional[str]           =						field(default=UpperCamelCase					,							metadata={'help': 'A folder containing the training data.'}						)
					__A     :    Optional[str]           =						field(default=UpperCamelCase					,							metadata={'help': 'A folder containing the validation data.'}						)
					__A     :    Optional[float]           =						field(
					    default=0.15					,							metadata={'help': 'Percent to split off of train for validation.'}						)
					__A     :    int           =						field(default=32					,							metadata={'help': 'The size of the square patches to use for masking.'}						)
					__A     :    float           =						field(
					    default=0.6					,							metadata={'help': 'Percentage of patches to mask.'}					,							)
					__A     :    Optional[int]           =						field(
					    default=UpperCamelCase					,							metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of training examples to this '
					            'value if set.'
					        )
					    }					,							)
					__A     :    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  _lowercase			(       self      :     List[str]):
											A__	:							Optional[Any]						    = {}
											if self.train_dir is not None:
																	A__	:							str						    = self.train_dir
											if self.validation_dir is not None:
																	A__	:							int						    = self.validation_dir
											A__	:							int						    = data_files if data_files else None




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




class 							lowerCAmelCase__  :







					def __init__(       self      :     List[Any]			,		_A      :     Tuple=192			,		_A      :     str=32			,		_A      :     List[str]=4			,		_A      :     Optional[Any]=0.6):
											A__	:							Optional[int]						    = input_size
											A__	:							Optional[Any]						    = mask_patch_size
											A__	:							List[Any]						    = model_patch_size
											A__	:							Any						    = 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__	:							List[Any]						    = self.input_size // self.mask_patch_size
											A__	:							Dict						    = self.mask_patch_size // self.model_patch_size

											A__	:							Any						    = self.rand_size**2
											A__	:							Optional[int]						    = int(np.ceil(self.token_count * self.mask_ratio))







					def __call__(       self      :     str):
											A__	:							str						    = np.random.permutation(self.token_count)[: self.mask_count]
											A__	:							Optional[int]						    = np.zeros(self.token_count			,		dtype=_A)
											A__	:							Dict						    = 1

											A__	:							Optional[Any]						    = mask.reshape((self.rand_size, self.rand_size))
											A__	:							List[Any]						    = mask.repeat(self.scale			,		axis=0).repeat(self.scale			,		axis=1)

											return torch.tensor(mask.flatten())




def 	snake_case__       (					__lowercase       )		->		Optional[int]:







						"""simple docstring"""

						A__	:							int						    = torch.stack([example["pixel_values"] for example in examples]       )
						A__	:							int						    = torch.stack([example["mask"] for example in examples]       )
						return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def 	snake_case__       (					)		->		Optional[Any]:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = 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__	:							Optional[Any]						    = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
						else:
												A__    ,       A__    ,       A__	:							Tuple						    = 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"							,     __lowercase							,     __lowercase       )

						# 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__	:							Tuple						    = training_args.get_process_log_level()
						logger.setLevel(__lowercase       )
						transformers.utils.logging.set_verbosity(__lowercase       )
						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__	:							Tuple						    = None
						if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
												A__	:							List[str]						    = 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__	:							List[Any]						    = 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__	:							Optional[Any]						    = None if "validation" in ds.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split							,     __lowercase       ) and data_args.train_val_split > 0.0:
												A__	:							int						    = ds["train"].train_test_split(data_args.train_val_split       )
												A__	:							Any						    = split["train"]
												A__	:							Tuple						    = split["test"]

						# Create config
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.
						A__	:							List[str]						    = {
						    "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__	:							Dict						    = AutoConfig.from_pretrained(model_args.config_name_or_path							,     **__lowercase       )
						elif model_args.model_name_or_path:
												A__	:							Union[str, Any]						    = AutoConfig.from_pretrained(model_args.model_name_or_path							,     **__lowercase       )
						else:
												A__	:							Optional[int]						    = 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(__lowercase							,     "decoder_type"       ):
												A__	:							Optional[int]						    = "simmim"

						# adapt config
						A__	:							int						    = model_args.image_size if model_args.image_size is not None else config.image_size
						A__	:							Tuple						    = model_args.patch_size if model_args.patch_size is not None else config.patch_size
						A__	:							Union[str, Any]						    = (
						    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__	:							Optional[Any]						    = AutoImageProcessor.from_pretrained(model_args.image_processor_name							,     **__lowercase       )
						elif model_args.model_name_or_path:
												A__	:							str						    = AutoImageProcessor.from_pretrained(model_args.model_name_or_path							,     **__lowercase       )
						else:
												A__	:							List[str]						    = {
												    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
												}
												A__	:							Optional[int]						    = IMAGE_PROCESSOR_TYPES[model_args.model_type]()

						# create model
						if model_args.model_name_or_path:
												A__	:							int						    = AutoModelForMaskedImageModeling.from_pretrained(
												    model_args.model_name_or_path							,     from_tf=bool(".ckpt" in model_args.model_name_or_path       )							,     config=__lowercase							,     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__	:							Optional[int]						    = AutoModelForMaskedImageModeling.from_config(__lowercase       )

						if training_args.do_train:
												A__	:							Optional[Any]						    = ds["train"].column_names
						else:
												A__	:							Any						    = ds["validation"].column_names

						if data_args.image_column_name is not None:
												A__	:							Optional[Any]						    = data_args.image_column_name
						elif "image" in column_names:
												A__	:							Dict						    = "image"
						elif "img" in column_names:
												A__	:							Dict						    = "img"
						else:
												A__	:							Union[str, Any]						    = column_names[0]

						# transformations as done in original SimMIM paper
						# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
						A__	:							str						    = Compose(
						    [
						        Lambda(lambda __lowercase       : 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
						A__	:							List[Any]						    = 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(__lowercase       ):
												A__	:							Any						    = [transforms(__lowercase       ) for image in examples[image_column_name]]
												A__	:							str						    = [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__	:							Union[str, Any]						    = ds["train"].shuffle(seed=training_args.seed       ).select(range(data_args.max_train_samples       )       )
												# Set the training transforms
												ds["train"].set_transform(__lowercase       )

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

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

						# Training
						if training_args.do_train:
												A__	:							Dict						    = None
												if training_args.resume_from_checkpoint is not None:
																		A__	:							int						    = training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		A__	:							Dict						    = last_checkpoint
												A__	:							str						    = trainer.train(resume_from_checkpoint=__lowercase       )
												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__	:							Dict						    = trainer.evaluate()
												trainer.log_metrics("eval"							,     __lowercase       )
												trainer.save_metrics("eval"							,     __lowercase       )

						# Write model card and (optionally) push to hub
						A__	:							List[Any]						    = {
						    "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(**__lowercase       )
						else:
												trainer.create_model_card(**__lowercase       )


if __name__ == "__main__":
		main()




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


snake_case    :						Any  =						logging.getLogger(__name__)




class 							lowerCAmelCase__  :







					def __init__(       self      :     Union[str, Any]):
											A__	:							Dict						    = False







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     int			,		_A      :     Optional[int]			,		_A      :     List[str]			,		_A      :     Optional[Any]):
											if not self.initialized:
																	A__	:							List[Any]						    = RagRetriever(
																	    _A			,		question_encoder_tokenizer=_A			,		generator_tokenizer=_A			,		index=_A			,		init_retrieval=_A			,		)
																	A__	:							Dict						    = True







					def  _lowercase			(       self      :     Union[str, Any]):
											self.retriever.index.init_index()







					def  _lowercase			(       self      :     List[str]			,		_A      :     Optional[int]			,		_A      :     List[str]):
											A__    ,       A__	:							str						    = self.retriever._main_retrieve(_A			,		_A)
											return doc_ids, retrieved_doc_embeds




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Optional[Any]			,		_A      :     Union[str, Any]			,		_A      :     Tuple			,		_A      :     int			,		_A      :     Tuple			,		_A      :     Union[str, Any]=None):
											if index is not None and index.is_initialized() and len(_A) > 0:
																	raise ValueError(
																	    "When using Ray for distributed fine-tuning, "
																	    "you'll need to provide the paths instead, "
																	    "as the dataset and the index are loaded "
																	    "separately. More info in examples/rag/use_own_knowledge_dataset.py ")
											super().__init__(
											    _A			,		question_encoder_tokenizer=_A			,		generator_tokenizer=_A			,		index=_A			,		init_retrieval=_A			,		)
											A__	:							str						    = retrieval_workers
											if len(self.retrieval_workers) > 0:
																	ray.get(
																	    [
																	        worker.create_rag_retriever.remote(_A			,		_A			,		_A			,		_A)
																	        for worker in self.retrieval_workers
																	    ])







					def  _lowercase			(       self      :     Optional[Any]):
											logger.info("initializing retrieval")

											if len(self.retrieval_workers) > 0:
																	ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
											else:
																	# Non-distributed training. Load index into this same process.
																	self.index.init_index()







					def  _lowercase			(       self      :     Union[str, Any]			,		_A      :     List[str]			,		_A      :     Optional[Any]):
											if len(self.retrieval_workers) > 0:
																	# Select a random retrieval actor.
																	A__	:							Optional[Any]						    = self.retrieval_workers[random.randint(0			,		len(self.retrieval_workers) - 1)]
																	A__    ,       A__	:							List[Any]						    = ray.get(random_worker.retrieve.remote(_A			,		_A))
											else:
																	A__    ,       A__	:							Union[str, Any]						    = self._main_retrieve(_A			,		_A)
											return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_A)







					@classmethod
					def  _lowercase			(       cls      :     int			,		_A      :     Optional[int]			,		_A      :     List[str]=None			,		**_A      :     Optional[Any]):
											return super(_A			,		cls).get_tokenizers(_A			,		_A			,		**_A)







					@classmethod
					def  _lowercase			(       cls      :     Any			,		_A      :     Union[str, Any]			,		_A      :     Optional[Any]			,		_A      :     List[str]=None			,		**_A      :     Optional[Any]):
											A__	:							str						    = kwargs.pop("config"			,		_A) or RagConfig.from_pretrained(_A			,		**_A)
											A__	:							int						    = RagTokenizer.from_pretrained(_A			,		config=_A)
											A__	:							List[str]						    = rag_tokenizer.question_encoder
											A__	:							str						    = rag_tokenizer.generator
											if indexed_dataset is not None:
																	A__	:							Tuple						    = "custom"
																	A__	:							Tuple						    = CustomHFIndex(config.retrieval_vector_size			,		_A)
											else:
																	A__	:							Union[str, Any]						    = cls._build_index(_A)
											return cls(
											    _A			,		question_encoder_tokenizer=_A			,		generator_tokenizer=_A			,		retrieval_workers=_A			,		index=_A			,		)