




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_  :      Any		=		list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
snake_case_  :      Dict		=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 					snake_case_   :



				'''simple docstring'''
				lowerCamelCase       =  field(
				    default="cifar10"   ,	metadata={"help": "Name of a dataset from the datasets package"}	)
				lowerCamelCase       =  field(
				    default=a_   ,	metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}	)
				lowerCamelCase       =  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       =  field(default=a_   ,	metadata={"help": "A folder containing the training data."}	)
				lowerCamelCase       =  field(default=a_   ,	metadata={"help": "A folder containing the validation data."}	)
				lowerCamelCase       =  field(
				    default=0.15   ,	metadata={"help": "Percent to split off of train for validation."}	)
				lowerCamelCase       =  field(default=32   ,	metadata={"help": "The size of the square patches to use for masking."}	)
				lowerCamelCase       =  field(
				    default=0.6   ,	metadata={"help": "Percentage of patches to mask."}   ,	)
				lowerCamelCase       =  field(
				    default=a_   ,	metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of training examples to this "
				            "value if set."
				        )
				    }   ,	)
				lowerCamelCase       =  field(
				    default=a_   ,	metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
				            "value if set."
				        )
				    }   ,	)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[Any]							)							->			int:
											lowerCamelCase_			:    List[str]   						=      {}
											if self.train_dir is not None:
																		lowerCamelCase_			:    List[str]   						=      self.train_dir
											if self.validation_dir is not None:
																		lowerCamelCase_			:    Union[str, Any]   						=      self.validation_dir
											lowerCamelCase_			:    Any   						=      data_files if data_files else None





@dataclass
class 					snake_case_   :



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





class 					snake_case_   :



				'''simple docstring'''





				def __init__(      self							:   List[Any]      ,							__magic_name__							:   int=192      ,							__magic_name__							:   Dict=32      ,							__magic_name__							:   List[Any]=4      ,							__magic_name__							:   Union[str, Any]=0.6							)							->			List[Any]:
											lowerCamelCase_			:    str   						=      input_size
											lowerCamelCase_			:    Dict   						=      mask_patch_size
											lowerCamelCase_			:    Dict   						=      model_patch_size
											lowerCamelCase_			:    List[str]   						=      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"							)

											lowerCamelCase_			:    List[str]   						=      self.input_size // self.mask_patch_size
											lowerCamelCase_			:    List[str]   						=      self.mask_patch_size // self.model_patch_size

											lowerCamelCase_			:    int   						=      self.rand_size**2
											lowerCamelCase_			:    List[str]   						=      int(np.ceil(self.token_count * self.mask_ratio							)							)





				def __call__(      self							:   List[Any]							)							->			Optional[Any]:
											lowerCamelCase_			:    Tuple   						=      np.random.permutation(self.token_count							)[: self.mask_count]
											lowerCamelCase_			:    int   						=      np.zeros(self.token_count      ,							dtype=__magic_name__							)
											lowerCamelCase_			:    Union[str, Any]   						=      1

											lowerCamelCase_			:    Dict   						=      mask.reshape((self.rand_size, self.rand_size)							)
											lowerCamelCase_			:    Dict   						=      mask.repeat(self.scale      ,							axis=0							).repeat(self.scale      ,							axis=1							)

											return torch.tensor(mask.flatten()							)



def        __a							(  __UpperCAmelCase		:						str      ) ->					str:
							"""simple docstring"""




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



def        __a							(  ) ->					Tuple:
							"""simple docstring"""




							lowerCamelCase_			:    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.
														lowerCamelCase_						,						lowerCamelCase_						,						lowerCamelCase_			:    Tuple   						=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
							else:
														lowerCamelCase_						,						lowerCamelCase_						,						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_mim"   ,		lowerCamelCase__   ,		lowerCamelCase__      )

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

							# Create config
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							lowerCamelCase_			:    Any   						=      {
							    "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:
														lowerCamelCase_			:    Any   						=      AutoConfig.from_pretrained(model_args.config_name_or_path   ,		**lowerCamelCase__      )
							elif model_args.model_name_or_path:
														lowerCamelCase_			:    Optional[Any]   						=      AutoConfig.from_pretrained(model_args.model_name_or_path   ,		**lowerCamelCase__      )
							else:
														lowerCamelCase_			:    Tuple   						=      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(lowerCamelCase__   ,		"decoder_type"      ):
														lowerCamelCase_			:    Optional[Any]   						=      "simmim"

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

							# create model
							if model_args.model_name_or_path:
														lowerCamelCase_			:    str   						=      AutoModelForMaskedImageModeling.from_pretrained(
														    model_args.model_name_or_path   ,		from_tf=bool(".ckpt" in model_args.model_name_or_path      )   ,		config=lowerCamelCase__   ,		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_			:    Any   						=      AutoModelForMaskedImageModeling.from_config(lowerCamelCase__      )

							if training_args.do_train:
														lowerCamelCase_			:    str   						=      ds["train"].column_names
							else:
														lowerCamelCase_			:    int   						=      ds["validation"].column_names

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

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

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

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

							# Training
							if training_args.do_train:
														lowerCamelCase_			:    str   						=      None
														if training_args.resume_from_checkpoint is not None:
																					lowerCamelCase_			:    str   						=      training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					lowerCamelCase_			:    Tuple   						=      last_checkpoint
														lowerCamelCase_			:    Optional[int]   						=      trainer.train(resume_from_checkpoint=lowerCamelCase__      )
														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"   ,		lowerCamelCase__      )
														trainer.save_metrics("eval"   ,		lowerCamelCase__      )

							# Write model card and (optionally) push to hub
							lowerCamelCase_			:    Dict   						=      {
							    "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(**lowerCamelCase__      )
							else:
														trainer.create_model_card(**lowerCamelCase__      )


if __name__ == "__main__":
	main()





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any=None      ):





			'''simple docstring'''




			lowerCamelCase    		=  argparse.ArgumentParser(add_help=lowerCamelCase__  ,							allow_abbrev=lowerCamelCase__      )
			# The main config parser
			lowerCamelCase    		=  config_command_parser(lowerCamelCase__      )
			# The subparser to add commands to
			lowerCamelCase    		=  config_parser.add_subparsers(title="""subcommands"""  ,							dest="""subcommand"""      )

			# Then add other parsers with the parent parser
			default_command_parser(lowerCamelCase__  ,							parents=[parent_parser]      )
			update_command_parser(lowerCamelCase__  ,							parents=[parent_parser]      )

			return config_parser






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  get_config_parser()
			lowerCamelCase    		=  config_parser.parse_args()

			if not hasattr(lowerCamelCase__  ,							"""func"""      ):
						config_parser.print_help()
						exit(1      )

			# Run
			args.func(lowerCamelCase__      )


if __name__ == "__main__":
		main()

