


'''simple docstring'''


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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
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')






@dataclass
class 	lowerCAmelCase__			:
			lowerCAmelCase :	Optional[str]          =     field(
			    default="cifar10"    ,       metadata={"help": "Name of a dataset from the datasets package"}	)
			lowerCAmelCase :	Optional[str]          =     field(
			    default=UpperCAmelCase__    ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}	)
			lowerCAmelCase :	Optional[str]          =     field(
			    default=UpperCAmelCase__    ,       metadata={"help": "The column name of the images in the files."}	)
			lowerCAmelCase :	Optional[str]          =     field(default=UpperCAmelCase__    ,       metadata={"help": "A folder containing the training data."}	)
			lowerCAmelCase :	Optional[str]          =     field(default=UpperCAmelCase__    ,       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 :	Optional[int]          =     field(
			    default=UpperCAmelCase__    ,       metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of training examples to this "
			            "value if set."
			        )
			    }    ,       )
			lowerCAmelCase :	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  :     Union[str, Any] )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      {}
					if self.train_dir is not None:
							_UpperCAmelCase		:       str								=      self.train_dir
					if self.validation_dir is not None:
							_UpperCAmelCase		:       List[Any]								=      self.validation_dir
					_UpperCAmelCase		:       List[str]								=      data_files if data_files else None






@dataclass
class 	lowerCAmelCase__			:
			lowerCAmelCase :	str          =     field(
			    default=UpperCAmelCase__    ,       metadata={
			        "help": (
			            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
			        )
			    }    ,       )
			lowerCAmelCase :	Optional[str]          =     field(
			    default=UpperCAmelCase__    ,       metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}	)
			lowerCAmelCase :	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"
			        )
			    }    ,       )
			lowerCAmelCase :	Optional[str]          =     field(
			    default=UpperCAmelCase__    ,       metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}	)
			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=UpperCAmelCase__    ,       metadata={"help": "Name or path of preprocessor config."}	)
			lowerCAmelCase :	bool          =     field(
			    default=UpperCAmelCase__    ,       metadata={
			        "help": (
			            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
			            "with private models)."
			        )
			    }    ,       )
			lowerCAmelCase :	float          =     field(
			    default=0.75    ,       metadata={"help": "The ratio of the number of masked tokens in the input sequence."}	)
			lowerCAmelCase :	bool          =     field(
			    default=UpperCAmelCase__    ,       metadata={"help": "Whether or not to train with normalized pixel values as target."}	)






@dataclass
class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	float          =     field(
			    default=1e-3    ,       metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}	)






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       List[str]								=      torch.stack([example["pixel_values"] for example in examples]   )
		return {"pixel_values": pixel_values}






def  __lowerCAmelCase ():
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		_UpperCAmelCase		:       List[str]								=      HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)   )
		if len(sys.argv   ) == 2 and sys.argv[1].endswith(".json"   ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_UpperCAmelCase		:       Optional[Any]								=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
		else:
				_UpperCAmelCase		:       Optional[Any]								=      parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry("run_mae"      , __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()

		_UpperCAmelCase		:       List[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.
		_UpperCAmelCase		:       Tuple								=      None
		if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
				_UpperCAmelCase		:       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.
		_UpperCAmelCase		:       Dict								=      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.
		_UpperCAmelCase		:       Any								=      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:
				_UpperCAmelCase		:       Optional[int]								=      ds["train"].train_test_split(data_args.train_val_split   )
				_UpperCAmelCase		:       List[Any]								=      split["train"]
				_UpperCAmelCase		:       Any								=      split["test"]

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		_UpperCAmelCase		:       Union[str, 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:
				_UpperCAmelCase		:       List[Any]								=      ViTMAEConfig.from_pretrained(model_args.config_name      , **__lowerCAmelCase   )
		elif model_args.model_name_or_path:
				_UpperCAmelCase		:       str								=      ViTMAEConfig.from_pretrained(model_args.model_name_or_path      , **__lowerCAmelCase   )
		else:
				_UpperCAmelCase		:       Any								=      ViTMAEConfig()
				logger.warning("You are instantiating a new config instance from scratch."   )
				if model_args.config_overrides is not None:
						logger.info(F"""Overriding config: {model_args.config_overrides}"""   )
						config.update_from_string(model_args.config_overrides   )
						logger.info(F"""New config: {config}"""   )

    # adapt config
		config.update(
		    {
		        "mask_ratio": model_args.mask_ratio,
		        "norm_pix_loss": model_args.norm_pix_loss,
		    }   )

		# create image processor
		if model_args.image_processor_name:
				_UpperCAmelCase		:       List[str]								=      ViTImageProcessor.from_pretrained(model_args.image_processor_name      , **__lowerCAmelCase   )
		elif model_args.model_name_or_path:
				_UpperCAmelCase		:       str								=      ViTImageProcessor.from_pretrained(model_args.model_name_or_path      , **__lowerCAmelCase   )
		else:
				_UpperCAmelCase		:       int								=      ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				_UpperCAmelCase		:       Optional[int]								=      ViTMAEForPreTraining.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"   )
				_UpperCAmelCase		:       List[Any]								=      ViTMAEForPreTraining(__lowerCAmelCase   )

		if training_args.do_train:
				_UpperCAmelCase		:       Any								=      ds["train"].column_names
		else:
				_UpperCAmelCase		:       List[str]								=      ds["validation"].column_names

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

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				_UpperCAmelCase		:       int								=      image_processor.size["shortest_edge"]
		else:
				_UpperCAmelCase		:       str								=      (image_processor.size["height"], image_processor.size["width"])
		_UpperCAmelCase		:       Tuple								=      Compose(
		    [
		        Lambda(lambda __lowerCAmelCase   : img.convert("RGB"   ) if img.mode != "RGB" else img   ),
		        RandomResizedCrop(__lowerCAmelCase      , scale=(0.2, 1.0)      , interpolation=InterpolationMode.BICUBIC   ),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean      , std=image_processor.image_std   ),
		    ]   )

		def preprocess_images(__lowerCAmelCase   ):
				_UpperCAmelCase		:       Any								=      [transforms(__lowerCAmelCase   ) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError("--do_train requires a train dataset"   )
				if data_args.max_train_samples is not None:
						_UpperCAmelCase		:       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(__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:
						_UpperCAmelCase		:       Any								=      (
						    ds["validation"].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
						)
				# Set the validation transforms
				ds["validation"].set_transform(__lowerCAmelCase   )

		# Compute absolute learning rate
		_UpperCAmelCase		:       Dict								=      (
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				_UpperCAmelCase		:       Union[str, Any]								=      training_args.base_learning_rate * total_train_batch_size / 256

		# Initialize our trainer
		_UpperCAmelCase		:       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:
				_UpperCAmelCase		:       Tuple								=      None
				if training_args.resume_from_checkpoint is not None:
						_UpperCAmelCase		:       int								=      training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_UpperCAmelCase		:       List[str]								=      last_checkpoint
				_UpperCAmelCase		:       Optional[Any]								=      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:
				_UpperCAmelCase		:       Any								=      trainer.evaluate()
				trainer.log_metrics("eval"      , __lowerCAmelCase   )
				trainer.save_metrics("eval"      , __lowerCAmelCase   )

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






def  __lowerCAmelCase (__lowerCAmelCase   ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
	main()







'''simple docstring'''


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
						lowerCamelCase__    	=     argparse.ArgumentParser(
						    description=(
						        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
						        ' Distillation'
						    )
						)
						parser.add_argument('--model_type', default='bert', choices=['bert'])
						parser.add_argument('--model_name', default='bert-base-uncased', type=str)
						parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
						parser.add_argument('--vocab_transform', action='store_true')
						lowerCamelCase__    	=     parser.parse_args()

						if args.model_type == "bert":
												lowerCamelCase__    	=     BertForMaskedLM.from_pretrained(args.model_name)
												lowerCamelCase__    	=     'bert'
						else:
												raise ValueError('args.model_type should be "bert".')

						lowerCamelCase__    	=     model.state_dict()
						lowerCamelCase__    	=     {}

						for w in ["word_embeddings", "position_embeddings"]:
												lowerCamelCase__    	=     state_dict[F'''{prefix}.embeddings.{w}.weight''']
						for w in ["weight", "bias"]:
												lowerCamelCase__    	=     state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

						lowerCamelCase__    	=     0
						for teacher_idx in [0, 2, 4, 7, 9, 11]:
												for w in ["weight", "bias"]:
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
																		]

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
																		]

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
																		]
												std_idx += 1

						lowerCamelCase__    	=     state_dict['cls.predictions.decoder.weight']
						lowerCamelCase__    	=     state_dict['cls.predictions.bias']
						if args.vocab_transform:
												for w in ["weight", "bias"]:
																		lowerCamelCase__    	=     state_dict[F'''cls.predictions.transform.dense.{w}''']
																		lowerCamelCase__    	=     state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

						print(F'''N layers selected for distillation: {std_idx}''')
						print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

						print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
						torch.save(compressed_sd, args.dump_checkpoint)



