
"""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
UpperCamelCase_					:						int				= 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				:




			"""simple docstring"""







			snake_case	      =				field(
			    default="cifar10"     ,				metadata={"help": "Name of a dataset from the datasets package"}      )
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}      )
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "The column name of the images in the files."}      )
			snake_case	      =				field(default=_lowercase     ,				metadata={"help": "A folder containing the training data."}      )
			snake_case	      =				field(default=_lowercase     ,				metadata={"help": "A folder containing the validation data."}      )
			snake_case	      =				field(
			    default=0.15     ,				metadata={"help": "Percent to split off of train for validation."}      )
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of training examples to this "
			            "value if set."
			        )
			    }     ,				)
			snake_case	      =				field(
			    default=_lowercase     ,				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]	)      -> Dict:







								"""simple docstring"""
								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 __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				field(
			    default=_lowercase     ,				metadata={
			        "help": (
			            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
			        )
			    }     ,				)
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}      )
			snake_case	      =				field(
			    default=_lowercase     ,				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"
			        )
			    }     ,				)
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}      )
			snake_case	      =				field(
			    default="main"     ,				metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}     ,				)
			snake_case	      =				field(default=_lowercase     ,				metadata={"help": "Name or path of preprocessor config."}      )
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={
			        "help": (
			            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
			            "with private models)."
			        )
			    }     ,				)
			snake_case	      =				field(
			    default=0.75     ,				metadata={"help": "The ratio of the number of masked tokens in the input sequence."}      )
			snake_case	      =				field(
			    default=_lowercase     ,				metadata={"help": "Whether or not to train with normalized pixel values as target."}      )





@dataclass
class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				field(
			    default=1E-3     ,				metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}      )





def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						torch.stack([example["pixel_values"] for example in examples]				)
					return {"pixel_values": pixel_values}
def 							A_     ():

					'''simple docstring'''





					A_				 =						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.
										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_mae"				,   __a				,   __a				)

					# 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(__a				)
					transformers.utils.logging.set_verbosity(__a				)
					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				,   __a				) 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"]

					# Load pretrained model and image processor
					#
					# 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:
										A_				 =						ViTMAEConfig.from_pretrained(model_args.config_name				,   **__a				)
					elif model_args.model_name_or_path:
										A_				 =						ViTMAEConfig.from_pretrained(model_args.model_name_or_path				,   **__a				)
					else:
										A_				 =						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:
										A_				 =						ViTImageProcessor.from_pretrained(model_args.image_processor_name				,   **__a				)
					elif model_args.model_name_or_path:
										A_				 =						ViTImageProcessor.from_pretrained(model_args.model_name_or_path				,   **__a				)
					else:
										A_				 =						ViTImageProcessor()

					# create model
					if model_args.model_name_or_path:
										A_				 =						ViTMAEForPreTraining.from_pretrained(
										    model_args.model_name_or_path				,   from_tf=bool(".ckpt" in model_args.model_name_or_path				)				,   config=__a				,   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_				 =						ViTMAEForPreTraining(__a				)

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

					def preprocess_images(__a				):
										A_				 =						[transforms(__a				) 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:
															A_				 =						ds["train"].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
										# Set the training transforms
										ds["train"].set_transform(__a				)

					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(__a				)

					# Compute absolute learning rate
					A_				 =						(
					    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
					)
					if training_args.base_learning_rate is not None:
										A_				 =						training_args.base_learning_rate * total_train_batch_size / 256

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

					# 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=__a				)
										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"				,   __a				)
										trainer.save_metrics("eval"				,   __a				)

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

					'''simple docstring'''





					main()


if __name__ == "__main__":
		main()






"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 							A_     (__a				):

					'''simple docstring'''





					return ConvertCommand(
					    args.model_type				,   args.tf_checkpoint				,   args.pytorch_dump_output				,   args.config				,   args.finetuning_task_name				)


UpperCamelCase_					:						Tuple				= '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''




class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			@staticmethod
			def      lowerCamelCase__     (				_snake_case						:					ArgumentParser	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						parser.add_parser(
								    "convert"       ,   help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."       ,   )
								train_parser.add_argument("--model_type"       ,   type=_snake_case       ,   required=_snake_case       ,   help="Model's type."	)
								train_parser.add_argument(
								    "--tf_checkpoint"       ,   type=_snake_case       ,   required=_snake_case       ,   help="TensorFlow checkpoint path or folder."	)
								train_parser.add_argument(
								    "--pytorch_dump_output"       ,   type=_snake_case       ,   required=_snake_case       ,   help="Path to the PyTorch saved model output."	)
								train_parser.add_argument("--config"       ,   type=_snake_case       ,   default=""       ,   help="Configuration file path or folder."	)
								train_parser.add_argument(
								    "--finetuning_task_name"       ,   type=_snake_case       ,   default=_snake_case       ,   help="Optional fine-tuning task name if the TF model was a finetuned model."       ,   )
								train_parser.set_defaults(func=_snake_case	)




			def __init__(				self						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   *_snake_case						:					Optional[int]       ,   )      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						logging.get_logger("transformers-cli/converting"	)

								self._logger.info(F'Loading model {model_type}'	)
								A_				 =						model_type
								A_				 =						tf_checkpoint
								A_				 =						pytorch_dump_output
								A_				 =						config
								A_				 =						finetuning_task_name






			def      lowerCamelCase__     (				self						:					Tuple	)      -> Dict:







								"""simple docstring"""
								if self._model_type == "albert":
													try:
																		from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "bert":
													try:
																		from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "funnel":
													try:
																		from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "t5":
													try:
																		from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "gpt":
													from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
													    convert_openai_checkpoint_to_pytorch,
													)

													convert_openai_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "transfo_xl":
													try:
																		from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																		    convert_transfo_xl_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													if "ckpt" in self._tf_checkpoint.lower():
																		A_				 =						self._tf_checkpoint
																		A_				 =						""
													else:
																		A_				 =						self._tf_checkpoint
																		A_				 =						""
													convert_transfo_xl_checkpoint_to_pytorch(
													    _snake_case       ,   self._config       ,   self._pytorch_dump_output       ,   _snake_case	)
								elif self._model_type == "gpt2":
													try:
																		from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																		    convert_gpta_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "xlnet":
													try:
																		from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																		    convert_xlnet_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_xlnet_checkpoint_to_pytorch(
													    self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output       ,   self._finetuning_task_name	)
								elif self._model_type == "xlm":
													from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
													    convert_xlm_checkpoint_to_pytorch,
													)

													convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._pytorch_dump_output	)
								elif self._model_type == "lxmert":
													from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
													    convert_lxmert_checkpoint_to_pytorch,
													)

													convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._pytorch_dump_output	)
								elif self._model_type == "rembert":
													from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
													    convert_rembert_tf_checkpoint_to_pytorch,
													)

													convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								else:
													raise ValueError(
													    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"	)




