

"""simple docstring"""




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


A_							:			List[str]											= logging.get_logger(__name__)

A_							:			List[Any]											= {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		str							=							'gptj'
			lowerCamelCase__   :		List[Any]							=							{
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}

			def __init__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						List[Any]=5_0_4_0_0    ,				__UpperCAmelCase				:						Any=2_0_4_8    ,				__UpperCAmelCase				:						Dict=4_0_9_6    ,				__UpperCAmelCase				:						Optional[int]=2_8    ,				__UpperCAmelCase				:						List[Any]=1_6    ,				__UpperCAmelCase				:						str=6_4    ,				__UpperCAmelCase				:						int=None    ,				__UpperCAmelCase				:						Dict="gelu_new"    ,				__UpperCAmelCase				:						int=0.0    ,				__UpperCAmelCase				:						str=0.0    ,				__UpperCAmelCase				:						Optional[Any]=0.0    ,				__UpperCAmelCase				:						Any=1e-5    ,				__UpperCAmelCase				:						Union[str, Any]=0.02    ,				__UpperCAmelCase				:						str=True    ,				__UpperCAmelCase				:						List[Any]=5_0_2_5_6    ,				__UpperCAmelCase				:						List[str]=5_0_2_5_6    ,				__UpperCAmelCase				:						Tuple=False    ,				**__UpperCAmelCase				:						Union[str, Any]    ,				)   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      n_positions
						SCREAMING_SNAKE_CASE__								=      n_embd
						SCREAMING_SNAKE_CASE__								=      n_layer
						SCREAMING_SNAKE_CASE__								=      n_head
						SCREAMING_SNAKE_CASE__								=      n_inner
						SCREAMING_SNAKE_CASE__								=      rotary_dim
						SCREAMING_SNAKE_CASE__								=      activation_function
						SCREAMING_SNAKE_CASE__								=      resid_pdrop
						SCREAMING_SNAKE_CASE__								=      embd_pdrop
						SCREAMING_SNAKE_CASE__								=      attn_pdrop
						SCREAMING_SNAKE_CASE__								=      layer_norm_epsilon
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      use_cache

						SCREAMING_SNAKE_CASE__								=      bos_token_id
						SCREAMING_SNAKE_CASE__								=      eos_token_id

						super().__init__(
						    bos_token_id=__UpperCAmelCase    ,				eos_token_id=__UpperCAmelCase    ,				tie_word_embeddings=__UpperCAmelCase    ,				**__UpperCAmelCase       )





class    lowerCamelCase						(A__			):

			def __init__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						PretrainedConfig    ,				__UpperCAmelCase				:						str = "default"    ,				__UpperCAmelCase				:						List[PatchingSpec] = None    ,				__UpperCAmelCase				:						bool = False    ,				)   ->    Optional[int]:
						super().__init__(__UpperCAmelCase    ,				task=__UpperCAmelCase    ,				patching_specs=__UpperCAmelCase    ,				use_past=__UpperCAmelCase       )
						if not getattr(self._config    ,				"""pad_token_id"""    ,				__UpperCAmelCase       ):
									# TODO: how to do that better?
									SCREAMING_SNAKE_CASE__								=      0

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Mapping[str, Mapping[int, str]]:
						SCREAMING_SNAKE_CASE__								=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}       )
						if self.use_past:
									self.fill_with_past_key_values_(__UpperCAmelCase    ,				direction="""inputs"""       )
									SCREAMING_SNAKE_CASE__								=      {0: """batch""", 1: """past_sequence + sequence"""}
						else:
									SCREAMING_SNAKE_CASE__								=      {0: """batch""", 1: """sequence"""}

						return common_inputs

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    int:
						return self._config.n_layer

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    int:
						return self._config.n_head

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						PreTrainedTokenizer    ,				__UpperCAmelCase				:						int = -1    ,				__UpperCAmelCase				:						int = -1    ,				__UpperCAmelCase				:						bool = False    ,				__UpperCAmelCase				:						Optional[TensorType] = None    ,				)   ->    Mapping[str, Any]:
						SCREAMING_SNAKE_CASE__								=      super(__UpperCAmelCase    ,				self       ).generate_dummy_inputs(
						    __UpperCAmelCase    ,				batch_size=__UpperCAmelCase    ,				seq_length=__UpperCAmelCase    ,				is_pair=__UpperCAmelCase    ,				framework=__UpperCAmelCase       )

						# We need to order the input in the way they appears in the forward()
						SCREAMING_SNAKE_CASE__								=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}       )

						# Need to add the past_keys
						if self.use_past:
									if not is_torch_available():
												raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
									else:
												import torch

												SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      common_inputs["""input_ids"""].shape
												# Not using the same length for past_key_values
												SCREAMING_SNAKE_CASE__								=      seqlen + 2
												SCREAMING_SNAKE_CASE__								=      (
												    batch,
												    self.num_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // self.num_attention_heads,
												)
												SCREAMING_SNAKE_CASE__								=      [
												    (torch.zeros(__UpperCAmelCase       ), torch.zeros(__UpperCAmelCase       )) for _ in range(self.num_layers       )
												]

						SCREAMING_SNAKE_CASE__								=      common_inputs["""attention_mask"""]
						if self.use_past:
									SCREAMING_SNAKE_CASE__								=      ordered_inputs["""attention_mask"""].dtype
									SCREAMING_SNAKE_CASE__								=      torch.cat(
									    [ordered_inputs["""attention_mask"""], torch.ones(__UpperCAmelCase    ,				__UpperCAmelCase    ,				dtype=__UpperCAmelCase       )]    ,				dim=1       )

						return ordered_inputs





			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    int:
						return 1_3





"""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
A_							:			List[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")





@dataclass
class    lowerCamelCase						:
			lowerCamelCase__   :		Optional[str]							=							field(
			    default='cifar10'			,metadata={'help': 'Name of a dataset from the datasets package'}			)
			lowerCamelCase__   :		Optional[str]							=							field(
			    default=A__			,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
			lowerCamelCase__   :		Optional[str]							=							field(
			    default=A__			,metadata={'help': 'The column name of the images in the files.'}			)
			lowerCamelCase__   :		Optional[str]							=							field(default=A__			,metadata={'help': 'A folder containing the training data.'}			)
			lowerCamelCase__   :		Optional[str]							=							field(default=A__			,metadata={'help': 'A folder containing the validation data.'}			)
			lowerCamelCase__   :		Optional[float]							=							field(
			    default=0.1_5			,metadata={'help': 'Percent to split off of train for validation.'}			)
			lowerCamelCase__   :		Optional[int]							=							field(
			    default=A__			,metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of training examples to this '
			            'value if set.'
			        )
			    }			,)
			lowerCamelCase__   :		Optional[int]							=							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				:						Any       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      {}
						if self.train_dir is not None:
									SCREAMING_SNAKE_CASE__								=      self.train_dir
						if self.validation_dir is not None:
									SCREAMING_SNAKE_CASE__								=      self.validation_dir
						SCREAMING_SNAKE_CASE__								=      data_files if data_files else None





@dataclass
class    lowerCamelCase						:
			lowerCamelCase__   :		str							=							field(
			    default=A__			,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=A__			,metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}			)
			lowerCamelCase__   :		Optional[str]							=							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__   :		Optional[str]							=							field(
			    default=A__			,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=A__			,metadata={'help': 'Name or path of preprocessor config.'}			)
			lowerCamelCase__   :		bool							=							field(
			    default=A__			,metadata={
			        'help': (
			            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
			            'with private models).'
			        )
			    }			,)
			lowerCamelCase__   :		float							=							field(
			    default=0.7_5			,metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}			)
			lowerCamelCase__   :		bool							=							field(
			    default=A__			,metadata={'help': 'Whether or not to train with normalized pixel values as target.'}			)





@dataclass
class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		float							=							field(
			    default=1E-3			,metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}			)

def 	A       (     snake_case__    ):




			'''simple docstring'''


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

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      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.
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
			else:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      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"""		,				snake_case__		,				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()

			SCREAMING_SNAKE_CASE__								=      training_args.get_process_log_level()
			logger.setLevel(snake_case__    )
			transformers.utils.logging.set_verbosity(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.
			SCREAMING_SNAKE_CASE__								=      None
			if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
						SCREAMING_SNAKE_CASE__								=      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.
			SCREAMING_SNAKE_CASE__								=      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.
			SCREAMING_SNAKE_CASE__								=      None if """validation""" in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split		,				snake_case__    ) and data_args.train_val_split > 0.0:
						SCREAMING_SNAKE_CASE__								=      ds["""train"""].train_test_split(data_args.train_val_split    )
						SCREAMING_SNAKE_CASE__								=      split["""train"""]
						SCREAMING_SNAKE_CASE__								=      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.
			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,
			}
			if model_args.config_name:
						SCREAMING_SNAKE_CASE__								=      ViTMAEConfig.from_pretrained(model_args.config_name		,				**snake_case__    )
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE__								=      ViTMAEConfig.from_pretrained(model_args.model_name_or_path		,				**snake_case__    )
			else:
						SCREAMING_SNAKE_CASE__								=      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:
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained(model_args.image_processor_name		,				**snake_case__    )
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained(model_args.model_name_or_path		,				**snake_case__    )
			else:
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor()

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

			if training_args.do_train:
						SCREAMING_SNAKE_CASE__								=      ds["""train"""].column_names
			else:
						SCREAMING_SNAKE_CASE__								=      ds["""validation"""].column_names

			if data_args.image_column_name is not None:
						SCREAMING_SNAKE_CASE__								=      data_args.image_column_name
			elif "image" in column_names:
						SCREAMING_SNAKE_CASE__								=      """image"""
			elif "img" in column_names:
						SCREAMING_SNAKE_CASE__								=      """img"""
			else:
						SCREAMING_SNAKE_CASE__								=      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:
						SCREAMING_SNAKE_CASE__								=      image_processor.size["""shortest_edge"""]
			else:
						SCREAMING_SNAKE_CASE__								=      (image_processor.size["""height"""], image_processor.size["""width"""])
			SCREAMING_SNAKE_CASE__								=      Compose(
			    [
			        Lambda(lambda snake_case__    : img.convert("""RGB"""    ) if img.mode != "RGB" else img    ),
			        RandomResizedCrop(snake_case__		,				scale=(0.2, 1.0)		,				interpolation=InterpolationMode.BICUBIC    ),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean		,				std=image_processor.image_std    ),
			    ]    )

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

			# Compute absolute learning rate
			SCREAMING_SNAKE_CASE__								=      (
			    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
			)
			if training_args.base_learning_rate is not None:
						SCREAMING_SNAKE_CASE__								=      training_args.base_learning_rate * total_train_batch_size / 2_56

			# Initialize our trainer
			SCREAMING_SNAKE_CASE__								=      Trainer(
			    model=snake_case__		,				args=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=snake_case__		,				data_collator=snake_case__		,				)

			# Training
			if training_args.do_train:
						SCREAMING_SNAKE_CASE__								=      None
						if training_args.resume_from_checkpoint is not None:
									SCREAMING_SNAKE_CASE__								=      training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									SCREAMING_SNAKE_CASE__								=      last_checkpoint
						SCREAMING_SNAKE_CASE__								=      trainer.train(resume_from_checkpoint=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:
						SCREAMING_SNAKE_CASE__								=      trainer.evaluate()
						trainer.log_metrics("""eval"""		,				snake_case__    )
						trainer.save_metrics("""eval"""		,				snake_case__    )

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

def 	A       (     snake_case__    ):




			'''simple docstring'''


			main()


if __name__ == "__main__":
						main()


