



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



			def    _lowerCamelCase	(						self       :  Any)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       {}
						if self.train_dir is not None:
									_UpperCAmelCase			=       self.train_dir
						if self.validation_dir is not None:
									_UpperCAmelCase			=       self.validation_dir
						_UpperCAmelCase			=       data_files if data_files else None







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







@dataclass
class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					field(
			    default=1e-3    ,						metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}						)





def 							A     (					_UpperCAmelCase    :       List[str]   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       torch.stack([example['pixel_values'] for example in examples]   )
			return {"pixel_values": pixel_values}





def 							A     (					)    ->			Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase			=       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	,					_UpperCAmelCase	,					_UpperCAmelCase			=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
			else:
						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       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'							,       _UpperCAmelCase							,       _UpperCAmelCase   )

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

			# create model
			if model_args.model_name_or_path:
						_UpperCAmelCase			=       ViTMAEForPreTraining.from_pretrained(
						    model_args.model_name_or_path							,       from_tf=bool('.ckpt' in model_args.model_name_or_path   )							,       config=_UpperCAmelCase							,       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			=       ViTMAEForPreTraining(_UpperCAmelCase   )

			if training_args.do_train:
						_UpperCAmelCase			=       ds['train'].column_names
			else:
						_UpperCAmelCase			=       ds['validation'].column_names

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

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

			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			=       (
									    ds['validation'].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
									)
						# Set the validation transforms
						ds["validation"].set_transform(_UpperCAmelCase   )

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

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

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

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





def 							A     (					_UpperCAmelCase    :       List[str]   )    ->			Union[str, Any]:







			'''simple docstring'''
			main()


if __name__ == "__main__":
							main()





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''decision_transformer'''
			UpperCamelCase        =					['''past_key_values''']
			UpperCamelCase        =					{
			    '''max_position_embeddings''': '''n_positions''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}



			def __init__(						self       :  Any			,		A       :  Optional[int]=17			,		A       :  List[str]=4			,		A       :  int=1_28			,		A       :  Union[str, Any]=40_96			,		A       :  Any=True			,		A       :  Any=1			,		A       :  List[Any]=10_24			,		A       :  List[Any]=3			,		A       :  Tuple=1			,		A       :  Any=None			,		A       :  Optional[int]="relu"			,		A       :  Union[str, Any]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=1E-5			,		A       :  List[Any]=0.0_2			,		A       :  Tuple=True			,		A       :  Union[str, Any]=True			,		A       :  str=5_02_56			,		A       :  Union[str, Any]=5_02_56			,		A       :  List[Any]=False			,		A       :  Optional[int]=False			,		**A       :  int			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       state_dim
						_UpperCAmelCase			=       act_dim
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       max_ep_len
						_UpperCAmelCase			=       action_tanh
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       n_positions
						_UpperCAmelCase			=       n_layer
						_UpperCAmelCase			=       n_head
						_UpperCAmelCase			=       n_inner
						_UpperCAmelCase			=       activation_function
						_UpperCAmelCase			=       resid_pdrop
						_UpperCAmelCase			=       embd_pdrop
						_UpperCAmelCase			=       attn_pdrop
						_UpperCAmelCase			=       layer_norm_epsilon
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       scale_attn_weights
						_UpperCAmelCase			=       use_cache
						_UpperCAmelCase			=       scale_attn_by_inverse_layer_idx
						_UpperCAmelCase			=       reorder_and_upcast_attn

						_UpperCAmelCase			=       bos_token_id
						_UpperCAmelCase			=       eos_token_id

						super().__init__(bos_token_id=A			,		eos_token_id=A			,		**A)
