

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 UpperCAmelCase						:


				'''simple docstring'''






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



						"""simple docstring"""





						A_		:    Dict         =				{}
						if self.train_dir is not None:
								A_		:    Optional[Any]         =				self.train_dir
						if self.validation_dir is not None:
								A_		:    Optional[Any]         =				self.validation_dir
						A_		:    str         =				data_files if data_files else None





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






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





@dataclass
class UpperCAmelCase						( UpperCamelCase_  ):


				'''simple docstring'''






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




def     UpperCamelCase						(	__lowercase						:			Optional[int]     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				torch.stack([example['pixel_values'] for example in examples]     )
		return {"pixel_values": pixel_values}




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Tuple         =				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_		:    int         =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
		else:
				A_		:    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_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()

		A_		:    List[Any]         =				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.
		A_		:    Any         =				None
		if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
				A_		:    Optional[int]         =				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_		:    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.
		A_		:    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:
				A_		:    List[Any]         =				ds["""train"""].train_test_split(data_args.train_val_split     )
				A_		:    List[str]         =				split["""train"""]
				A_		:    int         =				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_		:    int         =				{
		    """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_		:    List[Any]         =				ViTMAEConfig.from_pretrained(model_args.config_name						,**__lowerCAmelCase     )
		elif model_args.model_name_or_path:
				A_		:    List[str]         =				ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,**__lowerCAmelCase     )
		else:
				A_		:    int         =				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_		:    int         =				ViTImageProcessor.from_pretrained(model_args.image_processor_name						,**__lowerCAmelCase     )
		elif model_args.model_name_or_path:
				A_		:    Tuple         =				ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,**__lowerCAmelCase     )
		else:
				A_		:    Optional[Any]         =				ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				A_		:    List[str]         =				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'     )
				A_		:    Union[str, Any]         =				ViTMAEForPreTraining(__lowerCAmelCase     )

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

		if data_args.image_column_name is not None:
				A_		:    List[Any]         =				data_args.image_column_name
		elif "image" in column_names:
				A_		:    Tuple         =				"""image"""
		elif "img" in column_names:
				A_		:    Dict         =				"""img"""
		else:
				A_		:    List[str]         =				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_		:    str         =				image_processor.size["""shortest_edge"""]
		else:
				A_		:    List[str]         =				(image_processor.size["""height"""], image_processor.size["""width"""])
		A_		:    Any         =				Compose(
		    [
		        Lambda(lambda __lowercase     : 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(__lowercase						:			Optional[Any]     ):
				A_		:    Optional[int]         =				[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:
						A_		:    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:
						A_		:    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
		A_		:    Dict         =				(
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				A_		:    List[str]         =				training_args.base_learning_rate * total_train_batch_size / 2_56

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

		# Write model card and (optionally) push to hub
		A_		:    str         =				{
		    """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     UpperCamelCase						(	__lowercase						:			List[str]     ):
		'''simple docstring'''




		main()


if __name__ == "__main__":
	main()







from __future__ import annotations

import requests

_UpperCAmelCase            =		set(
    """approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			int = 1						,__lowercase						:			str = "new"						,__lowercase						:			list | None = None     ):
		'''simple docstring'''




		A_		:    Tuple         =				wanted_data or []
		if invalid_search_terms := ", ".join(sorted(set(__lowercase     ) - valid_terms     )     ):
				A_		:    int         =				f'''Invalid search term: {invalid_search_terms}'''
				raise ValueError(__lowercase     )
		A_		:    Optional[int]         =				requests.get(
		    f'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''						,headers={'User-agent': 'A random string'}						,)
		if response.status_code == 4_29:
				raise requests.HTTPError

		A_		:    Optional[Any]         =				response.json()
		if not wanted_data:
				return {id_: data["data"]["children"][id_] for id_ in range(__lowercase     )}

		A_		:    Union[str, Any]         =				{}
		for id_ in range(__lowercase     ):
				A_		:    List[str]         =				{
				    item: data['data']['children'][id_]['data'][item] for item in wanted_data
				}
		return data_dict


if __name__ == "__main__":
	# If you get Error 429, that means you are rate limited.Try after some time
	print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))






