

"""simple docstring"""




import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


A_							:			Tuple											= re.compile(R"\s+")

def 	A       (     snake_case__    ):




			'''simple docstring'''


			return {"hash": hashlib.mda(re.sub(snake_case__		,				""""""		,				example["""content"""]    ).encode("""utf-8"""    )    ).hexdigest()}

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      [len(snake_case__    ) for line in example["""content"""].splitlines()]
			return {"line_mean": np.mean(snake_case__    ), "line_max": max(snake_case__    )}

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      np.mean([c.isalnum() for c in example["""content"""]]    )
			return {"alpha_frac": alpha_frac}

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			if example["hash"] in uniques:
						uniques.remove(example["""hash"""]    )
						return True
			else:
						return False

def 	A       (     snake_case__		,				snake_case__=5    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      ["""auto-generated""", """autogenerated""", """automatically generated"""]
			SCREAMING_SNAKE_CASE__								=      example["""content"""].splitlines()
			for _, line in zip(range(snake_case__    )		,				snake_case__    ):
						for keyword in keywords:
									if keyword in line.lower():
												return {"autogenerated": True}
			else:
						return {"autogenerated": False}

def 	A       (     snake_case__		,				snake_case__=5		,				snake_case__=0.05    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      ["""unit tests""", """test file""", """configuration file"""]
			SCREAMING_SNAKE_CASE__								=      example["""content"""].splitlines()
			SCREAMING_SNAKE_CASE__								=      0
			SCREAMING_SNAKE_CASE__								=      0
			# first test
			for _, line in zip(range(snake_case__    )		,				snake_case__    ):
						for keyword in keywords:
									if keyword in line.lower():
												return {"config_or_test": True}
    # second test
			SCREAMING_SNAKE_CASE__								=      example["""content"""].count("""\n"""    )
			SCREAMING_SNAKE_CASE__								=      int(coeff * nlines    )
			for line in lines:
						count_config += line.lower().count("""config"""    )
						count_test += line.lower().count("""test"""    )
						if count_config > threshold or count_test > threshold:
									return {"config_or_test": True}
			return {"config_or_test": False}

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      ["""def """, """class """, """for """, """while """]
			SCREAMING_SNAKE_CASE__								=      example["""content"""].splitlines()
			for line in lines:
						for keyword in keywords:
									if keyword in line.lower():
												return {"has_no_keywords": False}
			return {"has_no_keywords": True}

def 	A       (     snake_case__		,				snake_case__=4    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      example["""content"""].splitlines()
			SCREAMING_SNAKE_CASE__								=      0
			for line in lines:
						counter += line.lower().count("""="""    )
						if counter > minimum:
									return {"has_few_assignments": False}
			return {"has_few_assignments": True}

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      tokenizer(example["""content"""]		,				truncation=snake_case__    )["""input_ids"""]
			SCREAMING_SNAKE_CASE__								=      len(example["""content"""]    ) / len(snake_case__    )
			return {"ratio": ratio}

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      {}
			results.update(get_hash(snake_case__    )    )
			results.update(line_stats(snake_case__    )    )
			results.update(alpha_stats(snake_case__    )    )
			results.update(char_token_ratio(snake_case__    )    )
			results.update(is_autogenerated(snake_case__    )    )
			results.update(is_config_or_test(snake_case__    )    )
			results.update(has_no_keywords(snake_case__    )    )
			results.update(has_few_assignments(snake_case__    )    )
			return results

def 	A       (     snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			if not check_uniques(snake_case__		,				snake_case__    ):
						return False
			elif example["autogenerated"]:
						return False
			elif example["line_max"] > args.line_max:
						return False
			elif example["line_mean"] > args.line_mean:
						return False
			elif example["alpha_frac"] < args.alpha_frac:
						return False
			elif example["ratio"] < args.min_token_ratio:
						return False
			elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
						return False
			elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
						return False
			elif example["has_few_assignments"]:
						return False
			else:
						return True

def 	A       (     snake_case__    ):




			'''simple docstring'''


			with open(snake_case__		,				"""rb"""    ) as f_in:
						with gzip.open(str(snake_case__    ) + """.gz"""		,				"""wb"""		,				compresslevel=6    ) as f_out:
									shutil.copyfileobj(snake_case__		,				snake_case__    )
			os.unlink(snake_case__    )


# Settings
A_							:			Any											= HfArgumentParser(PreprocessingArguments)
A_							:			int											= parser.parse_args()
if args.num_workers is None:
						A_							:			List[Any]											= multiprocessing.cpu_count()
A_							:			Union[str, Any]											= AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
A_							:			Any											= time.time()
A_							:			int											= load_dataset(args.dataset_name, split="train")
print(F'Time to load dataset: {time.time()-t_start:.2f}')

# Run preprocessing
A_							:			Tuple											= time.time()
A_							:			Union[str, Any]											= ds.map(preprocess, num_proc=args.num_workers)
print(F'Time to preprocess dataset: {time.time()-t_start:.2f}')

# Deduplicate hashes
A_							:			Optional[Any]											= set(ds.unique("hash"))
A_							:			Dict											= len(uniques) / len(ds)
print(F'Fraction of duplicates: {1-frac:.2%}')

# Deduplicate data and apply heuristics
A_							:			str											= time.time()
A_							:			List[Any]											= ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args})
print(F'Time to filter dataset: {time.time()-t_start:.2f}')
print(F'Size of filtered dataset: {len(ds_filter)}')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
						A_							:			str											= time.time()
						A_							:			List[Any]											= deduplicate_dataset(ds_filter, args.jaccard_threshold)
						print(F'Time to deduplicate dataset: {time.time()-t_start:.2f}')
						print(F'Size of deduplicate dataset: {len(ds_filter)}')

# Save data in batches of samples_per_file
A_							:			Dict											= Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
						with open(output_dir / "duplicate_clusters.json", "w") as f:
												json.dump(duplicate_clusters, f)

A_							:			Optional[Any]											= output_dir / "data"
data_dir.mkdir(exist_ok=True)

A_							:			Tuple											= time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
						A_							:			Dict											= str(data_dir / F'file-{file_number+1:012}.json')
						A_							:			Any											= min(len(ds_filter), index + args.samples_per_file)
						ds_filter.select(list(range(index, end_index))).to_json(file_path)
						compress_file(file_path)




print(F'Time to save dataset: {time.time()-t_start:.2f}')





"""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()


