






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
__lowerCamelCase      :    str   		=   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 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






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



					def  _lowercase  (      self  :						List[Any] ):
						snake_case__				:   int     =						{}
						if self.train_dir is not None:
							snake_case__				:   Tuple     =						self.train_dir
						if self.validation_dir is not None:
							snake_case__				:   Dict     =						self.validation_dir
						snake_case__				:   str     =						data_files if data_files else None



@dataclass
class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






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



@dataclass
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	snake_case__				:   Union[str, Any]     =						torch.stack([example["pixel_values"] for example in examples]     )
	return {"pixel_values": pixel_values}







def   SCREAMING_SNAKE_CASE       (				):
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	snake_case__				:   int     =						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.
		snake_case__,		snake_case__,		snake_case__				:   List[Any]     =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
	else:
		snake_case__,		snake_case__,		snake_case__				:   Optional[Any]     =						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()

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

	# create model
	if model_args.model_name_or_path:
		snake_case__				:   Optional[Any]     =						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"     )
		snake_case__				:   Tuple     =						ViTMAEForPreTraining(snake_case_     )

	if training_args.do_train:
		snake_case__				:   Union[str, Any]     =						ds["train"].column_names
	else:
		snake_case__				:   Any     =						ds["validation"].column_names

	if data_args.image_column_name is not None:
		snake_case__				:   List[Any]     =						data_args.image_column_name
	elif "image" in column_names:
		snake_case__				:   Tuple     =						"image"
	elif "img" in column_names:
		snake_case__				:   List[str]     =						"img"
	else:
		snake_case__				:   Any     =						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:
		snake_case__				:   int     =						image_processor.size["shortest_edge"]
	else:
		snake_case__				:   List[Any]     =						(image_processor.size["height"], image_processor.size["width"])
	snake_case__				:   Optional[Any]     =						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_      :							Dict     ):
		snake_case__				:   Optional[Any]     =						[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:
			snake_case__				:   Any     =						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:
			snake_case__				:   Tuple     =						(
			    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
	snake_case__				:   int     =						(
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		snake_case__				:   Optional[Any]     =						training_args.base_learning_rate * total_train_batch_size / 256

	# Initialize our trainer
	snake_case__				:   Optional[Any]     =						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:
		snake_case__				:   Tuple     =						None
		if training_args.resume_from_checkpoint is not None:
			snake_case__				:   Any     =						training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			snake_case__				:   Optional[int]     =						last_checkpoint
		snake_case__				:   List[Any]     =						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:
		snake_case__				:   int     =						trainer.evaluate()
		trainer.log_metrics("eval"  ,						snake_case_     )
		trainer.save_metrics("eval"  ,						snake_case_     )

	# Write model card and (optionally) push to hub
	snake_case__				:   Union[str, Any]     =						{
	    "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   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]     ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()











import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


__lowerCamelCase      :    Optional[int]   		=   """\
@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}

"""

__lowerCamelCase      :    str   		=   """\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""

__lowerCamelCase      :    str   		=   """
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].
    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: \"c\" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric('mauve')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,     _KWARGS_DESCRIPTION		)
class 							SCREAMING_SNAKE_CASE__		(       datasets.Metric		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Dict ):
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,	citation=_CITATION							,	homepage="https://github.com/krishnap25/mauve"							,	inputs_description=_KWARGS_DESCRIPTION							,	features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"							,	id="sequence" ),
						            "references": datasets.Value("string"							,	id="sequence" ),
						        } )							,	codebase_urls=["https://github.com/krishnap25/mauve"]							,	reference_urls=[
						        "https://arxiv.org/abs/2102.01454",
						        "https://github.com/krishnap25/mauve",
						    ]							,	)






					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Dict							,	__A  :						List[str]							,	__A  :						int=None							,	__A  :						List[Any]=None							,	__A  :						Optional[int]=None							,	__A  :						List[Any]=None							,	__A  :						Union[str, Any]="auto"							,	__A  :						Optional[Any]=-1							,	__A  :						Optional[Any]=0.9							,	__A  :						Any=5							,	__A  :						List[Any]=5_0_0							,	__A  :						Tuple="gpt2-large"							,	__A  :						Optional[Any]=-1							,	__A  :						str=1_0_2_4							,	__A  :						Tuple=2_5							,	__A  :						str=5							,	__A  :						Optional[int]=True							,	__A  :						Any=2_5							,	):
						snake_case__				:   List[Any]     =						compute_mauve(
						    p_text=__A							,	q_text=__A							,	p_features=__A							,	q_features=__A							,	p_tokens=__A							,	q_tokens=__A							,	num_buckets=__A							,	pca_max_data=__A							,	kmeans_explained_var=__A							,	kmeans_num_redo=__A							,	kmeans_max_iter=__A							,	featurize_model_name=__A							,	device_id=__A							,	max_text_length=__A							,	divergence_curve_discretization_size=__A							,	mauve_scaling_factor=__A							,	verbose=__A							,	seed=__A							,	)
						return out



