

'''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
_lowerCAmelCase         =						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   :
 __SCREAMING_SNAKE_CASE					:Optional[str]      	=					field(
     default="""cifar10"""		,metadata={"""help""": """Name of a dataset from the datasets package"""}     )
 __SCREAMING_SNAKE_CASE					:Optional[str]      	=					field(
     default=__a		,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}     )
 __SCREAMING_SNAKE_CASE					:Optional[str]      	=					field(
     default=__a		,metadata={"""help""": """The column name of the images in the files."""}     )
 __SCREAMING_SNAKE_CASE					:Optional[str]      	=					field(default=__a		,metadata={"""help""": """A folder containing the training data."""}     )
 __SCREAMING_SNAKE_CASE					:Optional[str]      	=					field(default=__a		,metadata={"""help""": """A folder containing the validation data."""}     )
 __SCREAMING_SNAKE_CASE					:Optional[float]      	=					field(
     default=0.15		,metadata={"""help""": """Percent to split off of train for validation."""}     )
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					field(
     default=__a		,metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of training examples to this """
             """value if set."""
         )
     }		,)
 __SCREAMING_SNAKE_CASE					: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 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    {}
      if self.train_dir is not None:
           __magic_name__						       =    self.train_dir
      if self.validation_dir is not None:
           __magic_name__						       =    self.validation_dir
      __magic_name__						       =    data_files if data_files else None






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






@dataclass
class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:float      	=					field(
     default=1E-3		,metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}     )




def 						UpperCamelCase	(			a			)				->     List[str]:






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




def 						UpperCamelCase	(			)				->     Optional[int]:






     '''simple docstring'''
     # 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.

     __magic_name__						       =    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.
          __magic_name__       ,  __magic_name__       ,  __magic_name__						       =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
     else:
          __magic_name__       ,  __magic_name__       ,  __magic_name__						       =    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'''		,						a		,						a			)

     # 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()

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

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

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

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

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

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

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

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

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

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




def 						UpperCamelCase	(			a			)				->     int:






     '''simple docstring'''
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
      main()









'''simple docstring'''




import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_lowerCAmelCase         =						"\\n\n"

_lowerCAmelCase         =						"\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"

_lowerCAmelCase         =						"\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to 'cuda' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        78.22\n        >>> print(round(results[\"perplexities\"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = datasets.load_dataset(\"wikitext\",\n        ...                                     \"wikitext-2-raw-v1\",\n        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!='']\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        60.35\n        >>> print(round(results[\"perplexities\"][0], 2))\n        81.12\n"






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



 def 			snake_case__  (   self		:   Dict      ):
      return datasets.MetricInfo(
          description=_DESCRIPTION			,				citation=_CITATION			,				inputs_description=_KWARGS_DESCRIPTION			,				features=datasets.Features(
              {
                  '''input_texts''': datasets.Value('''string'''      ),
              }      )			,				reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']			,				)



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   int			,				a__		:   Dict			,				a__		:   int = 16			,				a__		:   bool = True			,				a__		:   Any=None      ):
      if device is not None:
           assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
           if device == "gpu":
                __magic_name__						       =    '''cuda'''
      else:
           __magic_name__						       =    '''cuda''' if torch.cuda.is_available() else '''cpu'''

      __magic_name__						       =    AutoModelForCausalLM.from_pretrained(a__      )
      __magic_name__						       =    model.to(a__      )

      __magic_name__						       =    AutoTokenizer.from_pretrained(a__      )

      # if batch_size > 1 (which generally leads to padding being required), and
      # if there is not an already assigned pad_token, assign an existing
      # special token to also be the padding token
      if tokenizer.pad_token is None and batch_size > 1:
           __magic_name__						       =    list(tokenizer.special_tokens_map_extended.values()      )
           # check that the model already has at least one special token defined
           assert (
               len(a__      ) > 0
           ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
           # assign one of the special tokens to also be the pad token
           tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]}      )

      if add_start_token:
           # leave room for <BOS> token to be added:
           assert (
               tokenizer.bos_token is not None
           ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
           __magic_name__						       =    model.config.max_length - 1
      else:
           __magic_name__						       =    model.config.max_length

      __magic_name__						       =    tokenizer(
          a__			,				add_special_tokens=a__			,				padding=a__			,				truncation=a__			,				max_length=a__			,				return_tensors='''pt'''			,				return_attention_mask=a__			,				).to(a__      )

      __magic_name__						       =    encodings['''input_ids''']
      __magic_name__						       =    encodings['''attention_mask''']

      # check that each input is long enough:
      if add_start_token:
           assert torch.all(torch.ge(attn_masks.sum(1      )			,				1      )      ), "Each input text must be at least one token long."
      else:
           assert torch.all(
               torch.ge(attn_masks.sum(1      )			,				2      )      ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

      __magic_name__						       =    []
      __magic_name__						       =    CrossEntropyLoss(reduction='''none'''      )

      for start_index in logging.tqdm(range(0			,				len(a__      )			,				a__      )      ):
           __magic_name__						       =    min(start_index + batch_size			,				len(a__      )      )
           __magic_name__						       =    encoded_texts[start_index:end_index]
           __magic_name__						       =    attn_masks[start_index:end_index]

           if add_start_token:
                __magic_name__						       =    torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0      )      ).to(a__      )
                __magic_name__						       =    torch.cat([bos_tokens_tensor, encoded_batch]			,				dim=1      )
                __magic_name__						       =    torch.cat(
                    [torch.ones(bos_tokens_tensor.size()			,				dtype=torch.intaa      ).to(a__      ), attn_mask]			,				dim=1      )

           __magic_name__						       =    encoded_batch

           with torch.no_grad():
                __magic_name__						       =    model(a__			,				attention_mask=a__      ).logits

           __magic_name__						       =    out_logits[..., :-1, :].contiguous()
           __magic_name__						       =    labels[..., 1:].contiguous()
           __magic_name__						       =    attn_mask[..., 1:].contiguous()

           __magic_name__						       =    torch.expa(
               (loss_fct(shift_logits.transpose(1			,				2      )			,				a__      ) * shift_attention_mask_batch).sum(1      )
               / shift_attention_mask_batch.sum(1      )      )

           ppls += perplexity_batch.tolist()

      return {"perplexities": ppls, "mean_perplexity": np.mean(a__      )}






