


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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   :					Optional[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")

A   :					Optional[Any]	=      list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A   :					Optional[int]	=      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class     _lowercase				:


       """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. If not set, will try to use 'image' or 'img'."}							,						)
       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=32							,						metadata={"help": "The size of the square patches to use for masking."})
       A__      		=    field(
           default=0.6							,						metadata={"help": "Percentage of patches to mask."}							,						)
       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 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						{}
             if self.train_dir is not None:
                   lowerCamelCase__	:       Any					=						self.train_dir
             if self.validation_dir is not None:
                   lowerCamelCase__	:       Tuple					=						self.validation_dir
             lowerCamelCase__	:       int					=						data_files if data_files else None







@dataclass
class     _lowercase				:


       """simple docstring"""




       A__      		=    field(
           default=_UpperCAmelCase							,						metadata={
               "help": (
                   "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
                   "checkpoint identifier on the hub. "
                   "Don't set if you want to train a model from scratch."
               )
           }							,						)
       A__      		=    field(
           default=_UpperCAmelCase							,						metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_UpperCAmelCase)}							,						)
       A__      		=    field(
           default=_UpperCAmelCase							,						metadata={"help": "Pretrained config name or path if not the same as model_name"})
       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 (cache) the pretrained models/datasets downloaded from the hub"}							,						)
       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=_UpperCAmelCase							,						metadata={
               "help": (
                   "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
               )
           }							,						)
       A__      		=    field(
           default=_UpperCAmelCase							,						metadata={
               "help": (
                   "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
               )
           }							,						)
       A__      		=    field(
           default=_UpperCAmelCase							,						metadata={"help": "Stride to use for the encoder."}							,						)







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Dict      ,   __lowerCamelCase							:    List[Any]=192      ,   __lowerCamelCase							:    Optional[int]=32      ,   __lowerCamelCase							:    Optional[Any]=4      ,   __lowerCamelCase							:    List[str]=0.6 ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						input_size
             lowerCamelCase__	:       List[Any]					=						mask_patch_size
             lowerCamelCase__	:       Tuple					=						model_patch_size
             lowerCamelCase__	:       Dict					=						mask_ratio

             if self.input_size % self.mask_patch_size != 0:
                   raise ValueError("Input size must be divisible by mask patch size" )
             if self.mask_patch_size % self.model_patch_size != 0:
                   raise ValueError("Mask patch size must be divisible by model patch size" )

             lowerCamelCase__	:       List[Any]					=						self.input_size // self.mask_patch_size
             lowerCamelCase__	:       List[str]					=						self.mask_patch_size // self.model_patch_size

             lowerCamelCase__	:       Optional[int]					=						self.rand_size**2
             lowerCamelCase__	:       Union[str, Any]					=						int(np.ceil(self.token_count * self.mask_ratio ) )





       def __call__(     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						np.random.permutation(self.token_count )[: self.mask_count]
             lowerCamelCase__	:       Optional[Any]					=						np.zeros(self.token_count      ,   dtype=lowercase__ )
             lowerCamelCase__	:       Optional[Any]					=						1

             lowerCamelCase__	:       Any					=						mask.reshape((self.rand_size, self.rand_size) )
             lowerCamelCase__	:       List[Any]					=						mask.repeat(self.scale      ,   axis=0 ).repeat(self.scale      ,   axis=1 )

             return torch.tensor(mask.flatten() )



def    lowercase_	(	_A    :					Dict		):

      """simple docstring"""




      lowerCamelCase__	:       Any					=						torch.stack([example["pixel_values"] for example in examples]		)
      lowerCamelCase__	:       Optional[int]					=						torch.stack([example["mask"] for example in examples]		)
      return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def    lowercase_	(	):

      """simple docstring"""




      lowerCamelCase__	:       Any					=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
      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.
            lowerCamelCase__	:       Tuple					=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
      else:
            lowerCamelCase__	:       List[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_mim"					,      SCREAMING_SNAKE_CASE__					,      SCREAMING_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()

      lowerCamelCase__	:       Optional[int]					=						training_args.get_process_log_level()
      logger.setLevel(SCREAMING_SNAKE_CASE__		)
      transformers.utils.logging.set_verbosity(SCREAMING_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.
      lowerCamelCase__	:       List[Any]					=						None
      if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
            lowerCamelCase__	:       Dict					=						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.
      lowerCamelCase__	:       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.
      lowerCamelCase__	:       int					=						None if """validation""" in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split					,      SCREAMING_SNAKE_CASE__		) and data_args.train_val_split > 0.0:
            lowerCamelCase__	:       List[Any]					=						ds["""train"""].train_test_split(data_args.train_val_split		)
            lowerCamelCase__	:       List[str]					=						split["""train"""]
            lowerCamelCase__	:       List[str]					=						split["""test"""]

      # Create config
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      lowerCamelCase__	:       Dict					=						{
          """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_or_path:
            lowerCamelCase__	:       str					=						AutoConfig.from_pretrained(model_args.config_name_or_path					,      **SCREAMING_SNAKE_CASE__		)
      elif model_args.model_name_or_path:
            lowerCamelCase__	:       str					=						AutoConfig.from_pretrained(model_args.model_name_or_path					,      **SCREAMING_SNAKE_CASE__		)
      else:
            lowerCamelCase__	:       Dict					=						CONFIG_MAPPING[model_args.model_type]()
            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}"		)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
      if hasattr(SCREAMING_SNAKE_CASE__					,      "decoder_type"		):
            lowerCamelCase__	:       str					=						"""simmim"""

      # adapt config
      lowerCamelCase__	:       Tuple					=						model_args.image_size if model_args.image_size is not None else config.image_size
      lowerCamelCase__	:       str					=						model_args.patch_size if model_args.patch_size is not None else config.patch_size
      lowerCamelCase__	:       List[Any]					=						(
          model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
      )

      config.update(
          {
              "image_size": model_args.image_size,
              "patch_size": model_args.patch_size,
              "encoder_stride": model_args.encoder_stride,
          }		)

      # create image processor
      if model_args.image_processor_name:
            lowerCamelCase__	:       Union[str, Any]					=						AutoImageProcessor.from_pretrained(model_args.image_processor_name					,      **SCREAMING_SNAKE_CASE__		)
      elif model_args.model_name_or_path:
            lowerCamelCase__	:       Optional[Any]					=						AutoImageProcessor.from_pretrained(model_args.model_name_or_path					,      **SCREAMING_SNAKE_CASE__		)
      else:
            lowerCamelCase__	:       str					=						{
                conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
            }
            lowerCamelCase__	:       Tuple					=						IMAGE_PROCESSOR_TYPES[model_args.model_type]()

      # create model
      if model_args.model_name_or_path:
            lowerCamelCase__	:       List[Any]					=						AutoModelForMaskedImageModeling.from_pretrained(
                model_args.model_name_or_path					,      from_tf=bool(".ckpt" in model_args.model_name_or_path		)					,      config=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					,      )
      else:
            logger.info("Training new model from scratch"		)
            lowerCamelCase__	:       Optional[int]					=						AutoModelForMaskedImageModeling.from_config(SCREAMING_SNAKE_CASE__		)

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

      if data_args.image_column_name is not None:
            lowerCamelCase__	:       Any					=						data_args.image_column_name
      elif "image" in column_names:
            lowerCamelCase__	:       str					=						"""image"""
      elif "img" in column_names:
            lowerCamelCase__	:       Dict					=						"""img"""
      else:
            lowerCamelCase__	:       Optional[int]					=						column_names[0]

      # transformations as done in original SimMIM paper
      # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
      lowerCamelCase__	:       List[str]					=						Compose(
          [
              Lambda(lambda _A		: img.convert("RGB"		) if img.mode != "RGB" else img		),
              RandomResizedCrop(model_args.image_size					,      scale=(0.67, 1.0)					,      ratio=(3.0 / 4.0, 4.0 / 3.0)		),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean					,      std=image_processor.image_std		),
          ]		)

      # create mask generator
      lowerCamelCase__	:       Any					=						MaskGenerator(
          input_size=model_args.image_size					,      mask_patch_size=data_args.mask_patch_size					,      model_patch_size=model_args.patch_size					,      mask_ratio=data_args.mask_ratio					,      )

      def preprocess_images(_A    :					List[Any]		):
            lowerCamelCase__	:       List[str]					=						[transforms(SCREAMING_SNAKE_CASE__		) for image in examples[image_column_name]]
            lowerCamelCase__	:       Any					=						[mask_generator() for i in range(len(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:
                  lowerCamelCase__	:       Optional[int]					=						ds["""train"""].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
            # Set the training transforms
            ds["train"].set_transform(SCREAMING_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:
                  lowerCamelCase__	:       Union[str, Any]					=						(
                      ds["""validation"""].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
                  )
            # Set the validation transforms
            ds["validation"].set_transform(SCREAMING_SNAKE_CASE__		)

      # Initialize our trainer
      lowerCamelCase__	:       Tuple					=						Trainer(
          model=SCREAMING_SNAKE_CASE__					,      args=SCREAMING_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=SCREAMING_SNAKE_CASE__					,      data_collator=SCREAMING_SNAKE_CASE__					,      )

      # Training
      if training_args.do_train:
            lowerCamelCase__	:       Any					=						None
            if training_args.resume_from_checkpoint is not None:
                  lowerCamelCase__	:       str					=						training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  lowerCamelCase__	:       int					=						last_checkpoint
            lowerCamelCase__	:       List[Any]					=						trainer.train(resume_from_checkpoint=SCREAMING_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:
            lowerCamelCase__	:       List[Any]					=						trainer.evaluate()
            trainer.log_metrics("eval"					,      SCREAMING_SNAKE_CASE__		)
            trainer.save_metrics("eval"					,      SCREAMING_SNAKE_CASE__		)

      # Write model card and (optionally) push to hub
      lowerCamelCase__	:       Optional[Any]					=						{
          """finetuned_from""": model_args.model_name_or_path,
          """tasks""": """masked-image-modeling""",
          """dataset""": data_args.dataset_name,
          """tags""": ["""masked-image-modeling"""],
      }
      if training_args.push_to_hub:
            trainer.push_to_hub(**SCREAMING_SNAKE_CASE__		)
      else:
            trainer.create_model_card(**SCREAMING_SNAKE_CASE__		)


if __name__ == "__main__":
    main()






import os
from datetime import datetime as dt

from github import Github


A   :					Union[str, Any]	=      [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "enhancement",
    "new pipeline/model",
    "new scheduler",
    "wip",
]

def    lowercase_	(	):

      """simple docstring"""




      lowerCamelCase__	:       Optional[int]					=						Github(os.environ["GITHUB_TOKEN"]		)
      lowerCamelCase__	:       str					=						g.get_repo("huggingface/diffusers"		)
      lowerCamelCase__	:       Optional[int]					=						repo.get_issues(state="open"		)

      for issue in open_issues:
            lowerCamelCase__	:       str					=						sorted(issue.get_comments()					,      key=lambda _A		: i.created_at					,      reverse=_A		)
            lowerCamelCase__	:       str					=						comments[0] if len(_A		) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()		)
            ):
                  # Closes the issue after 7 days of inactivity since the Stalebot notification.
                  issue.edit(state="closed"		)
            elif (
                "stale" in issue.get_labels()
                and last_comment is not None
                and last_comment.user.login != "github-actions[bot]"
            ):
                  # Opens the issue if someone other than Stalebot commented.
                  issue.edit(state="open"		)
                  issue.remove_from_labels("stale"		)
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()		)
            ):
                  # Post a Stalebot notification after 23 days of inactivity.
                  issue.create_comment(
                      "This issue has been automatically marked as stale because it has not had "
                      "recent activity. If you think this still needs to be addressed "
                      "please comment on this thread.\n\nPlease note that issues that do not follow the "
                      "[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) "
                      "are likely to be ignored."		)
                  issue.add_to_labels("stale"		)


if __name__ == "__main__":
    main()


