

'''simple docstring'''

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
UpperCAmelCase					 =	logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')

UpperCAmelCase					 =	list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase					 =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			field(
           default="cifar10" ,      metadata={"help": "Name of a dataset from the datasets package"} )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."} ,      )
       lowerCAmelCase_												=			field(default=A ,      metadata={"help": "A folder containing the training data."} )
       lowerCAmelCase_												=			field(default=A ,      metadata={"help": "A folder containing the validation data."} )
       lowerCAmelCase_												=			field(
           default=0.1_5 ,      metadata={"help": "Percent to split off of train for validation."} )
       lowerCAmelCase_												=			field(default=3_2 ,      metadata={"help": "The size of the square patches to use for masking."} )
       lowerCAmelCase_												=			field(
           default=0.6 ,      metadata={"help": "Percentage of patches to mask."} ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of training examples to this "
                   "value if set."
               )
           } ,      )
       lowerCAmelCase_												=			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      :			Tuple   ):







            """simple docstring"""







            __lowercase      		={}
            if self.train_dir is not None:
                 __lowercase      		=self.train_dir
            if self.validation_dir is not None:
                 __lowercase      		=self.validation_dir
            __lowercase      		=data_files if data_files else None






@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			field(
           default=A ,      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."
               )
           } ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(A )} ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "Pretrained config name or path if not the same as model_name"} )
       lowerCAmelCase_												=			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_												=			field(
           default=A ,      metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"} ,      )
       lowerCAmelCase_												=			field(
           default="main" ,      metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} ,      )
       lowerCAmelCase_												=			field(default=A ,      metadata={"help": "Name or path of preprocessor config."} )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={
               "help": (
                   "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                   "with private models)."
               )
           } ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={
               "help": (
                   "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
               )
           } ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={
               "help": (
                   "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
               )
           } ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "Stride to use for the encoder."} ,      )






class 	lowerCAmelCase  :







       def __init__(				self      :			int    ,   __lowercase      :			int=192    ,   __lowercase      :			Dict=32    ,   __lowercase      :			Any=4    ,   __lowercase      :			Any=0.6   ):







            """simple docstring"""







            __lowercase      		=input_size
            __lowercase      		=mask_patch_size
            __lowercase      		=model_patch_size
            __lowercase      		=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'   )

            __lowercase      		=self.input_size // self.mask_patch_size
            __lowercase      		=self.mask_patch_size // self.model_patch_size

            __lowercase      		=self.rand_size**2
            __lowercase      		=int(np.ceil(self.token_count * self.mask_ratio   )   )







       def __call__(				self      :			str   ):







            """simple docstring"""







            __lowercase      		=np.random.permutation(self.token_count   )[: self.mask_count]
            __lowercase      		=np.zeros(self.token_count    ,   dtype=__lowercase   )
            __lowercase      		=1

            __lowercase      		=mask.reshape((self.rand_size, self.rand_size)   )
            __lowercase      		=mask.repeat(self.scale    ,   axis=0   ).repeat(self.scale    ,   axis=1   )

            return torch.tensor(mask.flatten()   )






def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     __lowercase      		=torch.stack([example['pixel_values'] for example in examples]			)
     __lowercase      		=torch.stack([example['mask'] for example in examples]			)
     return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def 					__UpperCamelCase				(						):






     '''simple docstring'''





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

     # 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()

     __lowercase      		=training_args.get_process_log_level()
     logger.setLevel(lowercase__			)
     transformers.utils.logging.set_verbosity(lowercase__			)
     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.
     __lowercase      		=None
     if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
          __lowercase      		=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.
     __lowercase      		=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.
     __lowercase      		=None if 'validation' in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split,       lowercase__			) and data_args.train_val_split > 0.0:
          __lowercase      		=ds['train'].train_test_split(data_args.train_val_split			)
          __lowercase      		=split['train']
          __lowercase      		=split['test']

     # Create config
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     __lowercase      		={
         '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:
          __lowercase      		=AutoConfig.from_pretrained(model_args.config_name_or_path,       **lowercase__			)
     elif model_args.model_name_or_path:
          __lowercase      		=AutoConfig.from_pretrained(model_args.model_name_or_path,       **lowercase__			)
     else:
          __lowercase      		=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(lowercase__,       'decoder_type'			):
          __lowercase      		='simmim'

     # adapt config
     __lowercase      		=model_args.image_size if model_args.image_size is not None else config.image_size
     __lowercase      		=model_args.patch_size if model_args.patch_size is not None else config.patch_size
     __lowercase      		=(
         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:
          __lowercase      		=AutoImageProcessor.from_pretrained(model_args.image_processor_name,       **lowercase__			)
     elif model_args.model_name_or_path:
          __lowercase      		=AutoImageProcessor.from_pretrained(model_args.model_name_or_path,       **lowercase__			)
     else:
          __lowercase      		={
              conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
          }
          __lowercase      		=IMAGE_PROCESSOR_TYPES[model_args.model_type]()

     # create model
     if model_args.model_name_or_path:
          __lowercase      		=AutoModelForMaskedImageModeling.from_pretrained(
              model_args.model_name_or_path,       from_tf=bool('.ckpt' in model_args.model_name_or_path			),       config=lowercase__,       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'			)
          __lowercase      		=AutoModelForMaskedImageModeling.from_config(lowercase__			)

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

     if data_args.image_column_name is not None:
          __lowercase      		=data_args.image_column_name
     elif "image" in column_names:
          __lowercase      		='image'
     elif "img" in column_names:
          __lowercase      		='img'
     else:
          __lowercase      		=column_names[0]

     # transformations as done in original SimMIM paper
     # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
     __lowercase      		=Compose(
         [
             Lambda(lambda lowercase__			: 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
     __lowercase      		=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(lowercase__    :	List[Any]			):
          __lowercase      		=[transforms(lowercase__			) for image in examples[image_column_name]]
          __lowercase      		=[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:
               __lowercase      		=ds['train'].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
          # Set the training transforms
          ds["train"].set_transform(lowercase__			)

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

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

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

     # Write model card and (optionally) push to hub
     __lowercase      		={
         '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(**lowercase__			)
     else:
          trainer.create_model_card(**lowercase__			)


if __name__ == "__main__":
       main()





'''simple docstring'''

def 					__UpperCamelCase				(						lowercase__    :	List[str],       lowercase__    :	Tuple			):






     '''simple docstring'''





     __lowercase      		=[0 for i in range(r + 1			)]
     # nc0 = 1
     __lowercase      		=1
     for i in range(1,       n + 1			):
          # to compute current row from previous row.
          __lowercase      		=min(lowercase__,       lowercase__			)
          while j > 0:
               c[j] += c[j - 1]
               j -= 1
     return c[r]







print(binomial_coefficient(n=10, r=5))


