


'''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
UpperCAmelCase_ :     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					:




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


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			{}
  if self.train_dir is not None:
   __A  :		Any       =			self.train_dir
  if self.validation_dir is not None:
   __A  :		int       =			self.validation_dir
  __A  :		List[str]       =			data_files if data_files else None


@dataclass
class 						_lowerCamelCase					:




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


@dataclass
class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		float    							=						field(
     default=1e-3    ,		metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}				)


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 __A  :		List[str]       =			torch.stack([example['pixel_values'] for example in examples]			)
 return {"pixel_values": pixel_values}


def 			_lowercase       (				):
 # 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.

 __A  :		List[Any]       =			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.
  __A     ,__A     ,__A  :		Dict       =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
 else:
  __A     ,__A     ,__A  :		Tuple       =			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',				UpperCamelCase__,				UpperCamelCase__			)

 # 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()

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

 # create model
 if model_args.model_name_or_path:
  __A  :		Optional[int]       =			ViTMAEForPreTraining.from_pretrained(
      model_args.model_name_or_path,				from_tf=bool('.ckpt' in model_args.model_name_or_path			),				config=UpperCamelCase__,				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'			)
  __A  :		str       =			ViTMAEForPreTraining(UpperCamelCase__			)

 if training_args.do_train:
  __A  :		Union[str, Any]       =			ds['train'].column_names
 else:
  __A  :		Optional[int]       =			ds['validation'].column_names

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

 def preprocess_images(UpperCamelCase__       :					Any			):
  __A  :		Tuple       =			[transforms(UpperCamelCase__			) 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:
   __A  :		List[str]       =			ds['train'].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
  # Set the training transforms
  ds["train"].set_transform(UpperCamelCase__			)

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

 # Compute absolute learning rate
 __A  :		Optional[Any]       =			(
     training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
 )
 if training_args.base_learning_rate is not None:
  __A  :		List[Any]       =			training_args.base_learning_rate * total_train_batch_size / 256

 # Initialize our trainer
 __A  :		Union[str, Any]       =			Trainer(
     model=UpperCamelCase__,				args=UpperCamelCase__,				train_dataset=ds['train'] if training_args.do_train else None,				eval_dataset=ds['validation'] if training_args.do_eval else None,				tokenizer=UpperCamelCase__,				data_collator=UpperCamelCase__,				)

 # Training
 if training_args.do_train:
  __A  :		Dict       =			None
  if training_args.resume_from_checkpoint is not None:
   __A  :		Union[str, Any]       =			training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   __A  :		int       =			last_checkpoint
  __A  :		Optional[int]       =			trainer.train(resume_from_checkpoint=UpperCamelCase__			)
  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:
  __A  :		Tuple       =			trainer.evaluate()
  trainer.log_metrics('eval',				UpperCamelCase__			)
  trainer.save_metrics('eval',				UpperCamelCase__			)

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


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
 main()










'''simple docstring'''
from heapq import heappop, heappush

import numpy as np


def 			_lowercase       (				UpperCamelCase__       :					np.ndarray,				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					bool,				):
 __A     ,__A  :		Optional[Any]       =			grid.shape
 __A  :		List[Any]       =			[-1, 1, 0, 0]
 __A  :		Optional[int]       =			[0, 0, -1, 1]
 if allow_diagonal:
  dx += [-1, -1, 1, 1]
  dy += [-1, 1, -1, 1]

 __A     ,__A  :		int       =			[(0, source)], set()
 __A  :		Any       =			np.full((rows, cols),				np.inf			)
 __A  :		int       =			0
 __A  :		Any       =			np.empty((rows, cols),				dtype=UpperCamelCase__			)
 __A  :		List[Any]       =			None

 while queue:
  ((__A)     ,(__A))  :		List[Any]       =			heappop(UpperCamelCase__			)
  if (x, y) in visited:
   continue
  visited.add((x, y)			)

  if (x, y) == destination:
   __A  :		Tuple       =			[]
   while (x, y) != source:
    path.append((x, y)			)
    __A     ,__A  :		int       =			predecessors[x, y]
   path.append(UpperCamelCase__			)  # add the source manually
   path.reverse()
   return matrix[destination], path

  for i in range(len(UpperCamelCase__			)			):
   __A     ,__A  :		List[Any]       =			x + dx[i], y + dy[i]
   if 0 <= nx < rows and 0 <= ny < cols:
    __A  :		str       =			grid[nx][ny]
    if next_node == 1 and matrix[nx, ny] > dist + 1:
     heappush(UpperCamelCase__,				(dist + 1, (nx, ny))			)
     __A  :		int       =			dist + 1
     __A  :		List[str]       =			(x, y)

 return np.inf, []


if __name__ == "__main__":
 import doctest

 doctest.testmod()






