


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


@dataclass
class 		_snake_case					:
					lowerCAmelCase      :Optional[str]   =					field(
					    default='''cifar10'''       ,   metadata={'''help''': '''Name of a dataset from the datasets package'''}	)
					lowerCAmelCase      :Optional[str]   =					field(
					    default=a__       ,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}	)
					lowerCAmelCase      :Optional[str]   =					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      :Optional[str]   =					field(default=a__       ,   metadata={'''help''': '''A folder containing the training data.'''}	)
					lowerCAmelCase      :Optional[str]   =					field(default=a__       ,   metadata={'''help''': '''A folder containing the validation data.'''}	)
					lowerCAmelCase      :Optional[float]   =					field(
					    default=0.15       ,   metadata={'''help''': '''Percent to split off of train for validation.'''}	)
					lowerCAmelCase      :int   =					field(default=32       ,   metadata={'''help''': '''The size of the square patches to use for masking.'''}	)
					lowerCAmelCase      :float   =					field(
					    default=0.6       ,   metadata={'''help''': '''Percentage of patches to mask.'''}       ,   )
					lowerCAmelCase      :Optional[int]   =					field(
					    default=a__       ,   metadata={
					        '''help''': (
					            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
					            '''value if set.'''
					        )
					    }       ,   )
					lowerCAmelCase      :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):
								UpperCAmelCase__  :					List[Any]        =     {}
								if self.train_dir is not None:
											UpperCAmelCase__  :					List[Any]        =     self.train_dir
								if self.validation_dir is not None:
											UpperCAmelCase__  :					Tuple        =     self.validation_dir
								UpperCAmelCase__  :					Optional[Any]        =     data_files if data_files else None


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


class 		_snake_case					:




					def __init__(      self							,							_lowerCamelCase=192							,							_lowerCamelCase=32							,							_lowerCamelCase=4							,							_lowerCamelCase=0.6):
								UpperCAmelCase__  :					Tuple        =     input_size
								UpperCAmelCase__  :					Any        =     mask_patch_size
								UpperCAmelCase__  :					Optional[int]        =     model_patch_size
								UpperCAmelCase__  :					Tuple        =     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""")

								UpperCAmelCase__  :					Tuple        =     self.input_size // self.mask_patch_size
								UpperCAmelCase__  :					List[str]        =     self.mask_patch_size // self.model_patch_size

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




					def __call__(      self):
								UpperCAmelCase__  :					Any        =     np.random.permutation(self.token_count)[: self.mask_count]
								UpperCAmelCase__  :					Dict        =     np.zeros(self.token_count							,							dtype=_lowerCamelCase)
								UpperCAmelCase__  :					Dict        =     1

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

								return torch.tensor(mask.flatten())







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[Any]        =     torch.stack([example["""pixel_values"""] for example in examples]					)
			UpperCAmelCase__  :					Tuple        =     torch.stack([example["""mask"""] for example in examples]					)
			return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def     _UpperCamelCase      (		):
			# 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.

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

			UpperCAmelCase__  :					Dict        =     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.
			UpperCAmelCase__  :					Tuple        =     None
			if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
						UpperCAmelCase__  :					List[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.
			UpperCAmelCase__  :					Dict        =     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.
			UpperCAmelCase__  :					Optional[int]        =     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:
						UpperCAmelCase__  :					Dict        =     ds["""train"""].train_test_split(data_args.train_val_split					)
						UpperCAmelCase__  :					List[Any]        =     split["""train"""]
						UpperCAmelCase__  :					int        =     split["""test"""]

			# Create config
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			UpperCAmelCase__  :					int        =     {
			    """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:
						UpperCAmelCase__  :					Union[str, Any]        =     AutoConfig.from_pretrained(model_args.config_name_or_path ,      **UpperCamelCase__					)
			elif model_args.model_name_or_path:
						UpperCAmelCase__  :					str        =     AutoConfig.from_pretrained(model_args.model_name_or_path ,      **UpperCamelCase__					)
			else:
						UpperCAmelCase__  :					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(UpperCamelCase__ ,      """decoder_type"""					):
						UpperCAmelCase__  :					str        =     """simmim"""

			# adapt config
			UpperCAmelCase__  :					Optional[Any]        =     model_args.image_size if model_args.image_size is not None else config.image_size
			UpperCAmelCase__  :					int        =     model_args.patch_size if model_args.patch_size is not None else config.patch_size
			UpperCAmelCase__  :					Dict        =     (
			    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:
						UpperCAmelCase__  :					List[Any]        =     AutoImageProcessor.from_pretrained(model_args.image_processor_name ,      **UpperCamelCase__					)
			elif model_args.model_name_or_path:
						UpperCAmelCase__  :					Optional[Any]        =     AutoImageProcessor.from_pretrained(model_args.model_name_or_path ,      **UpperCamelCase__					)
			else:
						UpperCAmelCase__  :					Dict        =     {
						    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
						}
						UpperCAmelCase__  :					Union[str, Any]        =     IMAGE_PROCESSOR_TYPES[model_args.model_type]()

			# create model
			if model_args.model_name_or_path:
						UpperCAmelCase__  :					Tuple        =     AutoModelForMaskedImageModeling.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"""					)
						UpperCAmelCase__  :					Tuple        =     AutoModelForMaskedImageModeling.from_config(UpperCamelCase__					)

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

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

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

			# Initialize our trainer
			UpperCAmelCase__  :					str        =     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:
						UpperCAmelCase__  :					str        =     None
						if training_args.resume_from_checkpoint is not None:
									UpperCAmelCase__  :					List[Any]        =     training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									UpperCAmelCase__  :					Dict        =     last_checkpoint
						UpperCAmelCase__  :					str        =     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:
						UpperCAmelCase__  :					int        =     trainer.evaluate()
						trainer.log_metrics("""eval""" ,      UpperCamelCase__					)
						trainer.save_metrics("""eval""" ,      UpperCamelCase__					)

			# Write model card and (optionally) push to hub
			UpperCAmelCase__  :					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(**UpperCamelCase__					)
			else:
						trainer.create_model_card(**UpperCamelCase__					)


if __name__ == "__main__":
		main()



'''simple docstring'''



from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def     _UpperCamelCase      (		UpperCamelCase__					):

			# A local function to see if a dot lands in the circle.
			def is_in_circle(UpperCamelCase__ ,      UpperCamelCase__					) -> bool:
						UpperCAmelCase__  :					List[str]        =     sqrt((x**2) + (y**2)					)
						# Our circle has a radius of 1, so a distance
						# greater than 1 would land outside the circle.
						return distance_from_centre <= 1

			# The proportion of guesses that landed in the circle
			UpperCAmelCase__  :					Optional[Any]        =     mean(
			    int(is_in_circle(uniform(-1.0 ,      1.0					) ,      uniform(-1.0 ,      1.0					)					)					)
			    for _ in range(UpperCamelCase__					)					)
			# The ratio of the area for circle to square is pi/4.
			UpperCAmelCase__  :					int        =     proportion * 4
			print(f'''The estimated value of pi is {pi_estimate}'''					)
			print(f'''The numpy value of pi is {pi}'''					)
			print(f'''The total error is {abs(pi - pi_estimate					)}'''					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ = 0.0 ,      UpperCamelCase__ = 1.0 ,      ):
			return mean(
			    function_to_integrate(uniform(UpperCamelCase__ ,      UpperCamelCase__					)					) for _ in range(UpperCamelCase__					)					) * (max_value - min_value)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ = 0.0 ,      UpperCamelCase__ = 1.0					):

			def identity_function(UpperCamelCase__					) -> float:
						return x

			UpperCAmelCase__  :					List[str]        =     area_under_curve_estimator(
			    UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
			UpperCAmelCase__  :					Optional[int]        =     (max_value * max_value - min_value * min_value) / 2

			print("""******************"""					)
			print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}'''					)
			print(f'''Estimated value is {estimated_value}'''					)
			print(f'''Expected value is {expected_value}'''					)
			print(f'''Total error is {abs(estimated_value - expected_value					)}'''					)
			print("""******************"""					)







def     _UpperCamelCase      (		UpperCamelCase__					):

			def function_to_integrate(UpperCamelCase__					) -> float:
						return sqrt(4.0 - x * x					)

			UpperCAmelCase__  :					Dict        =     area_under_curve_estimator(
			    UpperCamelCase__ ,      UpperCamelCase__ ,      0.0 ,      2.0					)

			print("""******************"""					)
			print("""Estimating pi using area_under_curve_estimator"""					)
			print(f'''Estimated value is {estimated_value}'''					)
			print(f'''Expected value is {pi}'''					)
			print(f'''Total error is {abs(estimated_value - pi					)}'''					)
			print("""******************"""					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()