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
_SCREAMING_SNAKE_CASE										=						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')

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



@dataclass
class 				a							:







 """simple docstring"""






 lowerCamelCase	:Optional[str]    			=		field(
     default='''cifar10'''       ,			metadata={'''help''': '''Name of a dataset from the datasets package'''}       )
 lowerCamelCase	:Optional[str]    			=		field(
     default=__lowerCAmelCase       ,			metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}       )
 lowerCamelCase	:Optional[str]    			=		field(
     default=__lowerCAmelCase       ,			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=__lowerCAmelCase       ,			metadata={'''help''': '''A folder containing the training data.'''}       )
 lowerCamelCase	:Optional[str]    			=		field(default=__lowerCAmelCase       ,			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=__lowerCAmelCase       ,			metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     }       ,			)
 lowerCamelCase	:Optional[int]    			=		field(
     default=__lowerCAmelCase       ,			metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
             '''value if set.'''
         )
     }       ,			)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  {}
        if self.train_dir is not None:
               _A        =  self.train_dir
        if self.validation_dir is not None:
               _A        =  self.validation_dir
        _A        =  data_files if data_files else None



@dataclass
class 				a							:







 """simple docstring"""






 lowerCamelCase	:str    			=		field(
     default=__lowerCAmelCase       ,			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=__lowerCAmelCase       ,			metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__lowerCAmelCase       )}       ,			)
 lowerCamelCase	:Optional[str]    			=		field(
     default=__lowerCAmelCase       ,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}       )
 lowerCamelCase	:Optional[str]    			=		field(
     default=__lowerCAmelCase       ,			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=__lowerCAmelCase       ,			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=__lowerCAmelCase       ,			metadata={'''help''': '''Name or path of preprocessor config.'''}       )
 lowerCamelCase	:bool    			=		field(
     default=__lowerCAmelCase       ,			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=__lowerCAmelCase       ,			metadata={
         '''help''': (
             '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
         )
     }       ,			)
 lowerCamelCase	:Optional[int]    			=		field(
     default=__lowerCAmelCase       ,			metadata={
         '''help''': (
             '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
         )
     }       ,			)
 lowerCamelCase	:Optional[int]    			=		field(
     default=__lowerCAmelCase       ,			metadata={'''help''': '''Stride to use for the encoder.'''}       ,			)



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_=1_92		,				lowerCAmelCase_=32		,				lowerCAmelCase_=4		,				lowerCAmelCase_=0.6							)  ->					Optional[int]:
        _A        =  input_size
        _A        =  mask_patch_size
        _A        =  model_patch_size
        _A        =  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"""							)

        _A        =  self.input_size // self.mask_patch_size
        _A        =  self.mask_patch_size // self.model_patch_size

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



 def __call__(					self							)  ->					Dict:
        _A        =  np.random.permutation(self.token_count							)[: self.mask_count]
        _A        =  np.zeros(self.token_count		,				dtype=lowerCAmelCase_							)
        _A        =  1

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

        return torch.tensor(mask.flatten()							)






def  snake_case  (       snake_case__			:Optional[Any])						->   Union[str, Any]:
       _A        =  torch.stack([example["""pixel_values"""] for example in examples])
       _A        =  torch.stack([example["""mask"""] for example in examples])
       return {"pixel_values": pixel_values, "bool_masked_pos": mask}






def  snake_case  (       )						->   Dict:
       # 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        =  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.
              _A   ,  _A   ,  _A        =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
       else:
              _A   ,  _A   ,  _A        =  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"""   ,	snake_case__   ,	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()

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

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

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

       # create model
       if model_args.model_name_or_path:
              _A        =  AutoModelForMaskedImageModeling.from_pretrained(
                  model_args.model_name_or_path   ,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path)   ,	config=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""")
              _A        =  AutoModelForMaskedImageModeling.from_config(snake_case__)

       if training_args.do_train:
              _A        =  ds["""train"""].column_names
       else:
              _A        =  ds["""validation"""].column_names

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

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

       # Initialize our trainer
       _A        =  Trainer(
           model=snake_case__   ,	args=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=snake_case__   ,	data_collator=snake_case__   ,	)

       # Training
       if training_args.do_train:
              _A        =  None
              if training_args.resume_from_checkpoint is not None:
                     _A        =  training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     _A        =  last_checkpoint
              _A        =  trainer.train(resume_from_checkpoint=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:
              _A        =  trainer.evaluate()
              trainer.log_metrics("""eval"""   ,	snake_case__)
              trainer.save_metrics("""eval"""   ,	snake_case__)

       # Write model card and (optionally) push to hub
       _A        =  {
           """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(**snake_case__)
       else:
              trainer.create_model_card(**snake_case__)


if __name__ == "__main__":
    main()





from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)






def  snake_case  (       snake_case__			:Union[str, Any]   ,	snake_case__			:Dict)						->   Any:
       _A        =  []
       for part_id in partition_order:
              _A        =  df.where(F'''SPARK_PARTITION_ID() = {part_id}''').collect()
              for row_idx, row in enumerate(snake_case__):
                     expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict()))
       return expected_row_ids_and_row_dicts






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Optional[Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(100).repartition(1)
       _A        =  Spark(snake_case__)
       # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
       # that each partition can hold 2 rows.
       spark_builder._repartition_df_if_needed(max_shard_size=16)
       # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
       assert spark_builder.df.rdd.getNumPartitions() == 50






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Union[str, Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(10).repartition(2)
       _A        =  [1, 0]
       _A        =  _generate_iterable_examples(snake_case__   ,	snake_case__)  # Reverse the partitions.
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	snake_case__)

       for i, (row_id, row_dict) in enumerate(generate_fn()):
              _A   ,  _A        =  expected_row_ids_and_row_dicts[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   int:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(10).repartition(1)
       _A        =  SparkExamplesIterable(snake_case__)
       assert it.n_shards == 1
       for i, (row_id, row_dict) in enumerate(snake_case__):
              assert row_id == F'''0_{i}'''
              assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Union[str, Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(30).repartition(3)
       # Mock the generator so that shuffle reverses the partition indices.
       with patch("""numpy.random.Generator""") as generator_mock:
              _A        =  lambda snake_case__: x.reverse()
              _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[2, 1, 0])

              _A        =  SparkExamplesIterable(snake_case__).shuffle_data_sources(snake_case__)
              assert shuffled_it.n_shards == 3
              for i, (row_id, row_dict) in enumerate(snake_case__):
                     _A   ,  _A        =  expected_row_ids_and_row_dicts[i]
                     assert row_id == expected_row_id
                     assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   List[str]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(20).repartition(4)

       # Partitions 0 and 2
       _A        =  SparkExamplesIterable(snake_case__).shard_data_sources(worker_id=0   ,	num_workers=2)
       assert shard_it_a.n_shards == 2
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[0, 2])
       for i, (row_id, row_dict) in enumerate(snake_case__):
              _A   ,  _A        =  expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict

       # Partitions 1 and 3
       _A        =  SparkExamplesIterable(snake_case__).shard_data_sources(worker_id=1   ,	num_workers=2)
       assert shard_it_a.n_shards == 2
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[1, 3])
       for i, (row_id, row_dict) in enumerate(snake_case__):
              _A   ,  _A        =  expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Tuple:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(100).repartition(1)
       _A        =  Spark(snake_case__)
       # Choose a small max_shard_size for maximum partitioning.
       spark_builder._repartition_df_if_needed(max_shard_size=1)
       # The new number of partitions should not be greater than the number of rows.
       assert spark_builder.df.rdd.getNumPartitions() == 100




