
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
lowerCamelCase      =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")

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




@dataclass
class 				_lowerCamelCase :





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  field(
         default='''cifar10'''	,							metadata={'''help''': '''Name of a dataset from the datasets package'''} )
     SCREAMING_SNAKE_CASE_		   =  field(
         default=_UpperCAmelCase	,							metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
     SCREAMING_SNAKE_CASE_		   =  field(
         default=_UpperCAmelCase	,							metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}	,							)
     SCREAMING_SNAKE_CASE_		   =  field(default=_UpperCAmelCase	,							metadata={'''help''': '''A folder containing the training data.'''} )
     SCREAMING_SNAKE_CASE_		   =  field(default=_UpperCAmelCase	,							metadata={'''help''': '''A folder containing the validation data.'''} )
     SCREAMING_SNAKE_CASE_		   =  field(
         default=0.15	,							metadata={'''help''': '''Percent to split off of train for validation.'''} )
     SCREAMING_SNAKE_CASE_		   =  field(default=32	,							metadata={'''help''': '''The size of the square patches to use for masking.'''} )
     SCREAMING_SNAKE_CASE_		   =  field(
         default=0.6	,							metadata={'''help''': '''Percentage of patches to mask.'''}	,							)
     SCREAMING_SNAKE_CASE_		   =  field(
         default=_UpperCAmelCase	,							metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                 '''value if set.'''
             )
         }	,							)
     SCREAMING_SNAKE_CASE_		   =  field(
         default=_UpperCAmelCase	,							metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                 '''value if set.'''
             )
         }	,							)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







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




@dataclass
class 				_lowerCamelCase :





     """simple docstring"""







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




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE=1_9_2						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=4						,						__SCREAMING_SNAKE_CASE=0.6  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				input_size
           UpperCamelCase__    :   List[Any]					=				mask_patch_size
           UpperCamelCase__    :   Tuple					=				model_patch_size
           UpperCamelCase__    :   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'''  )

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

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






     def __call__(							self  )  ->   Dict:




           """simple docstring"""







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

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

           return torch.tensor(mask.flatten()  )


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


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   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.
            UpperCamelCase__    :   Tuple					=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
      else:
            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''' ,		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()

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

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

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

      # create model
      if model_args.model_name_or_path:
            UpperCamelCase__    :   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'''		)
            UpperCamelCase__    :   Optional[int]					=				AutoModelForMaskedImageModeling.from_config(SCREAMING_SNAKE_CASE__		)

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

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

      # transformations as done in original SimMIM paper
      # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
      UpperCamelCase__    :   List[str]					=				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__    :   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__    :   List[str]					=				[transforms(SCREAMING_SNAKE_CASE__		) for image in examples[image_column_name]]
            UpperCamelCase__    :   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:
                  UpperCamelCase__    :   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:
                  UpperCamelCase__    :   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
      UpperCamelCase__    :   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:
            UpperCamelCase__    :   Any					=				None
            if training_args.resume_from_checkpoint is not None:
                  UpperCamelCase__    :   str					=				training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  UpperCamelCase__    :   int					=				last_checkpoint
            UpperCamelCase__    :   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:
            UpperCamelCase__    :   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
      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(**SCREAMING_SNAKE_CASE__		)
      else:
            trainer.create_model_card(**SCREAMING_SNAKE_CASE__		)


if __name__ == "__main__":
    main()




import torch

from transformers import AutoModel




class 				_lowerCamelCase (      torch.nn.Module ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"  )  ->   Optional[Any]:




           """simple docstring"""







           super(__SCREAMING_SNAKE_CASE						,						self  ).__init__()

           UpperCamelCase__    :   Tuple					=				AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE						,						return_dict=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.CosineSimilarity(3						,						1e-08  )
           UpperCamelCase__    :   List[Any]					=				torch.nn.Softmax(dim=1  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						**__SCREAMING_SNAKE_CASE  )  ->   Union[str, Any]:




           """simple docstring"""







           return self.bert(**__SCREAMING_SNAKE_CASE  ).last_hidden_state






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           return token_embeddings.sum(2						,						keepdim=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1  )  ->   str:




           """simple docstring"""







           return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				W_supports['''sizes'''].tolist()
           UpperCamelCase__    :   Tuple					=				W_supports['''start_token_id'''].item()
           UpperCamelCase__    :   int					=				W_supports['''end_token_id'''].item()

           del W_supports["sizes"]
           del W_supports["start_token_id"]
           del W_supports["end_token_id"]

           UpperCamelCase__    :   Optional[int]					=				self.BERT(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				self.BERT(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   Dict					=				None

           UpperCamelCase__    :   Any					=				W_supports['''input_ids'''] == start_token_id
           UpperCamelCase__    :   Optional[int]					=				W_supports['''input_ids'''] == end_token_id

           for i, size in enumerate(__SCREAMING_SNAKE_CASE  ):
                 if i == 0:
                       UpperCamelCase__    :   int					=				0
                 else:
                       UpperCamelCase__    :   Optional[int]					=				support_sizes[i - 1]

                 UpperCamelCase__    :   List[Any]					=				S[s : s + size][start_token_masks[s : s + size]]
                 UpperCamelCase__    :   Optional[Any]					=				S[s : s + size][end_token_masks[s : s + size]]

                 UpperCamelCase__    :   Tuple					=				torch.matmul(q[i]						,						s_start.T  ).sum(1  ).softmax(0  )
                 UpperCamelCase__    :   Optional[Any]					=				torch.matmul(q[i]						,						s_end.T  ).sum(1  ).softmax(0  )

                 if p_starts is not None:
                       UpperCamelCase__    :   Union[str, Any]					=				torch.vstack((p_starts, p_start)  )
                       UpperCamelCase__    :   str					=				torch.vstack((p_ends, p_end)  )
                 else:
                       UpperCamelCase__    :   List[Any]					=				p_start
                       UpperCamelCase__    :   List[str]					=				p_end

           return p_starts, p_ends


