





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_A   :       List[str]    					=    '''Create a default config file for Accelerate with only a few flags set.'''



def 			_a		(       UpperCAmelCase="no"     ,    UpperCAmelCase = default_json_config_file     ,    UpperCAmelCase = False	)							->							List[str]:







   """simple docstring"""
   lowerCamelCase__  :		Any     =				Path(__a	)
   path.parent.mkdir(parents=__a     ,    exist_ok=__a	)
   if path.exists():
      print(
          f"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."	)
      return False
   lowerCamelCase__  :		Union[str, Any]     =				mixed_precision.lower()
   if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
      raise ValueError(
          f"`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}"	)
   lowerCamelCase__  :		List[str]     =				{
       '''compute_environment''': '''LOCAL_MACHINE''',
       '''mixed_precision''': mixed_precision,
   }
   if torch.cuda.is_available():
      lowerCamelCase__  :		int     =				torch.cuda.device_count()
      lowerCamelCase__  :		List[Any]     =				num_gpus
      lowerCamelCase__  :		List[str]     =				False
      if num_gpus > 1:
         lowerCamelCase__  :		Union[str, Any]     =				'''MULTI_GPU'''
      else:
         lowerCamelCase__  :		str     =				'''NO'''
   elif is_xpu_available() and use_xpu:
      lowerCamelCase__  :		str     =				torch.xpu.device_count()
      lowerCamelCase__  :		str     =				num_xpus
      lowerCamelCase__  :		List[str]     =				False
      if num_xpus > 1:
         lowerCamelCase__  :		Optional[Any]     =				'''MULTI_XPU'''
      else:
         lowerCamelCase__  :		List[Any]     =				'''NO'''
   elif is_npu_available():
      lowerCamelCase__  :		Dict     =				torch.npu.device_count()
      lowerCamelCase__  :		Tuple     =				num_npus
      lowerCamelCase__  :		Tuple     =				False
      if num_npus > 1:
         lowerCamelCase__  :		Dict     =				'''MULTI_NPU'''
      else:
         lowerCamelCase__  :		Any     =				'''NO'''
   else:
      lowerCamelCase__  :		List[Any]     =				0
      lowerCamelCase__  :		Tuple     =				True
      lowerCamelCase__  :		str     =				1
      lowerCamelCase__  :		List[Any]     =				'''NO'''
   lowerCamelCase__  :		Dict     =				ClusterConfig(**__a	)
   config.to_json_file(__a	)
   return path



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase	)							->							List[Any]:







   """simple docstring"""
   lowerCamelCase__  :		List[str]     =				parser.add_parser('''default'''     ,    parents=__a     ,    help=__a     ,    formatter_class=__a	)
   parser.add_argument(
       '''--config_file'''     ,    default=__a     ,    help=(
           '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
           '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
           '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
           '''with \'huggingface\'.'''
       )     ,    dest='''save_location'''     ,    )

   parser.add_argument(
       '''--mixed_precision'''     ,    choices=['''no''', '''fp16''', '''bf16''']     ,    type=__a     ,    help='''Whether or not to use mixed precision training. '''
       '''Choose between FP16 and BF16 (bfloat16) training. '''
       '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''     ,    default='''no'''     ,    )
   parser.set_defaults(func=__a	)
   return parser




def 			_a		(       UpperCAmelCase	)							->							List[str]:







   """simple docstring"""
   lowerCamelCase__  :		str     =				write_basic_config(args.mixed_precision     ,    args.save_location	)
   if config_file:
      print(f"accelerate configuration saved at {config_file}"	)










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       :   Optional[int]					= 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[str]					= list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A       :   Optional[int]					= 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=SCREAMING_SNAKE_CASE  ,	metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}       )
			__lowerCamelCase      :			Optional[str]							     =       field(
			    default=SCREAMING_SNAKE_CASE  ,	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=SCREAMING_SNAKE_CASE  ,	metadata={'''help''': '''A folder containing the training data.'''}       )
			__lowerCamelCase      :			Optional[str]							     =       field(default=SCREAMING_SNAKE_CASE  ,	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=SCREAMING_SNAKE_CASE  ,	metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    }  ,	)
			__lowerCamelCase      :			Optional[int]							     =       field(
			    default=SCREAMING_SNAKE_CASE  ,	metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    }  ,	)






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







					"""simple docstring"""






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


class     A     :
			'''simple docstring'''




			def __init__(     self  :       str				,      __lowerCAmelCase  :       Optional[Any]=1_92				,      __lowerCAmelCase  :       Tuple=32				,      __lowerCAmelCase  :       str=4				,      __lowerCAmelCase  :       Union[str, Any]=0.6  )      ->      Union[str, Any]:







					"""simple docstring"""






					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  :       int  )      ->      Optional[Any]:







					"""simple docstring"""






					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   __lowerCamelCase   (				__a  :Optional[Any]       )						->							Union[str, Any]:






		"""simple docstring"""

		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   __lowerCamelCase   (				)						->							Any:






		"""simple docstring"""

		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"""       ,	__a       ,	__a       )

		# 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(__a       )
		transformers.utils.logging.set_verbosity(__a       )
		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       ,	__a       ) 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       ,	**__a       )
		elif model_args.model_name_or_path:
				A__     =  AutoConfig.from_pretrained(model_args.model_name_or_path       ,	**__a       )
		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(__a       ,	"""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       ,	**__a       )
		elif model_args.model_name_or_path:
				A__     =  AutoImageProcessor.from_pretrained(model_args.model_name_or_path       ,	**__a       )
		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=__a       ,	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(__a       )

		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 __a       : 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(__a  :Any       ):
				A__     =  [transforms(__a       ) 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(__a       )

		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(__a       )

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

		# 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=__a       )
				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"""       ,	__a       )
				trainer.save_metrics("""eval"""       ,	__a       )

		# 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(**__a       )
		else:
				trainer.create_model_card(**__a       )


if __name__ == "__main__":
					main()



