





import collections
import os
import re
from pathlib import Path


__lowerCAmelCase :List[Any]     =    'src/transformers'


# Matches is_xxx_available()
__lowerCAmelCase :Tuple     =    re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__lowerCAmelCase :Tuple     =    re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__lowerCAmelCase :Optional[int]     =    re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
__lowerCAmelCase :str     =    re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
__lowerCAmelCase :str     =    re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__lowerCAmelCase :Union[str, Any]     =    re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
__lowerCAmelCase :List[Any]     =    re.compile(R'^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
__lowerCAmelCase :List[Any]     =    re.compile(R'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
__lowerCAmelCase :Dict     =    re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
__lowerCAmelCase :Union[str, Any]     =    re.compile(R'^\s*try:')
# Catches a line with else:
__lowerCAmelCase :Dict     =    re.compile(R'^\s*else:')







def    A				(							UpperCAmelCase				):
					if _re_test_backend.search(UpperCAmelCase				) is None:
										return None
					_snake_case       :					Any				       =   [b[0] for b in _re_backend.findall(UpperCAmelCase				)]
					backends.sort()
					return "_and_".join(UpperCAmelCase				)







def    A				(							UpperCAmelCase				):
					with open(UpperCAmelCase  ,  "r"  ,  encoding="utf-8"  ,  newline="\n"				) as f:
										_snake_case       :					int				       =   f.readlines()

					_snake_case       :					Any				       =   0
					while line_index < len(UpperCAmelCase				) and not lines[line_index].startswith("_import_structure = {"				):
										line_index += 1

					# If this is a traditional init, just return.
					if line_index >= len(UpperCAmelCase				):
										return None

					# First grab the objects without a specific backend in _import_structure
					_snake_case       :					Dict				       =   []
					while not lines[line_index].startswith("if TYPE_CHECKING"				) and find_backend(lines[line_index]				) is None:
										_snake_case       :					Optional[int]				       =   lines[line_index]
										# If we have everything on a single line, let's deal with it.
										if _re_one_line_import_struct.search(UpperCAmelCase				):
															_snake_case       :					str				       =   _re_one_line_import_struct.search(UpperCAmelCase				).groups()[0]
															_snake_case       :					int				       =   re.findall(R"\[([^\]]+)\]"  ,  UpperCAmelCase				)
															for imp in imports:
																				objects.extend([obj[1:-1] for obj in imp.split(", "				)]				)
															line_index += 1
															continue
										_snake_case       :					Dict				       =   _re_import_struct_key_value.search(UpperCAmelCase				)
										if single_line_import_search is not None:
															_snake_case       :					int				       =   [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", "				) if len(UpperCAmelCase				) > 0]
															objects.extend(UpperCAmelCase				)
										elif line.startswith(" " * 8 + "\""				):
															objects.append(line[9:-3]				)
										line_index += 1

					_snake_case       :					Dict				       =   {"none": objects}
					# Let's continue with backend-specific objects in _import_structure
					while not lines[line_index].startswith("if TYPE_CHECKING"				):
										# If the line is an if not is_backend_available, we grab all objects associated.
										_snake_case       :					List[Any]				       =   find_backend(lines[line_index]				)
										# Check if the backend declaration is inside a try block:
										if _re_try.search(lines[line_index - 1]				) is None:
															_snake_case       :					Optional[int]				       =   None

										if backend is not None:
															line_index += 1

															# Scroll until we hit the else block of try-except-else
															while _re_else.search(lines[line_index]				) is None:
																				line_index += 1

															line_index += 1

															_snake_case       :					List[str]				       =   []
															# Until we unindent, add backend objects to the list
															while len(lines[line_index]				) <= 1 or lines[line_index].startswith(" " * 4				):
																				_snake_case       :					Any				       =   lines[line_index]
																				if _re_import_struct_add_one.search(UpperCAmelCase				) is not None:
																									objects.append(_re_import_struct_add_one.search(UpperCAmelCase				).groups()[0]				)
																				elif _re_import_struct_add_many.search(UpperCAmelCase				) is not None:
																									_snake_case       :					Optional[Any]				       =   _re_import_struct_add_many.search(UpperCAmelCase				).groups()[0].split(", "				)
																									_snake_case       :					Optional[Any]				       =   [obj[1:-1] for obj in imports if len(UpperCAmelCase				) > 0]
																									objects.extend(UpperCAmelCase				)
																				elif _re_between_brackets.search(UpperCAmelCase				) is not None:
																									_snake_case       :					int				       =   _re_between_brackets.search(UpperCAmelCase				).groups()[0].split(", "				)
																									_snake_case       :					Tuple				       =   [obj[1:-1] for obj in imports if len(UpperCAmelCase				) > 0]
																									objects.extend(UpperCAmelCase				)
																				elif _re_quote_object.search(UpperCAmelCase				) is not None:
																									objects.append(_re_quote_object.search(UpperCAmelCase				).groups()[0]				)
																				elif line.startswith(" " * 8 + "\""				):
																									objects.append(line[9:-3]				)
																				elif line.startswith(" " * 12 + "\""				):
																									objects.append(line[13:-3]				)
																				line_index += 1

															_snake_case       :					List[Any]				       =   objects
										else:
															line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
					_snake_case       :					Dict				       =   []
					while (
					    line_index < len(UpperCAmelCase				)
					    and find_backend(lines[line_index]				) is None
					    and not lines[line_index].startswith("else"				)
					):
										_snake_case       :					Tuple				       =   lines[line_index]
										_snake_case       :					List[str]				       =   _re_import.search(UpperCAmelCase				)
										if single_line_import_search is not None:
															objects.extend(single_line_import_search.groups()[0].split(", "				)				)
										elif line.startswith(" " * 8				):
															objects.append(line[8:-2]				)
										line_index += 1

					_snake_case       :					Optional[Any]				       =   {"none": objects}
					# Let's continue with backend-specific objects
					while line_index < len(UpperCAmelCase				):
										# If the line is an if is_backend_available, we grab all objects associated.
										_snake_case       :					str				       =   find_backend(lines[line_index]				)
										# Check if the backend declaration is inside a try block:
										if _re_try.search(lines[line_index - 1]				) is None:
															_snake_case       :					Dict				       =   None

										if backend is not None:
															line_index += 1

															# Scroll until we hit the else block of try-except-else
															while _re_else.search(lines[line_index]				) is None:
																				line_index += 1

															line_index += 1

															_snake_case       :					Dict				       =   []
															# Until we unindent, add backend objects to the list
															while len(lines[line_index]				) <= 1 or lines[line_index].startswith(" " * 8				):
																				_snake_case       :					Optional[int]				       =   lines[line_index]
																				_snake_case       :					int				       =   _re_import.search(UpperCAmelCase				)
																				if single_line_import_search is not None:
																									objects.extend(single_line_import_search.groups()[0].split(", "				)				)
																				elif line.startswith(" " * 12				):
																									objects.append(line[12:-2]				)
																				line_index += 1

															_snake_case       :					List[Any]				       =   objects
										else:
															line_index += 1

					return import_dict_objects, type_hint_objects







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):

					def find_duplicates(UpperCAmelCase				):
										return [k for k, v in collections.Counter(UpperCAmelCase				).items() if v > 1]

					if list(import_dict_objects.keys()				) != list(type_hint_objects.keys()				):
										return ["Both sides of the init do not have the same backends!"]

					_snake_case       :					Dict				       =   []
					for key in import_dict_objects.keys():
										_snake_case       :					Optional[Any]				       =   find_duplicates(import_dict_objects[key]				)
										if duplicate_imports:
															errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}"""				)
										_snake_case       :					Union[str, Any]				       =   find_duplicates(type_hint_objects[key]				)
										if duplicate_type_hints:
															errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"""				)

										if sorted(set(import_dict_objects[key]				)				) != sorted(set(type_hint_objects[key]				)				):
															_snake_case       :					str				       =   "base imports" if key == "none" else F"""{key} backend"""
															errors.append(F"""Differences for {name}:"""				)
															for a in type_hint_objects[key]:
																				if a not in import_dict_objects[key]:
																									errors.append(F"""  {a} in TYPE_HINT but not in _import_structure."""				)
															for a in import_dict_objects[key]:
																				if a not in type_hint_objects[key]:
																									errors.append(F"""  {a} in _import_structure but not in TYPE_HINT."""				)
					return errors







def    A				(							):
					_snake_case       :					str				       =   []
					for root, _, files in os.walk(UpperCAmelCase				):
										if "__init__.py" in files:
															_snake_case       :					Union[str, Any]				       =   os.path.join(UpperCAmelCase  ,  "__init__.py"				)
															_snake_case       :					Dict				       =   parse_init(UpperCAmelCase				)
															if objects is not None:
																				_snake_case       :					Tuple				       =   analyze_results(*UpperCAmelCase				)
																				if len(UpperCAmelCase				) > 0:
																									_snake_case       :					Union[str, Any]				       =   F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
																									failures.append("\n".join(UpperCAmelCase				)				)
					if len(UpperCAmelCase				) > 0:
										raise ValueError("\n\n".join(UpperCAmelCase				)				)







def    A				(							):
					_snake_case       :					Optional[Any]				       =   []
					for path, directories, files in os.walk(UpperCAmelCase				):
										for folder in directories:
															# Ignore private modules
															if folder.startswith("_"				):
																				directories.remove(UpperCAmelCase				)
																				continue
															# Ignore leftovers from branches (empty folders apart from pycache)
															if len(list((Path(UpperCAmelCase				) / folder).glob("*.py"				)				)				) == 0:
																				continue
															_snake_case       :					int				       =   str((Path(UpperCAmelCase				) / folder).relative_to(UpperCAmelCase				)				)
															_snake_case       :					Optional[Any]				       =   short_path.replace(os.path.sep  ,  "."				)
															submodules.append(UpperCAmelCase				)
										for fname in files:
															if fname == "__init__.py":
																				continue
															_snake_case       :					Optional[Any]				       =   str((Path(UpperCAmelCase				) / fname).relative_to(UpperCAmelCase				)				)
															_snake_case       :					int				       =   short_path.replace(".py"  ,  ""				).replace(os.path.sep  ,  "."				)
															if len(submodule.split("."				)				) == 1:
																				submodules.append(UpperCAmelCase				)
					return submodules


__lowerCAmelCase :Tuple     =    [
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
    'models.esm.openfold_utils',
]







def    A				(							):
					# This is to make sure the transformers module imported is the one in the repo.
					from transformers.utils import direct_transformers_import

					_snake_case       :					int				       =   direct_transformers_import(UpperCAmelCase				)

					_snake_case       :					str				       =   set(transformers._import_structure.keys()				)
					# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
					# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
					# (potentiall re-) add them.
					with open(os.path.join(UpperCAmelCase  ,  "__init__.py"				)  ,  "r"				) as f:
										_snake_case       :					Union[str, Any]				       =   f.read()
					import_structure_keys.update(set(re.findall(R"import_structure\[\"([^\"]*)\"\]"  ,  UpperCAmelCase				)				)				)

					_snake_case       :					Dict				       =   [
					    module
					    for module in get_transformers_submodules()
					    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
					]

					if len(UpperCAmelCase				) > 0:
										_snake_case       :					List[Any]				       =   "\n".join(F"""- {module}""" for module in module_not_registered				)
										raise ValueError(
										    "The following submodules are not properly registed in the main init of Transformers:\n"
										    F"""{list_of_modules}\n"""
										    "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."				)


if __name__ == "__main__":
							check_all_inits()
							check_submodules()






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 :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')

__lowerCAmelCase :Union[str, Any]     =    list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__lowerCAmelCase :Optional[Any]     =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class        _a:
		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        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

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






@dataclass
class        _a:
		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        _a:







		def __init__(    self ,						__snake_case=1_9_2 ,						__snake_case=3_2 ,						__snake_case=4 ,						__snake_case=0.6				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   input_size
							_snake_case       :					Optional[int]				       =   mask_patch_size
							_snake_case       :					Optional[Any]				       =   model_patch_size
							_snake_case       :					Any				       =   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"				)

							_snake_case       :					Optional[int]				       =   self.input_size // self.mask_patch_size
							_snake_case       :					Optional[int]				       =   self.mask_patch_size // self.model_patch_size

							_snake_case       :					List[str]				       =   self.rand_size**2
							_snake_case       :					Any				       =   int(np.ceil(self.token_count * self.mask_ratio				)				)







		def __call__(    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   np.random.permutation(self.token_count				)[: self.mask_count]
							_snake_case       :					Dict				       =   np.zeros(self.token_count ,						dtype=__snake_case				)
							_snake_case       :					Any				       =   1

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

							return torch.tensor(mask.flatten()				)







def    A				(							UpperCAmelCase				):
					_snake_case       :					List[Any]				       =   torch.stack([example["pixel_values"] for example in examples]				)
					_snake_case       :					Any				       =   torch.stack([example["mask"] for example in examples]				)
					return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def    A				(							):
					# 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.

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

					_snake_case       :					Any				       =   training_args.get_process_log_level()
					logger.setLevel(UpperCAmelCase				)
					transformers.utils.logging.set_verbosity(UpperCAmelCase				)
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

					# Log on each process the small summary:
					logger.warning(
					    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
					    + F"""distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}"""				)
					logger.info(F"""Training/evaluation parameters {training_args}"""				)

					# Detecting last checkpoint.
					_snake_case       :					Tuple				       =   None
					if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
										_snake_case       :					Optional[int]				       =   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.
					_snake_case       :					int				       =   load_dataset(
					    data_args.dataset_name  ,  data_args.dataset_config_name  ,  data_files=data_args.data_files  ,  cache_dir=model_args.cache_dir  ,  use_auth_token=True if model_args.use_auth_token else None  ,  )

					# If we don't have a validation split, split off a percentage of train as validation.
					_snake_case       :					str				       =   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:
										_snake_case       :					Tuple				       =   ds["train"].train_test_split(data_args.train_val_split				)
										_snake_case       :					List[str]				       =   split["train"]
										_snake_case       :					Optional[Any]				       =   split["test"]

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

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

					# create model
					if model_args.model_name_or_path:
										_snake_case       :					Union[str, Any]				       =   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"				)
										_snake_case       :					Any				       =   AutoModelForMaskedImageModeling.from_config(UpperCAmelCase				)

					if training_args.do_train:
										_snake_case       :					Optional[int]				       =   ds["train"].column_names
					else:
										_snake_case       :					List[str]				       =   ds["validation"].column_names

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

					# transformations as done in original SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					_snake_case       :					str				       =   Compose(
					    [
					        Lambda(lambda UpperCAmelCase				: img.convert("RGB"				) if img.mode != "RGB" else img				),
					        RandomResizedCrop(model_args.image_size  ,  scale=(0.6_7, 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
					_snake_case       :					List[str]				       =   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				):
										_snake_case       :					str				       =   [transforms(UpperCAmelCase				) for image in examples[image_column_name]]
										_snake_case       :					List[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:
															_snake_case       :					Dict				       =   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:
															_snake_case       :					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(UpperCAmelCase				)

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

					# Write model card and (optionally) push to hub
					_snake_case       :					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()