





import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu

class      lowerCAmelCase							(      nn.Module    ):
 def __init__(   self						:   Optional[int]			)    ->			Any:
     super().__init__()
     lowerCamelCase__					:   Any      				=	nn.Linear(3    ,  4			)
     lowerCamelCase__					:   List[Any]      				=	nn.BatchNormad(4			)
     lowerCamelCase__					:   Any      				=	nn.Linear(4    ,  5			)







 def   A_			(   self						:   List[Any]    ,  UpperCAmelCase						:   Dict			)    ->			Any:
     return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase			)			)			)


class      lowerCAmelCase							(      lowercase__    ):
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   Union[str, Any]    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   List[Any]			)    ->			Any:
     return (args[0] + 1,) + args[1:], kwargs


class      lowerCAmelCase							(      lowercase__    ):
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Tuple			)    ->			str:
     return output + 1


class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Any			)    ->			str:
     lowerCamelCase__					:   List[str]      				=	ModelForTest()
     lowerCamelCase__					:   Dict      				=	ModelHook()

     add_hook_to_module(UpperCAmelCase    ,  UpperCAmelCase			)
     self.assertEqual(test_model._hf_hook    ,  UpperCAmelCase			)
     self.assertTrue(hasattr(UpperCAmelCase    ,  '_old_forward'			)			)

     # Check adding the hook did not change the name or the signature
     self.assertEqual(test_model.forward.__name__    ,  'forward'			)
     self.assertListEqual(list(inspect.signature(test_model.forward			).parameters			)    ,  ['x']			)

     remove_hook_from_module(UpperCAmelCase			)
     self.assertFalse(hasattr(UpperCAmelCase    ,  '_hf_hook'			)			)
     self.assertFalse(hasattr(UpperCAmelCase    ,  '_old_forward'			)			)
 def   A_			(   self						:   str			)    ->			List[Any]:
     lowerCamelCase__					:   List[Any]      				=	ModelForTest()
     lowerCamelCase__					:   Optional[Any]      				=	ModelHook()

     add_hook_to_module(UpperCAmelCase    ,  UpperCAmelCase			)
     add_hook_to_module(UpperCAmelCase    ,  UpperCAmelCase    ,  append=UpperCAmelCase			)

     self.assertEqual(isinstance(test_model._hf_hook    ,  UpperCAmelCase			)    ,  UpperCAmelCase			)
     self.assertEqual(len(test_model._hf_hook.hooks			)    ,  2			)
     self.assertTrue(hasattr(UpperCAmelCase    ,  '_old_forward'			)			)

     # Check adding the hook did not change the name or the signature
     self.assertEqual(test_model.forward.__name__    ,  'forward'			)
     self.assertListEqual(list(inspect.signature(test_model.forward			).parameters			)    ,  ['x']			)

     remove_hook_from_module(UpperCAmelCase			)
     self.assertFalse(hasattr(UpperCAmelCase    ,  '_hf_hook'			)			)
     self.assertFalse(hasattr(UpperCAmelCase    ,  '_old_forward'			)			)
 def   A_			(   self						:   List[str]			)    ->			List[Any]:
     lowerCamelCase__					:   Any      				=	ModelForTest()
     lowerCamelCase__					:   Any      				=	torch.randn(2    ,  3			)
     lowerCamelCase__					:   Any      				=	test_model(x + 1			)
     lowerCamelCase__					:   int      				=	test_model(x + 2			)

     lowerCamelCase__					:   List[str]      				=	PreForwardHook()
     add_hook_to_module(UpperCAmelCase    ,  UpperCAmelCase			)
     lowerCamelCase__					:   Optional[int]      				=	test_model(UpperCAmelCase			)
     self.assertTrue(torch.allclose(UpperCAmelCase    ,  UpperCAmelCase    ,  atol=1e-5			)			)

     # Attaching a hook to a model when it already has one replaces, does not chain
     lowerCamelCase__					:   Dict      				=	PreForwardHook()
     add_hook_to_module(UpperCAmelCase    ,  UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	test_model(UpperCAmelCase			)
     self.assertTrue(torch.allclose(UpperCAmelCase    ,  UpperCAmelCase    ,  atol=1e-5			)			)

     # You need to use the sequential hook to chain two or more hooks
     lowerCamelCase__					:   Optional[int]      				=	SequentialHook(PreForwardHook()    ,  PreForwardHook()			)
     add_hook_to_module(UpperCAmelCase    ,  UpperCAmelCase			)

     lowerCamelCase__					:   str      				=	test_model(UpperCAmelCase			)
     assert torch.allclose(UpperCAmelCase    ,  UpperCAmelCase    ,  atol=1e-5			)
 def   A_			(   self						:   Optional[Any]			)    ->			Tuple:
     lowerCamelCase__					:   int      				=	ModelForTest()
     lowerCamelCase__					:   Tuple      				=	torch.randn(2    ,  3			)
     lowerCamelCase__					:   Tuple      				=	test_model(UpperCAmelCase			)

     lowerCamelCase__					:   str      				=	PostForwardHook()
     add_hook_to_module(UpperCAmelCase    ,  UpperCAmelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	test_model(UpperCAmelCase			)
     self.assertTrue(torch.allclose(UpperCAmelCase    ,  output + 1    ,  atol=1e-5			)			)

     # Attaching a hook to a model when it already has one replaces, does not chain
     lowerCamelCase__					:   Tuple      				=	PostForwardHook()
     add_hook_to_module(UpperCAmelCase    ,  UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	test_model(UpperCAmelCase			)
     self.assertTrue(torch.allclose(UpperCAmelCase    ,  output + 1    ,  atol=1e-5			)			)

     # You need to use the sequential hook to chain two or more hooks
     lowerCamelCase__					:   List[str]      				=	SequentialHook(PostForwardHook()    ,  PostForwardHook()			)
     add_hook_to_module(UpperCAmelCase    ,  UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	test_model(UpperCAmelCase			)
     assert torch.allclose(UpperCAmelCase    ,  output + 2    ,  atol=1e-5			)
 def   A_			(   self						:   int			)    ->			Optional[int]:
     lowerCamelCase__					:   str      				=	ModelForTest()
     lowerCamelCase__					:   Tuple      				=	torch.randn(2    ,  3			)
     lowerCamelCase__					:   int      				=	test_model(UpperCAmelCase			)

     lowerCamelCase__					:   Dict      				=	PostForwardHook()
     add_hook_to_module(UpperCAmelCase    ,  UpperCAmelCase			)
     lowerCamelCase__					:   int      				=	test_model(UpperCAmelCase			)
     self.assertTrue(torch.allclose(UpperCAmelCase    ,  output + 1			)			)
     self.assertTrue(outputa.requires_grad			)

     lowerCamelCase__					:   Optional[Any]      				=	True
     lowerCamelCase__					:   Optional[Any]      				=	test_model(UpperCAmelCase			)
     self.assertFalse(outputa.requires_grad			)
 @require_multi_gpu
 def   A_			(   self						:   int			)    ->			Dict:
     lowerCamelCase__					:   Any      				=	ModelForTest()
     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)

     # This will move each submodule on different devices
     add_hook_to_module(model.lineara    ,  AlignDevicesHook(execution_device=0			)			)
     add_hook_to_module(model.batchnorm    ,  AlignDevicesHook(execution_device=0			)			)
     add_hook_to_module(model.lineara    ,  AlignDevicesHook(execution_device=1			)			)

     self.assertEqual(model.lineara.weight.device    ,  torch.device(0			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device(0			)			)
     self.assertEqual(model.batchnorm.running_mean.device    ,  torch.device(0			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device(1			)			)

     # We can still make a forward pass. The input does not need to be on any particular device
     lowerCamelCase__					:   Any      				=	torch.randn(2    ,  3			)
     lowerCamelCase__					:   Union[str, Any]      				=	model(UpperCAmelCase			)
     self.assertEqual(output.device    ,  torch.device(1			)			)

     # We can add a general hook to put back output on same device as input.
     add_hook_to_module(UpperCAmelCase    ,  AlignDevicesHook(io_same_device=UpperCAmelCase			)			)
     lowerCamelCase__					:   List[str]      				=	torch.randn(2    ,  3			).to(0			)
     lowerCamelCase__					:   Tuple      				=	model(UpperCAmelCase			)
     self.assertEqual(output.device    ,  torch.device(0			)			)
 def   A_			(   self						:   Tuple			)    ->			List[Any]:
     lowerCamelCase__					:   Tuple      				=	ModelForTest()

     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)

     # This will move each submodule on different devices
     lowerCamelCase__					:   int      				=	{"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}

     add_hook_to_module(model.lineara    ,  AlignDevicesHook(**UpperCAmelCase			)			)
     add_hook_to_module(model.batchnorm    ,  AlignDevicesHook(**UpperCAmelCase			)			)
     add_hook_to_module(model.lineara    ,  AlignDevicesHook(**UpperCAmelCase			)			)

     # Parameters have been offloaded, so on the meta device
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     # Buffers are not included in the offload by default, so are on the execution device
     lowerCamelCase__					:   str      				=	torch.device(hook_kwargs['execution_device']			)
     self.assertEqual(model.batchnorm.running_mean.device    ,  UpperCAmelCase			)

     lowerCamelCase__					:   int      				=	torch.randn(2    ,  3			)
     lowerCamelCase__					:   str      				=	model(UpperCAmelCase			)
     self.assertEqual(output.device    ,  UpperCAmelCase			)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_module(model.lineara			)
     remove_hook_from_module(model.batchnorm			)
     remove_hook_from_module(model.lineara			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)

     # Now test with buffers included in the offload
     lowerCamelCase__					:   Tuple      				=	{
         "execution_device": 0 if torch.cuda.is_available() else "cpu",
         "offload": True,
         "offload_buffers": True,
     }

     add_hook_to_module(model.lineara    ,  AlignDevicesHook(**UpperCAmelCase			)			)
     add_hook_to_module(model.batchnorm    ,  AlignDevicesHook(**UpperCAmelCase			)			)
     add_hook_to_module(model.lineara    ,  AlignDevicesHook(**UpperCAmelCase			)			)

     # Parameters have been offloaded, so on the meta device, buffers included
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.batchnorm.running_mean.device    ,  torch.device('meta'			)			)

     lowerCamelCase__					:   int      				=	torch.randn(2    ,  3			)
     lowerCamelCase__					:   Tuple      				=	model(UpperCAmelCase			)
     self.assertEqual(output.device    ,  UpperCAmelCase			)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_module(model.lineara			)
     remove_hook_from_module(model.batchnorm			)
     remove_hook_from_module(model.lineara			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)
 def   A_			(   self						:   Dict			)    ->			Union[str, Any]:
     lowerCamelCase__					:   int      				=	ModelForTest()

     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)

     # This will move each submodule on different devices
     lowerCamelCase__					:   Any      				=	0 if torch.cuda.is_available() else "cpu"
     attach_align_device_hook(UpperCAmelCase    ,  execution_device=UpperCAmelCase    ,  offload=UpperCAmelCase			)

     # Parameters have been offloaded, so on the meta device
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     # Buffers are not included in the offload by default, so are on the execution device
     lowerCamelCase__					:   Optional[int]      				=	torch.device(UpperCAmelCase			)
     self.assertEqual(model.batchnorm.running_mean.device    ,  UpperCAmelCase			)

     lowerCamelCase__					:   Any      				=	torch.randn(2    ,  3			)
     lowerCamelCase__					:   Dict      				=	model(UpperCAmelCase			)
     self.assertEqual(output.device    ,  UpperCAmelCase			)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(UpperCAmelCase			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)

     # Now test with buffers included in the offload
     attach_align_device_hook(UpperCAmelCase    ,  execution_device=UpperCAmelCase    ,  offload=UpperCAmelCase    ,  offload_buffers=UpperCAmelCase			)

     # Parameters have been offloaded, so on the meta device, buffers included
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.batchnorm.running_mean.device    ,  torch.device('meta'			)			)

     lowerCamelCase__					:   Tuple      				=	torch.randn(2    ,  3			)
     lowerCamelCase__					:   Any      				=	model(UpperCAmelCase			)
     self.assertEqual(output.device    ,  UpperCAmelCase			)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(UpperCAmelCase			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)







 def   A_			(   self						:   Tuple			)    ->			List[str]:
     lowerCamelCase__					:   Tuple      				=	ModelForTest()

     # Everything is on CPU
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)

     # This will move each submodule on different devices
     lowerCamelCase__					:   Union[str, Any]      				=	0 if torch.cuda.is_available() else "cpu"
     attach_align_device_hook(
         UpperCAmelCase    ,  execution_device=UpperCAmelCase    ,  offload=UpperCAmelCase    ,  weights_map=model.state_dict()			)

     # Parameters have been offloaded, so on the meta device
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     # Buffers are not included in the offload by default, so are on the execution device
     lowerCamelCase__					:   str      				=	torch.device(UpperCAmelCase			)
     self.assertEqual(model.batchnorm.running_mean.device    ,  UpperCAmelCase			)

     lowerCamelCase__					:   Any      				=	torch.randn(2    ,  3			)
     lowerCamelCase__					:   str      				=	model(UpperCAmelCase			)
     self.assertEqual(output.device    ,  UpperCAmelCase			)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(UpperCAmelCase			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)

     # Now test with buffers included in the offload
     attach_align_device_hook(
         UpperCAmelCase    ,  execution_device=UpperCAmelCase    ,  offload=UpperCAmelCase    ,  weights_map=model.state_dict()    ,  offload_buffers=UpperCAmelCase    ,  )

     # Parameters have been offloaded, so on the meta device, buffers included
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('meta'			)			)
     self.assertEqual(model.batchnorm.running_mean.device    ,  torch.device('meta'			)			)

     lowerCamelCase__					:   Optional[int]      				=	torch.randn(2    ,  3			)
     lowerCamelCase__					:   Dict      				=	model(UpperCAmelCase			)
     self.assertEqual(output.device    ,  UpperCAmelCase			)

     # Removing hooks loads back the weights in the model.
     remove_hook_from_submodules(UpperCAmelCase			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.batchnorm.weight.device    ,  torch.device('cpu'			)			)
     self.assertEqual(model.lineara.weight.device    ,  torch.device('cpu'			)			)












import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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
_UpperCAmelCase       : List[str]			     =       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""")

@dataclass
class      lowerCAmelCase							:
 UpperCAmelCase__									=	field(
     default="""cifar10""",						metadata={"""help""": """Name of a dataset from the datasets package"""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """The column name of the images in the files."""}    )
 UpperCAmelCase__									=	field(default=__UpperCamelCase,						metadata={"""help""": """A folder containing the training data."""}    )
 UpperCAmelCase__									=	field(default=__UpperCamelCase,						metadata={"""help""": """A folder containing the validation data."""}    )
 UpperCAmelCase__									=	field(
     default=0.15,						metadata={"""help""": """Percent to split off of train for validation."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of training examples to this """
             """value if set."""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
             """value if set."""
         )
     },						)
 def   A_			(   self						:   Tuple			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[int]      				=	{}
     if self.train_dir is not None:
         lowerCamelCase__					:   int      				=	self.train_dir
     if self.validation_dir is not None:
         lowerCamelCase__					:   Dict      				=	self.validation_dir
     lowerCamelCase__					:   Union[str, Any]      				=	data_files if data_files else None


@dataclass
class      lowerCAmelCase							:
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}    )
 UpperCAmelCase__									=	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"""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}    )
 UpperCAmelCase__									=	field(
     default="""main""",						metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""},						)
 UpperCAmelCase__									=	field(default=__UpperCamelCase,						metadata={"""help""": """Name or path of preprocessor config."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
             """with private models)."""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=0.75,						metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}    )


@dataclass
class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	field(
     default=1E-3,						metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}    )


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Tuple:
    lowerCamelCase__					:   str      				=	torch.stack([example['pixel_values'] for example in examples]				)
    return {"pixel_values": pixel_values}


def  SCREAMING_SNAKE_CASE   ( )    ->  Union[str, Any]:
    # 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.

    lowerCamelCase__					:   Tuple      				=	HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)				)
    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.
        lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
    else:
        lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Union[str, 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_mae'		,			_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()

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

    # Load pretrained model and image processor
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    lowerCamelCase__					:   Union[str, Any]      				=	{
        '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:
        lowerCamelCase__					:   Tuple      				=	ViTMAEConfig.from_pretrained(model_args.config_name		,			**_UpperCAmelCase				)
    elif model_args.model_name_or_path:
        lowerCamelCase__					:   Tuple      				=	ViTMAEConfig.from_pretrained(model_args.model_name_or_path		,			**_UpperCAmelCase				)
    else:
        lowerCamelCase__					:   List[Any]      				=	ViTMAEConfig()
        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}"""				)

    # adapt config
    config.update(
        {
            'mask_ratio': model_args.mask_ratio,
            'norm_pix_loss': model_args.norm_pix_loss,
        }				)

    # create image processor
    if model_args.image_processor_name:
        lowerCamelCase__					:   Optional[Any]      				=	ViTImageProcessor.from_pretrained(model_args.image_processor_name		,			**_UpperCAmelCase				)
    elif model_args.model_name_or_path:
        lowerCamelCase__					:   Any      				=	ViTImageProcessor.from_pretrained(model_args.model_name_or_path		,			**_UpperCAmelCase				)
    else:
        lowerCamelCase__					:   List[Any]      				=	ViTImageProcessor()

    # create model
    if model_args.model_name_or_path:
        lowerCamelCase__					:   Dict      				=	ViTMAEForPreTraining.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'				)
        lowerCamelCase__					:   Dict      				=	ViTMAEForPreTraining(_UpperCAmelCase				)

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

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

    # transformations as done in original MAE paper
    # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
    if "shortest_edge" in image_processor.size:
        lowerCamelCase__					:   List[Any]      				=	image_processor.size['shortest_edge']
    else:
        lowerCamelCase__					:   Optional[int]      				=	(image_processor.size['height'], image_processor.size['width'])
    lowerCamelCase__					:   Optional[Any]      				=	Compose(
        [
            Lambda(lambda _UpperCAmelCase				: img.convert('RGB'				) if img.mode != "RGB" else img				),
            RandomResizedCrop(_UpperCAmelCase		,			scale=(0.2, 1.0)		,			interpolation=InterpolationMode.BICUBIC				),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=image_processor.image_mean		,			std=image_processor.image_std				),
        ]				)

    def preprocess_images(_UpperCAmelCase				):
        lowerCamelCase__					:   Tuple      				=	[transforms(_UpperCAmelCase				) for image in 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:
            lowerCamelCase__					:   Optional[int]      				=	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:
            lowerCamelCase__					:   List[str]      				=	(
                ds['validation'].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
            )
        # Set the validation transforms
        ds["validation"].set_transform(_UpperCAmelCase				)

    # Compute absolute learning rate
    lowerCamelCase__					:   Union[str, Any]      				=	(
        training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
    )
    if training_args.base_learning_rate is not None:
        lowerCamelCase__					:   Tuple      				=	training_args.base_learning_rate * total_train_batch_size / 256

    # Initialize our trainer
    lowerCamelCase__					:   str      				=	Trainer(
        model=_UpperCAmelCase		,			args=_UpperCAmelCase		,			train_dataset=ds['train'] if training_args.do_train else None		,			eval_dataset=ds['validation'] if training_args.do_eval else None		,			tokenizer=_UpperCAmelCase		,			data_collator=_UpperCAmelCase		,			)

    # Training
    if training_args.do_train:
        lowerCamelCase__					:   List[str]      				=	None
        if training_args.resume_from_checkpoint is not None:
            lowerCamelCase__					:   Optional[int]      				=	training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            lowerCamelCase__					:   Any      				=	last_checkpoint
        lowerCamelCase__					:   Optional[int]      				=	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:
        lowerCamelCase__					:   Dict      				=	trainer.evaluate()
        trainer.log_metrics('eval'		,			_UpperCAmelCase				)
        trainer.save_metrics('eval'		,			_UpperCAmelCase				)

    # Write model card and (optionally) push to hub
    lowerCamelCase__					:   Union[str, Any]      				=	{
        'tasks': 'masked-auto-encoding',
        'dataset': data_args.dataset_name,
        'tags': ['masked-auto-encoding'],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**_UpperCAmelCase				)
    else:
        trainer.create_model_card(**_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Optional[int]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()





