
import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
 from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DPTImageProcessor
class       _lowerCAmelCase  :

 """simple docstring"""

 def __init__(						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			Tuple=2					, SCREAMING_SNAKE_CASE__			:			Optional[int]=32					, SCREAMING_SNAKE_CASE__			:			Dict=16					, SCREAMING_SNAKE_CASE__			:			str=3					, SCREAMING_SNAKE_CASE__			:			str=True					, SCREAMING_SNAKE_CASE__			:			Optional[int]=True					, SCREAMING_SNAKE_CASE__			:			Tuple=32					, SCREAMING_SNAKE_CASE__			:			int=4					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=[0, 1, 2, 3]					, SCREAMING_SNAKE_CASE__			:			int=4					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=37					, SCREAMING_SNAKE_CASE__			:			Optional[Any]="gelu"					, SCREAMING_SNAKE_CASE__			:			Dict=0.1					, SCREAMING_SNAKE_CASE__			:			Optional[int]=0.1					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=0.02					, SCREAMING_SNAKE_CASE__			:			List[str]=3					, SCREAMING_SNAKE_CASE__			:			List[Any]=[1, 3_84, 24, 24]					, SCREAMING_SNAKE_CASE__			:			Optional[int]=True					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=None					, ):






      """simple docstring"""



      UpperCamelCase						    =					parent
      UpperCamelCase						    =					batch_size
      UpperCamelCase						    =					image_size
      UpperCamelCase						    =					patch_size
      UpperCamelCase						    =					num_channels
      UpperCamelCase						    =					is_training
      UpperCamelCase						    =					use_labels
      UpperCamelCase						    =					hidden_size
      UpperCamelCase						    =					num_hidden_layers
      UpperCamelCase						    =					backbone_out_indices
      UpperCamelCase						    =					num_attention_heads
      UpperCamelCase						    =					intermediate_size
      UpperCamelCase						    =					hidden_act
      UpperCamelCase						    =					hidden_dropout_prob
      UpperCamelCase						    =					attention_probs_dropout_prob
      UpperCamelCase						    =					initializer_range
      UpperCamelCase						    =					num_labels
      UpperCamelCase						    =					backbone_featmap_shape
      UpperCamelCase						    =					scope
      UpperCamelCase						    =					is_hybrid
      # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
      UpperCamelCase						    =					(image_size // patch_size) ** 2
      UpperCamelCase						    =					num_patches + 1

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      UpperCamelCase						    =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      UpperCamelCase						    =					None
      if self.use_labels:
           UpperCamelCase						    =					ids_tensor([self.batch_size, self.image_size, self.image_size]					, self.num_labels   )

      UpperCamelCase						    =					self.get_config()

      return config, pixel_values, labels

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					{
          'global_padding': 'same',
          'layer_type': 'bottleneck',
          'depths': [3, 4, 9],
          'out_features': ['stage1', 'stage2', 'stage3'],
          'embedding_dynamic_padding': True,
          'hidden_sizes': [96, 1_92, 3_84, 7_68],
          'num_groups': 2,
      }

      return DPTConfig(
          image_size=self.image_size					, patch_size=self.patch_size					, num_channels=self.num_channels					, hidden_size=self.hidden_size					, num_hidden_layers=self.num_hidden_layers					, backbone_out_indices=self.backbone_out_indices					, num_attention_heads=self.num_attention_heads					, intermediate_size=self.intermediate_size					, hidden_act=self.hidden_act					, hidden_dropout_prob=self.hidden_dropout_prob					, attention_probs_dropout_prob=self.attention_probs_dropout_prob					, is_decoder=SCREAMING_SNAKE_CASE__					, initializer_range=self.initializer_range					, is_hybrid=self.is_hybrid					, backbone_config=SCREAMING_SNAKE_CASE__					, backbone_featmap_shape=self.backbone_featmap_shape					, )

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					DPTModel(config=SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__   )
      self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)   )

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.num_labels
      UpperCamelCase						    =					DPTForDepthEstimation(SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__   )
      self.parent.assertEqual(result.predicted_depth.shape					, (self.batch_size, self.image_size, self.image_size)   )

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Dict   ):






      """simple docstring"""



      UpperCamelCase						    =					self.num_labels
      UpperCamelCase						    =					DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE__   )
      model.to(SCREAMING_SNAKE_CASE__   )
      model.eval()
      UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, labels=SCREAMING_SNAKE_CASE__   )
      self.parent.assertEqual(
          result.logits.shape					, (self.batch_size, self.num_labels, self.image_size, self.image_size)   )






 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.prepare_config_and_inputs()
      UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					config_and_inputs
      UpperCamelCase						    =					{'pixel_values': pixel_values}
      return config, inputs_dict




@require_torch
class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Tuple       						=(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
 SCREAMING_SNAKE_CASE_				:					str       						=(
     {
         "depth-estimation": DPTForDepthEstimation,
         "feature-extraction": DPTModel,
         "image-segmentation": DPTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 SCREAMING_SNAKE_CASE_				:					Any       						=False
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=False
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=False

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					DPTModelTester(self   )
      UpperCamelCase						    =					ConfigTester(self					, config_class=SCREAMING_SNAKE_CASE__					, has_text_modality=SCREAMING_SNAKE_CASE__					, hidden_size=37   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      self.config_tester.run_common_tests()

 @unittest.skip(reason='DPT does not use inputs_embeds'   )
 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
           self.assertIsInstance(model.get_input_embeddings()					, (nn.Module)   )
           UpperCamelCase						    =					model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__					, nn.Linear   )   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					inspect.signature(model.forward   )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase						    =					[*signature.parameters.keys()]

           UpperCamelCase						    =					['pixel_values']
           self.assertListEqual(arg_names[:1]					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      for model_class in self.all_model_classes:
           if model_class.__name__ == "DPTForDepthEstimation":
                continue

           UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase						    =					True

           if model_class in get_values(SCREAMING_SNAKE_CASE__   ):
                continue

           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
           model.to(SCREAMING_SNAKE_CASE__   )
           model.train()
           UpperCamelCase						    =					self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, return_labels=SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					model(**SCREAMING_SNAKE_CASE__   ).loss
           loss.backward()

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      for model_class in self.all_model_classes:
           if model_class.__name__ == "DPTForDepthEstimation":
                continue

           UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
           UpperCamelCase						    =					False
           UpperCamelCase						    =					True

           if model_class in get_values(SCREAMING_SNAKE_CASE__   ) or not model_class.supports_gradient_checkpointing:
                continue
           UpperCamelCase						    =					model_class(SCREAMING_SNAKE_CASE__   )
           model.to(SCREAMING_SNAKE_CASE__   )
           model.gradient_checkpointing_enable()
           model.train()
           UpperCamelCase						    =					self._prepare_for_class(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, return_labels=SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					model(**SCREAMING_SNAKE_CASE__   ).loss
           loss.backward()

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      UpperCamelCase						    =					_config_zero_init(SCREAMING_SNAKE_CASE__   )
      for model_class in self.all_model_classes:
           UpperCamelCase						    =					model_class(config=SCREAMING_SNAKE_CASE__   )
           # Skip the check for the backbone
           UpperCamelCase						    =					[]
           for name, module in model.named_modules():
                if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                     UpperCamelCase						    =					[F'{name}.{key}' for key in module.state_dict().keys()]
                     break

           for name, param in model.named_parameters():
                if param.requires_grad:
                     if name in backbone_params:
                          continue
                     self.assertIn(
                         ((param.data.mean() * 1e9).round() / 1e9).item()					, [0.0, 1.0]					, msg=F'Parameter {name} of model {model_class} seems not properly initialized'					, )

 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      pass

 @slow
 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
           UpperCamelCase						    =					DPTModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
           self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					'add'
      with self.assertRaises(SCREAMING_SNAKE_CASE__   ):
           UpperCamelCase						    =					DPTForDepthEstimation(SCREAMING_SNAKE_CASE__   )




def  __lowerCamelCase					( )       ->      List[str]:
     UpperCamelCase						    =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
     return image







@require_torch
@require_vision
@slow
class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'   )
      UpperCamelCase						    =					DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'   ).to(SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					prepare_img()
      UpperCamelCase						    =					image_processor(images=SCREAMING_SNAKE_CASE__					, return_tensors='pt'   ).to(SCREAMING_SNAKE_CASE__   )

      # forward pass
      with torch.no_grad():
           UpperCamelCase						    =					model(**SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					outputs.predicted_depth

      # verify the predicted depth
      UpperCamelCase						    =					torch.Size((1, 3_84, 3_84)   )
      self.assertEqual(predicted_depth.shape					, SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					torch.tensor(
          [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]   ).to(SCREAMING_SNAKE_CASE__   )

      self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00					, SCREAMING_SNAKE_CASE__					, atol=1e-4   )   )




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
_snake_case      = logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class       _lowerCAmelCase  :

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default="cifar10"			,	metadata={"help": "Name of a dataset from the datasets package"}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	metadata={"help": "The column name of the images in the files."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(default=__magic_name__			,	metadata={"help": "A folder containing the training data."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(default=__magic_name__			,	metadata={"help": "A folder containing the validation data."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[float]       						=field(
     default=0.15			,	metadata={"help": "Percent to split off of train for validation."}							)
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=field(
     default=__magic_name__			,	metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of training examples to this "
             "value if set."
         )
     }			,	)
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=field(
     default=__magic_name__			,	metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
             "value if set."
         )
     }			,	)

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					{}
      if self.train_dir is not None:
           UpperCamelCase						    =					self.train_dir
      if self.validation_dir is not None:
           UpperCamelCase						    =					self.validation_dir
      UpperCamelCase						    =					data_files if data_files else None




@dataclass
class       _lowerCAmelCase  :

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					str       						=field(
     default=__magic_name__			,	metadata={
         "help": (
             "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
         )
     }			,	)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}							)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	metadata={
         "help": (
             "Override some existing default config settings when a model is trained from scratch. Example: "
             "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
         )
     }			,	)
 SCREAMING_SNAKE_CASE_				:					Optional[str]       						=field(
     default=__magic_name__			,	metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}							)
 SCREAMING_SNAKE_CASE_				:					str       						=field(
     default="main"			,	metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}			,	)
 SCREAMING_SNAKE_CASE_				:					str       						=field(default=__magic_name__			,	metadata={"help": "Name or path of preprocessor config."}							)
 SCREAMING_SNAKE_CASE_				:					bool       						=field(
     default=__magic_name__			,	metadata={
         "help": (
             "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
             "with private models)."
         )
     }			,	)
 SCREAMING_SNAKE_CASE_				:					float       						=field(
     default=0.75			,	metadata={"help": "The ratio of the number of masked tokens in the input sequence."}							)
 SCREAMING_SNAKE_CASE_				:					bool       						=field(
     default=__magic_name__			,	metadata={"help": "Whether or not to train with normalized pixel values as target."}							)




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					float       						=field(
     default=1E-3			,	metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}							)




def  __lowerCamelCase					( _lowercase			)       ->      Optional[int]:
     UpperCamelCase						    =					torch.stack([example['pixel_values'] for example in examples]			)
     return {"pixel_values": pixel_values}

def  __lowerCamelCase					( )       ->      List[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.

     UpperCamelCase						    =					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.
          UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
     else:
          UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					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'						,     _lowercase						,     _lowercase			)

     # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'						,     datefmt='%m/%d/%Y %H:%M:%S'						,     handlers=[logging.StreamHandler(sys.stdout			)]						,     )

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     UpperCamelCase						    =					training_args.get_process_log_level()
     logger.setLevel(_lowercase			)
     transformers.utils.logging.set_verbosity(_lowercase			)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

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

     # Detecting last checkpoint.
     UpperCamelCase						    =					None
     if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
          UpperCamelCase						    =					get_last_checkpoint(training_args.output_dir			)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
               raise ValueError(
                   F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   'Use --overwrite_output_dir to overcome.'			)
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
               logger.info(
                   F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'			)

    # Initialize our dataset.
     UpperCamelCase						    =					load_dataset(
         data_args.dataset_name						,     data_args.dataset_config_name						,     data_files=data_args.data_files						,     cache_dir=model_args.cache_dir						,     use_auth_token=True if model_args.use_auth_token else None						,     )

     # If we don't have a validation split, split off a percentage of train as validation.
     UpperCamelCase						    =					None if 'validation' in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split						,     _lowercase			) and data_args.train_val_split > 0.0:
          UpperCamelCase						    =					ds['train'].train_test_split(data_args.train_val_split			)
          UpperCamelCase						    =					split['train']
          UpperCamelCase						    =					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.
     UpperCamelCase						    =					{
         '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:
          UpperCamelCase						    =					ViTMAEConfig.from_pretrained(model_args.config_name						,     **_lowercase			)
     elif model_args.model_name_or_path:
          UpperCamelCase						    =					ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,     **_lowercase			)
     else:
          UpperCamelCase						    =					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:
          UpperCamelCase						    =					ViTImageProcessor.from_pretrained(model_args.image_processor_name						,     **_lowercase			)
     elif model_args.model_name_or_path:
          UpperCamelCase						    =					ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,     **_lowercase			)
     else:
          UpperCamelCase						    =					ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          UpperCamelCase						    =					ViTMAEForPreTraining.from_pretrained(
              model_args.model_name_or_path						,     from_tf=bool('.ckpt' in model_args.model_name_or_path			)						,     config=_lowercase						,     cache_dir=model_args.cache_dir						,     revision=model_args.model_revision						,     use_auth_token=True if model_args.use_auth_token else None						,     )
     else:
          logger.info('Training new model from scratch'			)
          UpperCamelCase						    =					ViTMAEForPreTraining(_lowercase			)

     if training_args.do_train:
          UpperCamelCase						    =					ds['train'].column_names
     else:
          UpperCamelCase						    =					ds['validation'].column_names

     if data_args.image_column_name is not None:
          UpperCamelCase						    =					data_args.image_column_name
     elif "image" in column_names:
          UpperCamelCase						    =					'image'
     elif "img" in column_names:
          UpperCamelCase						    =					'img'
     else:
          UpperCamelCase						    =					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:
          UpperCamelCase						    =					image_processor.size['shortest_edge']
     else:
          UpperCamelCase						    =					(image_processor.size['height'], image_processor.size['width'])
     UpperCamelCase						    =					Compose(
         [
             Lambda(lambda _lowercase			: img.convert('RGB'			) if img.mode != "RGB" else img			),
             RandomResizedCrop(_lowercase						,     scale=(0.2, 1.0)						,     interpolation=InterpolationMode.BICUBIC			),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean						,     std=image_processor.image_std			),
         ]			)

     def preprocess_images(_lowercase			):
          UpperCamelCase						    =					[transforms(_lowercase			) 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:
               UpperCamelCase						    =					ds['train'].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
          # Set the training transforms
          ds["train"].set_transform(_lowercase			)

     if training_args.do_eval:
          if "validation" not in ds:
               raise ValueError('--do_eval requires a validation dataset'			)
          if data_args.max_eval_samples is not None:
               UpperCamelCase						    =					(
                   ds['validation'].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
               )
          # Set the validation transforms
          ds["validation"].set_transform(_lowercase			)

     # Compute absolute learning rate
     UpperCamelCase						    =					(
         training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
     )
     if training_args.base_learning_rate is not None:
          UpperCamelCase						    =					training_args.base_learning_rate * total_train_batch_size / 256

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

     # Training
     if training_args.do_train:
          UpperCamelCase						    =					None
          if training_args.resume_from_checkpoint is not None:
               UpperCamelCase						    =					training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               UpperCamelCase						    =					last_checkpoint
          UpperCamelCase						    =					trainer.train(resume_from_checkpoint=_lowercase			)
          trainer.save_model()
          trainer.log_metrics('train'						,     train_result.metrics			)
          trainer.save_metrics('train'						,     train_result.metrics			)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          UpperCamelCase						    =					trainer.evaluate()
          trainer.log_metrics('eval'						,     _lowercase			)
          trainer.save_metrics('eval'						,     _lowercase			)

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

def  __lowerCamelCase					( _lowercase			)       ->      int:
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()


