




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
lowercase__				:      List[Any]	   = 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")

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




@dataclass
class       a__	:
     a		:      Optional[str]		 =     field(
         default="""cifar10"""       , metadata={"""help""": """Name of a dataset from the datasets package"""}				)
     a		:      Optional[str]		 =     field(
         default=UpperCamelCase__       , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}				)
     a		:      Optional[str]		 =     field(
         default=UpperCamelCase__       , metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}       , )
     a		:      Optional[str]		 =     field(default=UpperCamelCase__       , metadata={"""help""": """A folder containing the training data."""}				)
     a		:      Optional[str]		 =     field(default=UpperCamelCase__       , metadata={"""help""": """A folder containing the validation data."""}				)
     a		:      Optional[float]		 =     field(
         default=0.1_5       , metadata={"""help""": """Percent to split off of train for validation."""}				)
     a		:      int		 =     field(default=32       , metadata={"""help""": """The size of the square patches to use for masking."""}				)
     a		:      float		 =     field(
         default=0.6       , metadata={"""help""": """Percentage of patches to mask."""}       , )
     a		:      Optional[int]		 =     field(
         default=UpperCamelCase__       , metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of training examples to this """
                 """value if set."""
             )
         }       , )
     a		:      Optional[int]		 =     field(
         default=UpperCamelCase__       , metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                 """value if set."""
             )
         }       , )







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



          '''simple docstring'''



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







@dataclass
class       a__	:
     a		:      str		 =     field(
         default=UpperCamelCase__       , metadata={
             """help""": (
                 """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
                 """checkpoint identifier on the hub. """
                 """Don't set if you want to train a model from scratch."""
             )
         }       , )
     a		:      Optional[str]		 =     field(
         default=UpperCamelCase__       , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCamelCase__				)}       , )
     a		:      Optional[str]		 =     field(
         default=UpperCamelCase__       , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
     a		:      Optional[str]		 =     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"""
             )
         }       , )
     a		:      Optional[str]		 =     field(
         default=UpperCamelCase__       , metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}       , )
     a		:      str		 =     field(
         default="""main"""       , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}       , )
     a		:      str		 =     field(default=UpperCamelCase__       , metadata={"""help""": """Name or path of preprocessor config."""}				)
     a		:      bool		 =     field(
         default=UpperCamelCase__       , metadata={
             """help""": (
                 """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                 """with private models)."""
             )
         }       , )
     a		:      Optional[int]		 =     field(
         default=UpperCamelCase__       , metadata={
             """help""": (
                 """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
             )
         }       , )
     a		:      Optional[int]		 =     field(
         default=UpperCamelCase__       , metadata={
             """help""": (
                 """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
             )
         }       , )
     a		:      Optional[int]		 =     field(
         default=UpperCamelCase__       , metadata={"""help""": """Stride to use for the encoder."""}       , )







class       a__	:







     def __init__(     self  ,		A=192  ,		A=32  ,		A=4  ,		A=0.6			)  ->   List[Any]:



          '''simple docstring'''



          a     =				input_size
          a     =				mask_patch_size
          a     =				model_patch_size
          a     =				mask_ratio

          if self.input_size % self.mask_patch_size != 0:
               raise ValueError("Input size must be divisible by mask patch size"			)
          if self.mask_patch_size % self.model_patch_size != 0:
               raise ValueError("Mask patch size must be divisible by model patch size"			)

          a     =				self.input_size // self.mask_patch_size
          a     =				self.mask_patch_size // self.model_patch_size

          a     =				self.rand_size**2
          a     =				int(np.ceil(self.token_count * self.mask_ratio			)			)







     def __call__(     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				np.random.permutation(self.token_count			)[: self.mask_count]
          a     =				np.zeros(self.token_count  ,		dtype=A			)
          a     =				1

          a     =				mask.reshape((self.rand_size, self.rand_size)			)
          a     =				mask.repeat(self.scale  ,		axis=0			).repeat(self.scale  ,		axis=1			)

          return torch.tensor(mask.flatten()			)







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     a     =				torch.stack([example["pixel_values"] for example in examples])
     a     =				torch.stack([example["mask"] for example in examples])
     return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def  SCREAMING_SNAKE_CASE							(       )      ->					Dict:
     # 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.

     a     =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
     if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          a  ,				a  ,				a     =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
     else:
          a  ,				a  ,				a     =				parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry("run_mim"		, __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()

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

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

     # If we don't have a validation split, split off a percentage of train as validation.
     a     =				None if "validation" in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split		, __UpperCamelCase) and data_args.train_val_split > 0.0:
          a     =				ds["train"].train_test_split(data_args.train_val_split)
          a     =				split["train"]
          a     =				split["test"]

     # Create config
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     a     =				{
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
         "use_auth_token": True if model_args.use_auth_token else None,
     }
     if model_args.config_name_or_path:
          a     =				AutoConfig.from_pretrained(model_args.config_name_or_path		, **__UpperCamelCase)
     elif model_args.model_name_or_path:
          a     =				AutoConfig.from_pretrained(model_args.model_name_or_path		, **__UpperCamelCase)
     else:
          a     =				CONFIG_MAPPING[model_args.model_type]()
          logger.warning("You are instantiating a new config instance from scratch.")
          if model_args.config_overrides is not None:
               logger.info(f'''Overriding config: {model_args.config_overrides}''')
               config.update_from_string(model_args.config_overrides)
               logger.info(f'''New config: {config}''')

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
     if hasattr(__UpperCamelCase		, "decoder_type"):
          a     =				"simmim"

     # adapt config
     a     =				model_args.image_size if model_args.image_size is not None else config.image_size
     a     =				model_args.patch_size if model_args.patch_size is not None else config.patch_size
     a     =				(
         model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
     )

     config.update(
         {
             "image_size": model_args.image_size,
             "patch_size": model_args.patch_size,
             "encoder_stride": model_args.encoder_stride,
         })

     # create image processor
     if model_args.image_processor_name:
          a     =				AutoImageProcessor.from_pretrained(model_args.image_processor_name		, **__UpperCamelCase)
     elif model_args.model_name_or_path:
          a     =				AutoImageProcessor.from_pretrained(model_args.model_name_or_path		, **__UpperCamelCase)
     else:
          a     =				{
              conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
          }
          a     =				IMAGE_PROCESSOR_TYPES[model_args.model_type]()

     # create model
     if model_args.model_name_or_path:
          a     =				AutoModelForMaskedImageModeling.from_pretrained(
              model_args.model_name_or_path		, from_tf=bool(".ckpt" in model_args.model_name_or_path)		, config=__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")
          a     =				AutoModelForMaskedImageModeling.from_config(__UpperCamelCase)

     if training_args.do_train:
          a     =				ds["train"].column_names
     else:
          a     =				ds["validation"].column_names

     if data_args.image_column_name is not None:
          a     =				data_args.image_column_name
     elif "image" in column_names:
          a     =				"image"
     elif "img" in column_names:
          a     =				"img"
     else:
          a     =				column_names[0]

     # transformations as done in original SimMIM paper
     # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
     a     =				Compose(
         [
             Lambda(lambda __UpperCamelCase: img.convert("RGB") if img.mode != "RGB" else img),
             RandomResizedCrop(model_args.image_size		, scale=(0.67, 1.0)		, ratio=(3.0 / 4.0, 4.0 / 3.0)),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean		, std=image_processor.image_std),
         ])

     # create mask generator
     a     =				MaskGenerator(
         input_size=model_args.image_size		, mask_patch_size=data_args.mask_patch_size		, model_patch_size=model_args.patch_size		, mask_ratio=data_args.mask_ratio		, )

     def preprocess_images(__UpperCamelCase):
          a     =				[transforms(__UpperCamelCase) for image in examples[image_column_name]]
          a     =				[mask_generator() for i in range(len(examples[image_column_name]))]

          return examples

     if training_args.do_train:
          if "train" not in ds:
               raise ValueError("--do_train requires a train dataset")
          if data_args.max_train_samples is not None:
               a     =				ds["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
          # Set the training transforms
          ds["train"].set_transform(__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:
               a     =				(
                   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
     a     =				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:
          a     =				None
          if training_args.resume_from_checkpoint is not None:
               a     =				training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               a     =				last_checkpoint
          a     =				trainer.train(resume_from_checkpoint=__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:
          a     =				trainer.evaluate()
          trainer.log_metrics("eval"		, __UpperCamelCase)
          trainer.save_metrics("eval"		, __UpperCamelCase)

     # Write model card and (optionally) push to hub
     a     =				{
         "finetuned_from": model_args.model_name_or_path,
         "tasks": "masked-image-modeling",
         "dataset": data_args.dataset_name,
         "tags": ["masked-image-modeling"],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**__UpperCamelCase)
     else:
          trainer.create_model_card(**__UpperCamelCase)


if __name__ == "__main__":
   main()







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       a__	:







     def __init__(     self  ,		A  ,		A=2  ,		A=32  ,		A=16  ,		A=3  ,		A=True  ,		A=True  ,		A=32  ,		A=4  ,		A=[0, 1, 2, 3]  ,		A=4  ,		A=37  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=0.0_2  ,		A=3  ,		A=[1, 384, 24, 24]  ,		A=True  ,		A=None  ,		)  ->   Any:



          '''simple docstring'''



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







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



          '''simple docstring'''



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

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

          a     =				self.get_config()

          return config, pixel_values, labels







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				{
              "global_padding": "same",
              "layer_type": "bottleneck",
              "depths": [3, 4, 9],
              "out_features": ["stage1", "stage2", "stage3"],
              "embedding_dynamic_padding": True,
              "hidden_sizes": [96, 192, 384, 768],
              "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=A  ,		initializer_range=self.initializer_range  ,		is_hybrid=self.is_hybrid  ,		backbone_config=A  ,		backbone_featmap_shape=self.backbone_featmap_shape  ,		)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   str:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   Optional[int]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   Dict:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.prepare_config_and_inputs()
          a  ,				a  ,				a     =				config_and_inputs
          a     =				{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class       a__	( UpperCamelCase__       , UpperCamelCase__       , unittest.TestCase				):
     a		:      Union[str, Any]		 =     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     a		:      Union[str, Any]		 =     (
         {
             """depth-estimation""": DPTForDepthEstimation,
             """feature-extraction""": DPTModel,
             """image-segmentation""": DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

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







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				DPTModelTester(self			)
          a     =				ConfigTester(self  ,		config_class=A  ,		has_text_modality=A  ,		hidden_size=37			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          self.config_tester.run_common_tests()







     @unittest.skip(reason="DPT does not use inputs_embeds"			)
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()

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







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()

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

               a     =				["pixel_values"]
               self.assertListEqual(arg_names[:1]  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*A			)







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



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*A			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



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

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

               if model_class in get_values(A			):
                    continue

               a     =				model_class(A			)
               model.to(A			)
               model.train()
               a     =				self._prepare_for_class(A  ,		A  ,		return_labels=A			)
               a     =				model(**A			).loss
               loss.backward()







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



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

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

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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()

          a     =				_config_zero_init(A			)
          for model_class in self.all_model_classes:
               a     =				model_class(config=A			)
               # Skip the check for the backbone
               a     =				[]
               for name, module in model.named_modules():
                    if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                         a     =				[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			)  ->   List[Any]:



          '''simple docstring'''



          pass







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()
          a     =				"add"
          with self.assertRaises(A			):
               a     =				DPTForDepthEstimation(A			)







def  SCREAMING_SNAKE_CASE							(       )      ->					str:
     a     =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     return image





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







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"			)
          a     =				DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"			).to(A			)

          a     =				prepare_img()
          a     =				image_processor(images=A  ,		return_tensors="pt"			).to(A			)

          # forward pass
          with torch.no_grad():
               a     =				model(**A			)
               a     =				outputs.predicted_depth

          # verify the predicted depth
          a     =				torch.Size((1, 384, 384)			)
          self.assertEqual(predicted_depth.shape  ,		A			)

          a     =				torch.tensor(
              [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]			).to(A			)

          self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100  ,		A  ,		atol=1e-4			)			)

