






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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A_       :int       =						logging.getLogger(__name__)

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

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

A_       :List[str]       =						list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A_       :Tuple       =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 				__A     :





				"""simple docstring"""







				UpperCamelCase__ :      Optional[str]			 =field(
				    default="""cifar10"""      ,     metadata={"""help""": """Name of a dataset from the datasets package"""} )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=a      ,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=a      ,     metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}      ,     )
				UpperCamelCase__ :      Optional[str]			 =field(default=a      ,     metadata={"""help""": """A folder containing the training data."""} )
				UpperCamelCase__ :      Optional[str]			 =field(default=a      ,     metadata={"""help""": """A folder containing the validation data."""} )
				UpperCamelCase__ :      Optional[float]			 =field(
				    default=0.15      ,     metadata={"""help""": """Percent to split off of train for validation."""} )
				UpperCamelCase__ :      int			 =field(default=3_2      ,     metadata={"""help""": """The size of the square patches to use for masking."""} )
				UpperCamelCase__ :      float			 =field(
				    default=0.6      ,     metadata={"""help""": """Percentage of patches to mask."""}      ,     )
				UpperCamelCase__ :      Optional[int]			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    }      ,     )
				UpperCamelCase__ :      Optional[int]			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
				            """value if set."""
				        )
				    }      ,     )


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		={}
									if self.train_dir is not None:
														__UpperCamelCase      :					Dict       		=self.train_dir
									if self.validation_dir is not None:
														__UpperCamelCase      :					Any       		=self.validation_dir
									__UpperCamelCase      :					Dict       		=data_files if data_files else None




@dataclass
class 				__A     :





				"""simple docstring"""







				UpperCamelCase__ :      str			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
				            """checkpoint identifier on the hub. """
				            """Don't set if you want to train a model from scratch."""
				        )
				    }      ,     )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=a      ,     metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a )}      ,     )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=a      ,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """Override some existing default config settings when a model is trained from scratch. Example: """
				            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
				        )
				    }      ,     )
				UpperCamelCase__ :      Optional[str]			 =field(
				    default=a      ,     metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}      ,     )
				UpperCamelCase__ :      str			 =field(
				    default="""main"""      ,     metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}      ,     )
				UpperCamelCase__ :      str			 =field(default=a      ,     metadata={"""help""": """Name or path of preprocessor config."""} )
				UpperCamelCase__ :      bool			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }      ,     )
				UpperCamelCase__ :      Optional[int]			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
				        )
				    }      ,     )
				UpperCamelCase__ :      Optional[int]			 =field(
				    default=a      ,     metadata={
				        """help""": (
				            """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
				        )
				    }      ,     )
				UpperCamelCase__ :      Optional[int]			 =field(
				    default=a      ,     metadata={"""help""": """Stride to use for the encoder."""}      ,     )




class 				__A     :





				"""simple docstring"""







				def __init__( self							,  lowerCamelCase__=192							,  lowerCamelCase__=32							,  lowerCamelCase__=4							,  lowerCamelCase__=0.6    ):




									"""simple docstring"""

									__UpperCamelCase      :					int       		=input_size
									__UpperCamelCase      :					Any       		=mask_patch_size
									__UpperCamelCase      :					List[Any]       		=model_patch_size
									__UpperCamelCase      :					Union[str, Any]       		=mask_ratio

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

									__UpperCamelCase      :					Any       		=self.input_size // self.mask_patch_size
									__UpperCamelCase      :					Dict       		=self.mask_patch_size // self.model_patch_size

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







				def __call__( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					str       		=np.random.permutation(self.token_count    )[: self.mask_count]
									__UpperCamelCase      :					Optional[int]       		=np.zeros(self.token_count							,  dtype=lowerCamelCase__    )
									__UpperCamelCase      :					int       		=1

									__UpperCamelCase      :					Any       		=mask.reshape((self.rand_size, self.rand_size)    )
									__UpperCamelCase      :					Tuple       		=mask.repeat(self.scale							,  axis=0    ).repeat(self.scale							,  axis=1    )

									return torch.tensor(mask.flatten()    )






def 							A     (       a_					)     ->						int:
					__UpperCamelCase      :					List[str]       		=torch.stack([example['pixel_values'] for example in examples]					)
					__UpperCamelCase      :					Dict       		=torch.stack([example['mask'] for example in examples]					)
					return {"pixel_values": pixel_values, "bool_masked_pos": mask}






def 							A     (       )     ->						List[str]:
					# 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      :					str       		=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.
										__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					Tuple       		=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
					else:
										__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase      :					Dict       		=parser.parse_args_into_dataclasses()

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

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

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

					__UpperCamelCase      :					Optional[Any]       		=training_args.get_process_log_level()
					logger.setLevel(a_					)
					transformers.utils.logging.set_verbosity(a_					)
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

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

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

    # Initialize our dataset.
					__UpperCamelCase      :					Union[str, 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.
					__UpperCamelCase      :					int       		=None if 'validation' in ds.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split							,a_					) and data_args.train_val_split > 0.0:
										__UpperCamelCase      :					int       		=ds['train'].train_test_split(data_args.train_val_split					)
										__UpperCamelCase      :					Optional[Any]       		=split['train']
										__UpperCamelCase      :					List[str]       		=split['test']

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

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
					if hasattr(a_							,'decoder_type'					):
										__UpperCamelCase      :					List[str]       		='simmim'

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

					# create model
					if model_args.model_name_or_path:
										__UpperCamelCase      :					List[Any]       		=AutoModelForMaskedImageModeling.from_pretrained(
										    model_args.model_name_or_path							,from_tf=bool('.ckpt' in model_args.model_name_or_path					)							,config=a_							,cache_dir=model_args.cache_dir							,revision=model_args.model_revision							,use_auth_token=True if model_args.use_auth_token else None							,)
					else:
										logger.info('Training new model from scratch'					)
										__UpperCamelCase      :					Tuple       		=AutoModelForMaskedImageModeling.from_config(a_					)

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

					if data_args.image_column_name is not None:
										__UpperCamelCase      :					Optional[Any]       		=data_args.image_column_name
					elif "image" in column_names:
										__UpperCamelCase      :					List[Any]       		='image'
					elif "img" in column_names:
										__UpperCamelCase      :					Optional[int]       		='img'
					else:
										__UpperCamelCase      :					List[Any]       		=column_names[0]

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

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

					def preprocess_images(a_					):
										__UpperCamelCase      :					Dict       		=[transforms(a_					) for image in examples[image_column_name]]
										__UpperCamelCase      :					int       		=[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:
															__UpperCamelCase      :					Optional[int]       		=ds['train'].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
										# Set the training transforms
										ds["train"].set_transform(a_					)

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

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

					# Training
					if training_args.do_train:
										__UpperCamelCase      :					Optional[Any]       		=None
										if training_args.resume_from_checkpoint is not None:
															__UpperCamelCase      :					List[str]       		=training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															__UpperCamelCase      :					List[Any]       		=last_checkpoint
										__UpperCamelCase      :					List[str]       		=trainer.train(resume_from_checkpoint=a_					)
										trainer.save_model()
										trainer.log_metrics('train'							,train_result.metrics					)
										trainer.save_metrics('train'							,train_result.metrics					)
										trainer.save_state()

					# Evaluation
					if training_args.do_eval:
										__UpperCamelCase      :					Optional[int]       		=trainer.evaluate()
										trainer.log_metrics('eval'							,a_					)
										trainer.save_metrics('eval'							,a_					)

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


if __name__ == "__main__":
							main()








"""simple docstring"""
import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:






		class 					_UpperCAmelCase   :


							@staticmethod
							def 	lowerCamelCase       (					*__UpperCamelCase			:List[Any]     ,       **__UpperCamelCase			:List[Any]		):
														pass






def 					A__				(			UpperCamelCase       ):
							A		=  hashlib.mda(image.tobytes()       )
							return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					_UpperCAmelCase   (	unittest.TestCase ):
					UpperCamelCase      =							MODEL_FOR_DEPTH_ESTIMATION_MAPPING


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[int]		):
												A		=  DepthEstimationPipeline(model=__UpperCamelCase     ,       image_processor=__UpperCamelCase		)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
												self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}     ,       __UpperCamelCase		)
												import datasets

												A		=  datasets.load_dataset("hf-internal-testing/fixtures_image_utils"     ,       "image"     ,       split="test"		)
												A		=  depth_estimator(
												    [
												        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
												        "http://images.cocodataset.org/val2017/000000039769.jpg",
												        # RGBA
												        dataset[0]["file"],
												        # LA
												        dataset[1]["file"],
												        # L
												        dataset[2]["file"],
												    ]		)
												self.assertEqual(
												    [
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
												    ]     ,       __UpperCamelCase     ,       )


					@require_tf
					@unittest.skip("Depth estimation is not implemented in TF"		)
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												pass


					@slow
					@require_torch
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  "Intel/dpt-large"
												A		=  pipeline("depth-estimation"     ,       model=__UpperCamelCase		)
												A		=  depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
												A		=  hashimage(outputs["depth"]		)

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)     ,       29.304		)
												self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)     ,       2.662		)


					@require_torch
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												# This is highly irregular to have no small tests.
												self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)


