


'''simple docstring'''





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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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-classification/requirements.txt")

__snake_case            =   list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__snake_case            =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			with open(SCREAMING_SNAKE_CASE_       ,  """rb"""	) as f:
						UpperCamelCase         =					Image.open(SCREAMING_SNAKE_CASE_	)
						return im.convert("""RGB"""	)




@dataclass
class    UpperCAmelCase					:
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
			    }      , )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
			lowercase						= field(default=__snake_case      , metadata={"""help""": """A folder containing the training data."""}    )
			lowercase						= field(default=__snake_case      , metadata={"""help""": """A folder containing the validation data."""}    )
			lowercase						= field(
			    default=0.15      , metadata={"""help""": """Percent to split off of train for validation."""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of training examples to this """
			            """value if set."""
			        )
			    }      , )
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
			            """value if set."""
			        )
			    }      , )




			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
									raise ValueError(
									    """You must specify either a dataset name from the hub or a train and/or validation directory."""						)




@dataclass
class    UpperCAmelCase					:
			lowercase						= field(
			    default="""google/vit-base-patch16-224-in21k"""      , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}      , )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__snake_case    )}      , )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}    )
			lowercase						= field(
			    default="""main"""      , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}      , )
			lowercase						= field(default=__snake_case      , metadata={"""help""": """Name or path of preprocessor config."""}    )
			lowercase						= field(
			    default=__snake_case      , metadata={
			        """help""": (
			            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
			            """with private models)."""
			        )
			    }      , )
			lowercase						= field(
			    default=__snake_case      , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}      , )






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any	):





			"""simple docstring"""




			UpperCamelCase         =					torch.stack([example["""pixel_values"""] for example in examples]	)
			UpperCamelCase         =					torch.tensor([example["""labels"""] for example in examples]	)
			return {"pixel_values": pixel_values, "labels": labels}






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					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         =					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_image_classification"""       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# 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(SCREAMING_SNAKE_CASE_	)
			transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE_	)
			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."""	)

    # Set seed before initializing model.
			set_seed(training_args.seed	)

			# Initialize our dataset and prepare it for the 'image-classification' task.
			if data_args.dataset_name is not None:
						UpperCamelCase         =					load_dataset(
						    data_args.dataset_name       ,  data_args.dataset_config_name       ,  cache_dir=model_args.cache_dir       ,  task="""image-classification"""       ,  use_auth_token=True if model_args.use_auth_token else None       ,  )
			else:
						UpperCamelCase         =					{}
						if data_args.train_dir is not None:
									UpperCamelCase         =					os.path.join(data_args.train_dir       ,  """**"""	)
						if data_args.validation_dir is not None:
									UpperCamelCase         =					os.path.join(data_args.validation_dir       ,  """**"""	)
						UpperCamelCase         =					load_dataset(
						    """imagefolder"""       ,  data_files=SCREAMING_SNAKE_CASE_       ,  cache_dir=model_args.cache_dir       ,  task="""image-classification"""       ,  )

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

			# Prepare label mappings.
			# We'll include these in the model's config to get human readable labels in the Inference API.
			UpperCamelCase         =					dataset["""train"""].features["""labels"""].names
			UpperCamelCase  ,  UpperCamelCase         =					{}, {}
			for i, label in enumerate(SCREAMING_SNAKE_CASE_	):
						UpperCamelCase         =					str(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					label

			# Load the accuracy metric from the datasets package
			UpperCamelCase         =					evaluate.load("""accuracy"""	)

			# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
			# predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(SCREAMING_SNAKE_CASE_   :		Tuple	):
						return metric.compute(predictions=np.argmax(p.predictions       ,  axis=1	)       ,  references=p.label_ids	)

			UpperCamelCase         =					AutoConfig.from_pretrained(
			    model_args.config_name or model_args.model_name_or_path       ,  num_labels=len(SCREAMING_SNAKE_CASE_	)       ,  labelaid=SCREAMING_SNAKE_CASE_       ,  idalabel=SCREAMING_SNAKE_CASE_       ,  finetuning_task="""image-classification"""       ,  cache_dir=model_args.cache_dir       ,  revision=model_args.model_revision       ,  use_auth_token=True if model_args.use_auth_token else None       ,  )
			UpperCamelCase         =					AutoModelForImageClassification.from_pretrained(
			    model_args.model_name_or_path       ,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path	)       ,  config=SCREAMING_SNAKE_CASE_       ,  cache_dir=model_args.cache_dir       ,  revision=model_args.model_revision       ,  use_auth_token=True if model_args.use_auth_token else None       ,  ignore_mismatched_sizes=model_args.ignore_mismatched_sizes       ,  )
			UpperCamelCase         =					AutoImageProcessor.from_pretrained(
			    model_args.image_processor_name or model_args.model_name_or_path       ,  cache_dir=model_args.cache_dir       ,  revision=model_args.model_revision       ,  use_auth_token=True if model_args.use_auth_token else None       ,  )

			# Define torchvision transforms to be applied to each image.
			if "shortest_edge" in image_processor.size:
						UpperCamelCase         =					image_processor.size["""shortest_edge"""]
			else:
						UpperCamelCase         =					(image_processor.size["""height"""], image_processor.size["""width"""])
			UpperCamelCase         =					Normalize(mean=image_processor.image_mean       ,  std=image_processor.image_std	)
			UpperCamelCase         =					Compose(
			    [
			        RandomResizedCrop(SCREAMING_SNAKE_CASE_	),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        normalize,
			    ]	)
			UpperCamelCase         =					Compose(
			    [
			        Resize(SCREAMING_SNAKE_CASE_	),
			        CenterCrop(SCREAMING_SNAKE_CASE_	),
			        ToTensor(),
			        normalize,
			    ]	)

			def train_transforms(SCREAMING_SNAKE_CASE_   :		Optional[Any]	):
						UpperCamelCase         =					[
						    _train_transforms(pil_img.convert("""RGB"""	)	) for pil_img in example_batch["""image"""]
						]
						return example_batch

			def val_transforms(SCREAMING_SNAKE_CASE_   :		Tuple	):
						UpperCamelCase         =					[_val_transforms(pil_img.convert("""RGB"""	)	) for pil_img in example_batch["""image"""]]
						return example_batch

			if training_args.do_train:
						if "train" not in dataset:
									raise ValueError("""--do_train requires a train dataset"""	)
						if data_args.max_train_samples is not None:
									UpperCamelCase         =					(
									    dataset["""train"""].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
									)
						# Set the training transforms
						dataset["train"].set_transform(SCREAMING_SNAKE_CASE_	)

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

			# Initalize our trainer
			UpperCamelCase         =					Trainer(
			    model=SCREAMING_SNAKE_CASE_       ,  args=SCREAMING_SNAKE_CASE_       ,  train_dataset=dataset["""train"""] if training_args.do_train else None       ,  eval_dataset=dataset["""validation"""] if training_args.do_eval else None       ,  compute_metrics=SCREAMING_SNAKE_CASE_       ,  tokenizer=SCREAMING_SNAKE_CASE_       ,  data_collator=SCREAMING_SNAKE_CASE_       ,  )

			# 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=SCREAMING_SNAKE_CASE_	)
						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"""       ,  SCREAMING_SNAKE_CASE_	)
						trainer.save_metrics("""eval"""       ,  SCREAMING_SNAKE_CASE_	)

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


if __name__ == "__main__":
			main()








import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case            =   logging.get_logger(__name__)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			UpperCamelCase         =					DPTConfig()

			if "large" in checkpoint_url:
						UpperCamelCase         =					1_024
						UpperCamelCase         =					4_096
						UpperCamelCase         =					24
						UpperCamelCase         =					16
						UpperCamelCase         =					[5, 11, 17, 23]
						UpperCamelCase         =					[256, 512, 1_024, 1_024]
						UpperCamelCase         =					(1, 384, 384)

			if "ade" in checkpoint_url:
						UpperCamelCase         =					True

						UpperCamelCase         =					150
						UpperCamelCase         =					"""huggingface/label-files"""
						UpperCamelCase         =					"""ade20k-id2label.json"""
						UpperCamelCase         =					json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  repo_type="""dataset"""	)	)       ,  """r"""	)	)
						UpperCamelCase         =					{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
						UpperCamelCase         =					idalabel
						UpperCamelCase         =					{v: k for k, v in idalabel.items()}
						UpperCamelCase         =					[1, 150, 480, 480]

			return config, expected_shape






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):





			"""simple docstring"""




			UpperCamelCase         =					["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			if (
			    "pretrained.model" in name
			    and "cls_token" not in name
			    and "pos_embed" not in name
			    and "patch_embed" not in name
			):
						UpperCamelCase         =					name.replace("""pretrained.model"""       ,  """dpt.encoder"""	)
			if "pretrained.model" in name:
						UpperCamelCase         =					name.replace("""pretrained.model"""       ,  """dpt.embeddings"""	)
			if "patch_embed" in name:
						UpperCamelCase         =					name.replace("""patch_embed"""       ,  """patch_embeddings"""	)
			if "pos_embed" in name:
						UpperCamelCase         =					name.replace("""pos_embed"""       ,  """position_embeddings"""	)
			if "attn.proj" in name:
						UpperCamelCase         =					name.replace("""attn.proj"""       ,  """attention.output.dense"""	)
			if "proj" in name and "project" not in name:
						UpperCamelCase         =					name.replace("""proj"""       ,  """projection"""	)
			if "blocks" in name:
						UpperCamelCase         =					name.replace("""blocks"""       ,  """layer"""	)
			if "mlp.fc1" in name:
						UpperCamelCase         =					name.replace("""mlp.fc1"""       ,  """intermediate.dense"""	)
			if "mlp.fc2" in name:
						UpperCamelCase         =					name.replace("""mlp.fc2"""       ,  """output.dense"""	)
			if "norm1" in name:
						UpperCamelCase         =					name.replace("""norm1"""       ,  """layernorm_before"""	)
			if "norm2" in name:
						UpperCamelCase         =					name.replace("""norm2"""       ,  """layernorm_after"""	)
			if "scratch.output_conv" in name:
						UpperCamelCase         =					name.replace("""scratch.output_conv"""       ,  """head"""	)
			if "scratch" in name:
						UpperCamelCase         =					name.replace("""scratch"""       ,  """neck"""	)
			if "layer1_rn" in name:
						UpperCamelCase         =					name.replace("""layer1_rn"""       ,  """convs.0"""	)
			if "layer2_rn" in name:
						UpperCamelCase         =					name.replace("""layer2_rn"""       ,  """convs.1"""	)
			if "layer3_rn" in name:
						UpperCamelCase         =					name.replace("""layer3_rn"""       ,  """convs.2"""	)
			if "layer4_rn" in name:
						UpperCamelCase         =					name.replace("""layer4_rn"""       ,  """convs.3"""	)
			if "refinenet" in name:
						UpperCamelCase         =					int(name[len("""neck.refinenet"""	) : len("""neck.refinenet"""	) + 1]	)
						# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
						UpperCamelCase         =					name.replace(f'refinenet{layer_idx}'       ,  f'fusion_stage.layers.{abs(layer_idx-4	)}'	)
			if "out_conv" in name:
						UpperCamelCase         =					name.replace("""out_conv"""       ,  """projection"""	)
			if "resConfUnit1" in name:
						UpperCamelCase         =					name.replace("""resConfUnit1"""       ,  """residual_layer1"""	)
			if "resConfUnit2" in name:
						UpperCamelCase         =					name.replace("""resConfUnit2"""       ,  """residual_layer2"""	)
			if "conv1" in name:
						UpperCamelCase         =					name.replace("""conv1"""       ,  """convolution1"""	)
			if "conv2" in name:
						UpperCamelCase         =					name.replace("""conv2"""       ,  """convolution2"""	)
			# readout blocks
			if "pretrained.act_postprocess1.0.project.0" in name:
						UpperCamelCase         =					name.replace("""pretrained.act_postprocess1.0.project.0"""       ,  """neck.reassemble_stage.readout_projects.0.0"""	)
			if "pretrained.act_postprocess2.0.project.0" in name:
						UpperCamelCase         =					name.replace("""pretrained.act_postprocess2.0.project.0"""       ,  """neck.reassemble_stage.readout_projects.1.0"""	)
			if "pretrained.act_postprocess3.0.project.0" in name:
						UpperCamelCase         =					name.replace("""pretrained.act_postprocess3.0.project.0"""       ,  """neck.reassemble_stage.readout_projects.2.0"""	)
			if "pretrained.act_postprocess4.0.project.0" in name:
						UpperCamelCase         =					name.replace("""pretrained.act_postprocess4.0.project.0"""       ,  """neck.reassemble_stage.readout_projects.3.0"""	)
			# resize blocks
			if "pretrained.act_postprocess1.3" in name:
						UpperCamelCase         =					name.replace("""pretrained.act_postprocess1.3"""       ,  """neck.reassemble_stage.layers.0.projection"""	)
			if "pretrained.act_postprocess1.4" in name:
						UpperCamelCase         =					name.replace("""pretrained.act_postprocess1.4"""       ,  """neck.reassemble_stage.layers.0.resize"""	)
			if "pretrained.act_postprocess2.3" in name:
						UpperCamelCase         =					name.replace("""pretrained.act_postprocess2.3"""       ,  """neck.reassemble_stage.layers.1.projection"""	)
			if "pretrained.act_postprocess2.4" in name:
						UpperCamelCase         =					name.replace("""pretrained.act_postprocess2.4"""       ,  """neck.reassemble_stage.layers.1.resize"""	)
			if "pretrained.act_postprocess3.3" in name:
						UpperCamelCase         =					name.replace("""pretrained.act_postprocess3.3"""       ,  """neck.reassemble_stage.layers.2.projection"""	)
			if "pretrained.act_postprocess4.3" in name:
						UpperCamelCase         =					name.replace("""pretrained.act_postprocess4.3"""       ,  """neck.reassemble_stage.layers.3.projection"""	)
			if "pretrained.act_postprocess4.4" in name:
						UpperCamelCase         =					name.replace("""pretrained.act_postprocess4.4"""       ,  """neck.reassemble_stage.layers.3.resize"""	)
			if "pretrained" in name:
						UpperCamelCase         =					name.replace("""pretrained"""       ,  """dpt"""	)
			if "bn" in name:
						UpperCamelCase         =					name.replace("""bn"""       ,  """batch_norm"""	)
			if "head" in name:
						UpperCamelCase         =					name.replace("""head"""       ,  """head.head"""	)
			if "encoder.norm" in name:
						UpperCamelCase         =					name.replace("""encoder.norm"""       ,  """layernorm"""	)
			if "auxlayer" in name:
						UpperCamelCase         =					name.replace("""auxlayer"""       ,  """auxiliary_head.head"""	)

			return name






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Tuple	):





			"""simple docstring"""




			for i in range(config.num_hidden_layers	):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						UpperCamelCase         =					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'	)
						UpperCamelCase         =					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'	)
						# next, add query, keys and values (in that order) to the state dict
						UpperCamelCase         =					in_proj_weight[: config.hidden_size, :]
						UpperCamelCase         =					in_proj_bias[: config.hidden_size]
						UpperCamelCase         =					in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCamelCase         =					in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCamelCase         =					in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCamelCase         =					in_proj_bias[-config.hidden_size :]






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCamelCase         =					Image.open(requests.get(SCREAMING_SNAKE_CASE_       ,  stream=SCREAMING_SNAKE_CASE_	).raw	)
			return im






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




			UpperCamelCase  ,  UpperCamelCase         =					get_dpt_config(SCREAMING_SNAKE_CASE_	)
			# load original state_dict from URL
			UpperCamelCase         =					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_       ,  map_location="""cpu"""	)
			# remove certain keys
			remove_ignore_keys_(SCREAMING_SNAKE_CASE_	)
			# rename keys
			for key in state_dict.copy().keys():
						UpperCamelCase         =					state_dict.pop(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					val
			# read in qkv matrices
			read_in_q_k_v(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# load HuggingFace model
			UpperCamelCase         =					DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE_	) if """ade""" in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE_	)
			model.load_state_dict(SCREAMING_SNAKE_CASE_	)
			model.eval()

			# Check outputs on an image
			UpperCamelCase         =					480 if """ade""" in checkpoint_url else 384
			UpperCamelCase         =					DPTImageProcessor(size=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					prepare_img()
			UpperCamelCase         =					image_processor(SCREAMING_SNAKE_CASE_       ,  return_tensors="""pt"""	)

			# forward pass
			UpperCamelCase         =					model(**SCREAMING_SNAKE_CASE_	).logits if """ade""" in checkpoint_url else model(**SCREAMING_SNAKE_CASE_	).predicted_depth

			# Assert logits
			UpperCamelCase         =					torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]	)
			if "ade" in checkpoint_url:
						UpperCamelCase         =					torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]	)
			assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE_	)
			assert (
			    torch.allclose(outputs[0, 0, :3, :3]       ,  SCREAMING_SNAKE_CASE_       ,  atol=1e-4	)
			    if "ade" in checkpoint_url
			    else torch.allclose(outputs[0, :3, :3]       ,  SCREAMING_SNAKE_CASE_	)
			)

			Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
			print(f'Saving model to {pytorch_dump_folder_path}'	)
			model.save_pretrained(SCREAMING_SNAKE_CASE_	)
			print(f'Saving image processor to {pytorch_dump_folder_path}'	)
			image_processor.save_pretrained(SCREAMING_SNAKE_CASE_	)

			if push_to_hub:
						print("""Pushing model to hub..."""	)
						model.push_to_hub(
						    repo_path_or_name=Path(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)       ,  organization="""nielsr"""       ,  commit_message="""Add model"""       ,  use_temp_dir=SCREAMING_SNAKE_CASE_       ,  )
						image_processor.push_to_hub(
						    repo_path_or_name=Path(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)       ,  organization="""nielsr"""       ,  commit_message="""Add image processor"""       ,  use_temp_dir=SCREAMING_SNAKE_CASE_       ,  )


if __name__ == "__main__":
			__snake_case            =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--checkpoint_url",
			    default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
			    type=str,
			    help="URL of the original DPT checkpoint you'd like to convert.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path to the output PyTorch model directory.",
			)
			parser.add_argument(
			    "--push_to_hub",
			    action="store_true",
			)
			parser.add_argument(
			    "--model_name",
			    default="dpt-large",
			    type=str,
			    help="Name of the model, in case you're pushing to the hub.",
			)

			__snake_case            =   parser.parse_args()
			convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




