




"""simple docstring"""




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()
lowerCAmelCase							:    int  							=							logging.get_logger(__name__)



def 					a__				(       snake_case__       )		->      str:
			lowerCamelCase       =      DPTConfig()

			if "large" in checkpoint_url:
						lowerCamelCase       =      10_24
						lowerCamelCase       =      40_96
						lowerCamelCase       =      24
						lowerCamelCase       =      16
						lowerCamelCase       =      [5, 11, 17, 23]
						lowerCamelCase       =      [2_56, 5_12, 10_24, 10_24]
						lowerCamelCase       =      (1, 3_84, 3_84)

			if "ade" in checkpoint_url:
						lowerCamelCase       =      True

						lowerCamelCase       =      1_50
						lowerCamelCase       =      """huggingface/label-files"""
						lowerCamelCase       =      """ade20k-id2label.json"""
						lowerCamelCase       =      json.load(open(cached_download(hf_hub_url(snake_case__ ,				snake_case__ ,				repo_type="""dataset"""       )       ) ,				"""r"""       )       )
						lowerCamelCase       =      {int(snake_case__       ): v for k, v in idalabel.items()}
						lowerCamelCase       =      idalabel
						lowerCamelCase       =      {v: k for k, v in idalabel.items()}
						lowerCamelCase       =      [1, 1_50, 4_80, 4_80]

			return config, expected_shape



def 					a__				(       snake_case__       )		->      Any:
			lowerCamelCase       =      ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(snake_case__ ,				snake_case__       )



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

			return name



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



def 					a__				(       )		->      List[Any]:
			lowerCamelCase       =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
			lowerCamelCase       =      Image.open(requests.get(snake_case__ ,				stream=snake_case__       ).raw       )
			return im



@torch.no_grad()
def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      Any:
			lowerCamelCase   ,     lowerCamelCase       =      get_dpt_config(snake_case__       )
			# load original state_dict from URL
			lowerCamelCase       =      torch.hub.load_state_dict_from_url(snake_case__ ,				map_location="""cpu"""       )
			# remove certain keys
			remove_ignore_keys_(snake_case__       )
			# rename keys
			for key in state_dict.copy().keys():
						lowerCamelCase       =      state_dict.pop(snake_case__       )
						lowerCamelCase       =      val
			# read in qkv matrices
			read_in_q_k_v(snake_case__ ,				snake_case__       )

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

			# Check outputs on an image
			lowerCamelCase       =      4_80 if """ade""" in checkpoint_url else 3_84
			lowerCamelCase       =      DPTImageProcessor(size=snake_case__       )

			lowerCamelCase       =      prepare_img()
			lowerCamelCase       =      image_processor(snake_case__ ,				return_tensors="""pt"""       )

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

			# Assert logits
			lowerCamelCase       =      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:
						lowerCamelCase       =      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(snake_case__       )
			assert (
			    torch.allclose(outputs[0, 0, :3, :3] ,				snake_case__ ,				atol=1E-4       )
			    if "ade" in checkpoint_url
			    else torch.allclose(outputs[0, :3, :3] ,				snake_case__       )
			)

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

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


if __name__ == "__main__":
					lowerCAmelCase							:    Dict  							=							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.""",
					)

					lowerCAmelCase							:    List[Any]  							=							parser.parse_args()
					convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









"""simple docstring"""




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:    List[str]  							=							{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    Tuple  							=							[
					    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """AutoformerForPrediction""",
					    """AutoformerModel""",
					    """AutoformerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_autoformer import (
					    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    AutoformerConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_autoformer import (
										    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AutoformerForPrediction,
										    AutoformerModel,
										    AutoformerPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase							:    str  							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



