



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       :					Tuple      					=					logging.get_logger(__name__)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : Optional[int]						=			DPTConfig()

						if "large" in checkpoint_url:
												snake_case_   : Tuple						=			1_0_2_4
												snake_case_   : Optional[Any]						=			4_0_9_6
												snake_case_   : Dict						=			2_4
												snake_case_   : Any						=			1_6
												snake_case_   : Tuple						=			[5, 1_1, 1_7, 2_3]
												snake_case_   : str						=			[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
												snake_case_   : int						=			(1, 3_8_4, 3_8_4)

						if "ade" in checkpoint_url:
												snake_case_   : str						=			True

												snake_case_   : Optional[int]						=			1_5_0
												snake_case_   : Optional[Any]						=			'huggingface/label-files'
												snake_case_   : Any						=			'ade20k-id2label.json'
												snake_case_   : List[Any]						=			json.load(open(cached_download(hf_hub_url(_lowerCamelCase					, _lowerCamelCase					, repo_type="""dataset"""    )    )					, """r"""    )    )
												snake_case_   : List[Any]						=			{int(_lowerCamelCase    ): v for k, v in idalabel.items()}
												snake_case_   : int						=			idalabel
												snake_case_   : Optional[Any]						=			{v: k for k, v in idalabel.items()}
												snake_case_   : str						=			[1, 1_5_0, 4_8_0, 4_8_0]

						return config, expected_shape
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Optional[int]						=			['pretrained.model.head.weight', 'pretrained.model.head.bias']
						for k in ignore_keys:
												state_dict.pop(_lowerCamelCase					, _lowerCamelCase    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


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

						return name
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Optional[int]    ):


						'''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)
												snake_case_   : List[str]						=			state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'    )
												snake_case_   : Tuple						=			state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias'    )
												# next, add query, keys and values (in that order) to the state dict
												snake_case_   : Optional[int]						=			in_proj_weight[: config.hidden_size, :]
												snake_case_   : int						=			in_proj_bias[: config.hidden_size]
												snake_case_   : List[str]						=			in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												snake_case_   : str						=			in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												snake_case_   : Dict						=			in_proj_weight[
												    -config.hidden_size :, :
												]
												snake_case_   : int						=			in_proj_bias[-config.hidden_size :]
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : int						=			'http://images.cocodataset.org/val2017/000000039769.jpg'
						snake_case_   : List[str]						=			Image.open(requests.get(_lowerCamelCase					, stream=_lowerCamelCase    ).raw    )
						return im
@torch.no_grad()
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			Tuple					, __UpperCamelCase      :			str					, __UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : Dict						=			get_dpt_config(_lowerCamelCase    )
						# load original state_dict from URL
						snake_case_   : Tuple						=			torch.hub.load_state_dict_from_url(_lowerCamelCase					, map_location="""cpu"""    )
						# remove certain keys
						remove_ignore_keys_(_lowerCamelCase    )
						# rename keys
						for key in state_dict.copy().keys():
												snake_case_   : Dict						=			state_dict.pop(_lowerCamelCase    )
												snake_case_   : Optional[int]						=			val
						# read in qkv matrices
						read_in_q_k_v(_lowerCamelCase					, _lowerCamelCase    )

						# load HuggingFace model
						snake_case_   : Union[str, Any]						=			DPTForSemanticSegmentation(_lowerCamelCase    ) if 'ade' in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase    )
						model.load_state_dict(_lowerCamelCase    )
						model.eval()

						# Check outputs on an image
						snake_case_   : Union[str, Any]						=			4_8_0 if 'ade' in checkpoint_url else 3_8_4
						snake_case_   : str						=			DPTImageProcessor(size=_lowerCamelCase    )

						snake_case_   : Any						=			prepare_img()
						snake_case_   : Tuple						=			image_processor(_lowerCamelCase					, return_tensors="""pt"""    )

						# forward pass
						snake_case_   : Union[str, Any]						=			model(**_lowerCamelCase    ).logits if 'ade' in checkpoint_url else model(**_lowerCamelCase    ).predicted_depth

						# Assert logits
						snake_case_   : Union[str, Any]						=			torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]    )
						if "ade" in checkpoint_url:
												snake_case_   : int						=			torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]    )
						assert outputs.shape == torch.Size(_lowerCamelCase    )
						assert (
						    torch.allclose(outputs[0, 0, :3, :3]					, _lowerCamelCase					, atol=1E-4    )
						    if "ade" in checkpoint_url
						    else torch.allclose(outputs[0, :3, :3]					, _lowerCamelCase    )
						)

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

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


if __name__ == "__main__":
					__lowerCAmelCase       :					str      					=					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       :					Any      					=					parser.parse_args()
					convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






"""simple docstring"""





import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


__lowerCAmelCase       :					Optional[int]      					=					logging.get_logger(__name__)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			nn.functional.normalize(__UpperCamelCase    )
						snake_case_   : Tuple						=			nn.functional.normalize(__UpperCamelCase    )
						return torch.mm(__UpperCamelCase					, normalized_text_embeds.t()    )






class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       CLIPConfig

		_lowerCamelCase			  =       ['''CLIPEncoderLayer''']





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







								'''simple docstring'''

								super().__init__(_lowercase   )

								snake_case_   : Tuple						=			CLIPVisionModel(config.vision_config   )
								snake_case_   : int						=			nn.Linear(config.vision_config.hidden_size							,    config.projection_dim							,    bias=_lowercase   )

								snake_case_   : Optional[Any]						=			nn.Parameter(torch.ones(1_7							,    config.projection_dim   )							,    requires_grad=_lowercase   )
								snake_case_   : Dict						=			nn.Parameter(torch.ones(3							,    config.projection_dim   )							,    requires_grad=_lowercase   )

								snake_case_   : Any						=			nn.Parameter(torch.ones(1_7   )							,    requires_grad=_lowercase   )
								snake_case_   : List[str]						=			nn.Parameter(torch.ones(3   )							,    requires_grad=_lowercase   )





		@torch.no_grad()
		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase   ) ->						Any:







								'''simple docstring'''

								snake_case_   : int						=			self.vision_model(_lowercase   )[1]  # pooled_output
								snake_case_   : str						=			self.visual_projection(_lowercase   )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								snake_case_   : Dict						=			cosine_distance(_lowercase							,    self.special_care_embeds   ).cpu().float().numpy()
								snake_case_   : List[str]						=			cosine_distance(_lowercase							,    self.concept_embeds   ).cpu().float().numpy()

								snake_case_   : Any						=			[]
								snake_case_   : Any						=			image_embeds.shape[0]
								for i in range(_lowercase   ):
														snake_case_   : List[Any]						=			{"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}

														# increase this value to create a stronger `nfsw` filter
														# at the cost of increasing the possibility of filtering benign images
														snake_case_   : int						=			0.0

														for concept_idx in range(len(special_cos_dist[0]   )   ):
																				snake_case_   : List[str]						=			special_cos_dist[i][concept_idx]
																				snake_case_   : Union[str, Any]						=			self.special_care_embeds_weights[concept_idx].item()
																				snake_case_   : Tuple						=			round(concept_cos - concept_threshold + adjustment							,    3   )
																				if result_img["special_scores"][concept_idx] > 0:
																										result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]}   )
																										snake_case_   : Dict						=			0.01

														for concept_idx in range(len(cos_dist[0]   )   ):
																				snake_case_   : int						=			cos_dist[i][concept_idx]
																				snake_case_   : List[Any]						=			self.concept_embeds_weights[concept_idx].item()
																				snake_case_   : List[str]						=			round(concept_cos - concept_threshold + adjustment							,    3   )
																				if result_img["concept_scores"][concept_idx] > 0:
																										result_img["bad_concepts"].append(_lowercase   )

														result.append(_lowercase   )

								snake_case_   : Union[str, Any]						=			[len(res["""bad_concepts"""]   ) > 0 for res in result]

								return images, has_nsfw_concepts





		@torch.no_grad()
		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			self.vision_model(_lowercase   )[1]  # pooled_output
								snake_case_   : List[str]						=			self.visual_projection(_lowercase   )

								snake_case_   : str						=			cosine_distance(_lowercase							,    self.special_care_embeds   )
								snake_case_   : Optional[int]						=			cosine_distance(_lowercase							,    self.concept_embeds   )

								# increase this value to create a stronger `nsfw` filter
								# at the cost of increasing the possibility of filtering benign images
								snake_case_   : Tuple						=			0.0

								snake_case_   : List[Any]						=			special_cos_dist - self.special_care_embeds_weights + adjustment
								# special_scores = special_scores.round(decimals=3)
								snake_case_   : str						=			torch.any(special_scores > 0							,    dim=1   )
								snake_case_   : List[str]						=			special_care * 0.01
								snake_case_   : Optional[int]						=			special_adjustment.unsqueeze(1   ).expand(-1							,    cos_dist.shape[1]   )

								snake_case_   : Optional[Any]						=			(cos_dist - self.concept_embeds_weights) + special_adjustment
								# concept_scores = concept_scores.round(decimals=3)
								snake_case_   : str						=			torch.any(concept_scores > 0							,    dim=1   )

								return images, has_nsfw_concepts

