






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()
A__      :     Union[str, Any]       =					logging.get_logger(__name__)






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     DPTConfig()

				if "large" in checkpoint_url:
								lowercase__  					=     1024
								lowercase__  					=     4096
								lowercase__  					=     24
								lowercase__  					=     16
								lowercase__  					=     [5, 11, 17, 23]
								lowercase__  					=     [256, 512, 1024, 1024]
								lowercase__  					=     (1, 384, 384)

				if "ade" in checkpoint_url:
								lowercase__  					=     True

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

				return config, expected_shape






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase_						,  lowerCamelCase_							)






def     a     (		lowerCamelCase_							):






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

				return name






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''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)
								lowercase__  					=     state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""							)
								lowercase__  					=     state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias"""							)
								# next, add query, keys and values (in that order) to the state dict
								lowercase__  					=     in_proj_weight[: config.hidden_size, :]
								lowercase__  					=     in_proj_bias[: config.hidden_size]
								lowercase__  					=     in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowercase__  					=     in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								lowercase__  					=     in_proj_weight[
								    -config.hidden_size :, :
								]
								lowercase__  					=     in_proj_bias[-config.hidden_size :]






def     a     (		):






				'''simple docstring'''
				lowercase__  					=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowercase__  					=     Image.open(requests.get(lowerCamelCase_						,  stream=lowerCamelCase_							).raw							)
				return im






@torch.no_grad()
def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__					,		lowercase__  					=     get_dpt_config(lowerCamelCase_							)
				# load original state_dict from URL
				lowercase__  					=     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():
								lowercase__  					=     state_dict.pop(lowerCamelCase_							)
								lowercase__  					=     val
				# read in qkv matrices
				read_in_q_k_v(lowerCamelCase_						,  lowerCamelCase_							)

				# load HuggingFace model
				lowercase__  					=     DPTForSemanticSegmentation(lowerCamelCase_							) if '''ade''' in checkpoint_url else DPTForDepthEstimation(lowerCamelCase_							)
				model.load_state_dict(lowerCamelCase_							)
				model.eval()

				# Check outputs on an image
				lowercase__  					=     480 if '''ade''' in checkpoint_url else 384
				lowercase__  					=     DPTImageProcessor(size=lowerCamelCase_							)

				lowercase__  					=     prepare_img()
				lowercase__  					=     image_processor(lowerCamelCase_						,  return_tensors='''pt'''							)

				# forward pass
				lowercase__  					=     model(**lowerCamelCase_							).logits if '''ade''' in checkpoint_url else model(**lowerCamelCase_							).predicted_depth

				# Assert logits
				lowercase__  					=     torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]							)
				if "ade" in checkpoint_url:
								lowercase__  					=     torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]							)
				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__":
							A__      :     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.',
							)

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__      :     Optional[int]       =					{
    'configuration_clap': [
        'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ClapAudioConfig',
        'ClapConfig',
        'ClapTextConfig',
    ],
    'processing_clap': ['ClapProcessor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__      :     str       =					[
							    'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ClapModel',
							    'ClapPreTrainedModel',
							    'ClapTextModel',
							    'ClapTextModelWithProjection',
							    'ClapAudioModel',
							    'ClapAudioModelWithProjection',
							]
							A__      :     List[Any]       =					['ClapFeatureExtractor']

if TYPE_CHECKING:
							from .configuration_clap import (
							    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
							    ClapAudioConfig,
							    ClapConfig,
							    ClapTextConfig,
							)
							from .processing_clap import ClapProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clap import ClapFeatureExtractor
														from .modeling_clap import (
														    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ClapAudioModel,
														    ClapAudioModelWithProjection,
														    ClapModel,
														    ClapPreTrainedModel,
														    ClapTextModel,
														    ClapTextModelWithProjection,
														)


else:
							import sys

							A__      :     int       =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




