




'''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()
_lowercase  =			logging.get_logger(__name__)
def 						lowerCamelCase__   (				a							):
						__snake_case						 =   DPTConfig(embedding_type='hybrid'							)

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

						if "nyu" or "midas" in checkpoint_url:
												__snake_case						 =   768
												__snake_case						 =   [1, 1, 1, 0.5]
												__snake_case						 =   [256, 512, 768, 768]
												__snake_case						 =   150
												__snake_case						 =   16
												__snake_case						 =   (1, 384, 384)
												__snake_case						 =   False
												__snake_case						 =   'project'

						if "ade" in checkpoint_url:
												__snake_case						 =   True
												__snake_case						 =   768
												__snake_case						 =   [1, 1, 1, 0.5]
												__snake_case						 =   150
												__snake_case						 =   16
												__snake_case						 =   'huggingface/label-files'
												__snake_case						 =   'ade20k-id2label.json'
												__snake_case						 =   json.load(open(cached_download(hf_hub_url(a    ,	a    ,	repo_type='dataset'							)							)    ,	'r'							)							)
												__snake_case						 =   {int(a							): v for k, v in idalabel.items()}
												__snake_case						 =   idalabel
												__snake_case						 =   {v: k for k, v in idalabel.items()}
												__snake_case						 =   [1, 150, 480, 480]

						return config, expected_shape
def 						lowerCamelCase__   (				a							):
						__snake_case						 =   ['pretrained.model.head.weight', 'pretrained.model.head.bias']
						for k in ignore_keys:
												state_dict.pop(a    ,	a							)
def 						lowerCamelCase__   (				a							):
						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						 =   name.replace('pretrained.model'    ,	'dpt.encoder'							)
						if "pretrained.model" in name:
												__snake_case						 =   name.replace('pretrained.model'    ,	'dpt.embeddings'							)
						if "patch_embed" in name:
												__snake_case						 =   name.replace('patch_embed'    ,	''							)
						if "pos_embed" in name:
												__snake_case						 =   name.replace('pos_embed'    ,	'position_embeddings'							)
						if "attn.proj" in name:
												__snake_case						 =   name.replace('attn.proj'    ,	'attention.output.dense'							)
						if "proj" in name and "project" not in name:
												__snake_case						 =   name.replace('proj'    ,	'projection'							)
						if "blocks" in name:
												__snake_case						 =   name.replace('blocks'    ,	'layer'							)
						if "mlp.fc1" in name:
												__snake_case						 =   name.replace('mlp.fc1'    ,	'intermediate.dense'							)
						if "mlp.fc2" in name:
												__snake_case						 =   name.replace('mlp.fc2'    ,	'output.dense'							)
						if "norm1" in name and "backbone" not in name:
												__snake_case						 =   name.replace('norm1'    ,	'layernorm_before'							)
						if "norm2" in name and "backbone" not in name:
												__snake_case						 =   name.replace('norm2'    ,	'layernorm_after'							)
						if "scratch.output_conv" in name:
												__snake_case						 =   name.replace('scratch.output_conv'    ,	'head'							)
						if "scratch" in name:
												__snake_case						 =   name.replace('scratch'    ,	'neck'							)
						if "layer1_rn" in name:
												__snake_case						 =   name.replace('layer1_rn'    ,	'convs.0'							)
						if "layer2_rn" in name:
												__snake_case						 =   name.replace('layer2_rn'    ,	'convs.1'							)
						if "layer3_rn" in name:
												__snake_case						 =   name.replace('layer3_rn'    ,	'convs.2'							)
						if "layer4_rn" in name:
												__snake_case						 =   name.replace('layer4_rn'    ,	'convs.3'							)
						if "refinenet" in name:
												__snake_case						 =   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						 =   name.replace(f'refinenet{layer_idx}'    ,	f'fusion_stage.layers.{abs(layer_idx-4							)}'							)
						if "out_conv" in name:
												__snake_case						 =   name.replace('out_conv'    ,	'projection'							)
						if "resConfUnit1" in name:
												__snake_case						 =   name.replace('resConfUnit1'    ,	'residual_layer1'							)
						if "resConfUnit2" in name:
												__snake_case						 =   name.replace('resConfUnit2'    ,	'residual_layer2'							)
						if "conv1" in name:
												__snake_case						 =   name.replace('conv1'    ,	'convolution1'							)
						if "conv2" in name:
												__snake_case						 =   name.replace('conv2'    ,	'convolution2'							)
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												__snake_case						 =   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						 =   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						 =   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						 =   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						 =   name.replace('pretrained.act_postprocess1.3'    ,	'neck.reassemble_stage.layers.0.projection'							)
						if "pretrained.act_postprocess1.4" in name:
												__snake_case						 =   name.replace('pretrained.act_postprocess1.4'    ,	'neck.reassemble_stage.layers.0.resize'							)
						if "pretrained.act_postprocess2.3" in name:
												__snake_case						 =   name.replace('pretrained.act_postprocess2.3'    ,	'neck.reassemble_stage.layers.1.projection'							)
						if "pretrained.act_postprocess2.4" in name:
												__snake_case						 =   name.replace('pretrained.act_postprocess2.4'    ,	'neck.reassemble_stage.layers.1.resize'							)
						if "pretrained.act_postprocess3.3" in name:
												__snake_case						 =   name.replace('pretrained.act_postprocess3.3'    ,	'neck.reassemble_stage.layers.2.projection'							)
						if "pretrained.act_postprocess4.3" in name:
												__snake_case						 =   name.replace('pretrained.act_postprocess4.3'    ,	'neck.reassemble_stage.layers.3.projection'							)
						if "pretrained.act_postprocess4.4" in name:
												__snake_case						 =   name.replace('pretrained.act_postprocess4.4'    ,	'neck.reassemble_stage.layers.3.resize'							)
						if "pretrained" in name:
												__snake_case						 =   name.replace('pretrained'    ,	'dpt'							)
						if "bn" in name:
												__snake_case						 =   name.replace('bn'    ,	'batch_norm'							)
						if "head" in name:
												__snake_case						 =   name.replace('head'    ,	'head.head'							)
						if "encoder.norm" in name:
												__snake_case						 =   name.replace('encoder.norm'    ,	'layernorm'							)
						if "auxlayer" in name:
												__snake_case						 =   name.replace('auxlayer'    ,	'auxiliary_head.head'							)
						if "backbone" in name:
												__snake_case						 =   name.replace('backbone'    ,	'backbone.bit.encoder'							)

						if ".." in name:
												__snake_case						 =   name.replace('..'    ,	'.'							)

						if "stem.conv" in name:
												__snake_case						 =   name.replace('stem.conv'    ,	'bit.embedder.convolution'							)
						if "blocks" in name:
												__snake_case						 =   name.replace('blocks'    ,	'layers'							)
						if "convolution" in name and "backbone" in name:
												__snake_case						 =   name.replace('convolution'    ,	'conv'							)
						if "layer" in name and "backbone" in name:
												__snake_case						 =   name.replace('layer'    ,	'layers'							)
						if "backbone.bit.encoder.bit" in name:
												__snake_case						 =   name.replace('backbone.bit.encoder.bit'    ,	'backbone.bit'							)
						if "embedder.conv" in name:
												__snake_case						 =   name.replace('embedder.conv'    ,	'embedder.convolution'							)
						if "backbone.bit.encoder.stem.norm" in name:
												__snake_case						 =   name.replace('backbone.bit.encoder.stem.norm'    ,	'backbone.bit.embedder.norm'							)
						return name
def 						lowerCamelCase__   (				a    ,	a							):
						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						 =   state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'							)
												__snake_case						 =   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						 =   in_proj_weight[: config.hidden_size, :]
												__snake_case						 =   in_proj_bias[: config.hidden_size]
												__snake_case						 =   in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												__snake_case						 =   in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												__snake_case						 =   in_proj_weight[
												    -config.hidden_size :, :
												]
												__snake_case						 =   in_proj_bias[-config.hidden_size :]
def 						lowerCamelCase__   (				):
						__snake_case						 =   'http://images.cocodataset.org/val2017/000000039769.jpg'
						__snake_case						 =   Image.open(requests.get(a    ,	stream=a							).raw							)
						return im
@torch.no_grad()
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a    ,	a							):
						__snake_case ,       __snake_case						 =   get_dpt_config(a							)
						# load original state_dict from URL
						# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
						__snake_case						 =   torch.load(a    ,	map_location='cpu'							)
						# remove certain keys
						remove_ignore_keys_(a							)
						# rename keys
						for key in state_dict.copy().keys():
												__snake_case						 =   state_dict.pop(a							)
												__snake_case						 =   val
						# read in qkv matrices
						read_in_q_k_v(a    ,	a							)

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

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

						__snake_case						 =   prepare_img()
						__snake_case						 =   image_processor(a    ,	return_tensors='pt'							)

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

						if show_prediction:
												__snake_case						 =   (
												    torch.nn.functional.interpolate(
												        outputs.unsqueeze(1							)    ,	size=(image.size[1], image.size[0])    ,	mode='bicubic'    ,	align_corners=a    ,	)
												    .squeeze()
												    .cpu()
												    .numpy()
												)

												Image.fromarray((prediction / prediction.max()) * 255							).show()

						if pytorch_dump_folder_path is not None:
												Path(a							).mkdir(exist_ok=a							)
												print(f'Saving model to {pytorch_dump_folder_path}'							)
												model.save_pretrained(a							)
												print(f'Saving image processor to {pytorch_dump_folder_path}'							)
												image_processor.save_pretrained(a							)

						if push_to_hub:
												model.push_to_hub('ybelkada/dpt-hybrid-midas'							)
												image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'							)


if __name__ == "__main__":
	_lowercase  =			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=False,
	    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.""",
	)
	parser.add_argument(
	    """--show_prediction""",
	    action="""store_true""",
	)

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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase  =			{
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GroupViTModel""",
	    """GroupViTPreTrainedModel""",
	    """GroupViTTextModel""",
	    """GroupViTVisionModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFGroupViTModel""",
	    """TFGroupViTPreTrainedModel""",
	    """TFGroupViTTextModel""",
	    """TFGroupViTVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_groupvit import (
	    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    GroupViTConfig,
	    GroupViTOnnxConfig,
	    GroupViTTextConfig,
	    GroupViTVisionConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_groupvit import (
		    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GroupViTModel,
		    GroupViTPreTrainedModel,
		    GroupViTTextModel,
		    GroupViTVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_groupvit import (
		    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFGroupViTModel,
		    TFGroupViTPreTrainedModel,
		    TFGroupViTTextModel,
		    TFGroupViTVisionModel,
		)

else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

