
'''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()
A			      =      logging.get_logger(__name__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[Any]):
				lowerCamelCase					:			Dict									= DPTConfig()

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

				if "ade" in checkpoint_url:
								lowerCamelCase					:			List[str]									= True

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

				return config, expected_shape





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Any									= ['pretrained.model.head.weight', 'pretrained.model.head.bias']
				for k in ignore_keys:
								state_dict.pop(UpperCAmelCase__       ,							UpperCAmelCase__)





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

				return name





def    UpperCAmelCase							(  UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							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					:			Any									= state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''')
								lowerCamelCase					:			Any									= 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					:			Tuple									= in_proj_weight[: config.hidden_size, :]
								lowerCamelCase					:			Any									= in_proj_bias[: config.hidden_size]
								lowerCamelCase					:			List[str]									= in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowerCamelCase					:			str									= in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								lowerCamelCase					:			Dict									= in_proj_weight[
								    -config.hidden_size :, :
								]
								lowerCamelCase					:			str									= in_proj_bias[-config.hidden_size :]





def    UpperCAmelCase							(  ):
				lowerCamelCase					:			int									= 'http://images.cocodataset.org/val2017/000000039769.jpg'
				lowerCamelCase					:			Optional[Any]									= Image.open(requests.get(UpperCAmelCase__       ,							stream=UpperCAmelCase__).raw)
				return im





@torch.no_grad()
def    UpperCAmelCase							(  UpperCAmelCase__							:							int       ,							UpperCAmelCase__							:							int       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							Tuple):
				lowerCamelCase					:			int									= get_dpt_config(UpperCAmelCase__)
				# load original state_dict from URL
				lowerCamelCase					:			List[Any]									= torch.hub.load_state_dict_from_url(UpperCAmelCase__       ,							map_location='cpu')
				# remove certain keys
				remove_ignore_keys_(UpperCAmelCase__)
				# rename keys
				for key in state_dict.copy().keys():
								lowerCamelCase					:			Optional[Any]									= state_dict.pop(UpperCAmelCase__)
								lowerCamelCase					:			List[Any]									= val
				# read in qkv matrices
				read_in_q_k_v(UpperCAmelCase__       ,							UpperCAmelCase__)

				# load HuggingFace model
				lowerCamelCase					:			List[str]									= DPTForSemanticSegmentation(UpperCAmelCase__) if 'ade' in checkpoint_url else DPTForDepthEstimation(UpperCAmelCase__)
				model.load_state_dict(UpperCAmelCase__)
				model.eval()

				# Check outputs on an image
				lowerCamelCase					:			List[str]									= 4_80 if 'ade' in checkpoint_url else 3_84
				lowerCamelCase					:			Tuple									= DPTImageProcessor(size=UpperCAmelCase__)

				lowerCamelCase					:			Dict									= prepare_img()
				lowerCamelCase					:			Tuple									= image_processor(UpperCAmelCase__       ,							return_tensors='pt')

				# forward pass
				lowerCamelCase					:			str									= model(**UpperCAmelCase__).logits if 'ade' in checkpoint_url else model(**UpperCAmelCase__).predicted_depth

				# Assert logits
				lowerCamelCase					:			List[Any]									= torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]])
				if "ade" in checkpoint_url:
								lowerCamelCase					:			Optional[Any]									= torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]])
				assert outputs.shape == torch.Size(UpperCAmelCase__)
				assert (
				    torch.allclose(outputs[0, 0, :3, :3]       ,							UpperCAmelCase__       ,							atol=1E-4)
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3]       ,							UpperCAmelCase__)
				)

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

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


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


'''simple docstring'''






import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A			      =      logging.get_logger(__name__)

A			      =      {
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	'''segformer'''
					def __init__(     self,    A=3,    A=4,    A=[2, 2, 2, 2],    A=[8, 4, 2, 1],    A=[32, 64, 160, 256],    A=[7, 3, 3, 3],    A=[4, 2, 2, 2],    A=[1, 2, 5, 8],    A=[4, 4, 4, 4],    A="gelu",    A=0.0,    A=0.0,    A=0.1,    A=0.02,    A=0.1,    A=1e-6,    A=256,    A=255,    **A,    ):

									"""simple docstring"""


									super().__init__(**A      )

									if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
													warnings.warn(
													    'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
													    ' removed, as the behaviour will default to that of reshape_last_stage = True.',    A,    )

									lowerCamelCase					:			Tuple									= num_channels
									lowerCamelCase					:			Dict									= num_encoder_blocks
									lowerCamelCase					:			Dict									= depths
									lowerCamelCase					:			Any									= sr_ratios
									lowerCamelCase					:			List[Any]									= hidden_sizes
									lowerCamelCase					:			List[Any]									= patch_sizes
									lowerCamelCase					:			Tuple									= strides
									lowerCamelCase					:			int									= mlp_ratios
									lowerCamelCase					:			Dict									= num_attention_heads
									lowerCamelCase					:			Union[str, Any]									= hidden_act
									lowerCamelCase					:			Tuple									= hidden_dropout_prob
									lowerCamelCase					:			Optional[int]									= attention_probs_dropout_prob
									lowerCamelCase					:			List[str]									= classifier_dropout_prob
									lowerCamelCase					:			Union[str, Any]									= initializer_range
									lowerCamelCase					:			Union[str, Any]									= drop_path_rate
									lowerCamelCase					:			int									= layer_norm_eps
									lowerCamelCase					:			Any									= decoder_hidden_size
									lowerCamelCase					:			Optional[int]									= kwargs.get('reshape_last_stage',    A      )
									lowerCamelCase					:			Any									= semantic_loss_ignore_index





class     __snake_case		(   a__):
					_lowerCAmelCase			     =	version.parse('''1.11''')
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 1e-4




					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return 12
