






'''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				=logging.get_logger(__name__)

def 				a						( _UpperCAmelCase			)					->					List[str]:






					"""simple docstring"""







					a_						= DPTConfig()

					if "large" in checkpoint_url:
										a_						= 1_0_2_4
										a_						= 4_0_9_6
										a_						= 2_4
										a_						= 1_6
										a_						= [5, 1_1, 1_7, 2_3]
										a_						= [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
										a_						= (1, 3_8_4, 3_8_4)

					if "ade" in checkpoint_url:
										a_						= True

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

					return config, expected_shape

def 				a						( _UpperCAmelCase			)					->					int:






					"""simple docstring"""







					a_						= ['pretrained.model.head.weight', 'pretrained.model.head.bias']
					for k in ignore_keys:
										state_dict.pop(_A       ,	_A			)

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

					return name

def 				a						( _UpperCAmelCase       ,	_UpperCAmelCase			)					->					Tuple:






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

def 				a						( )					->					Union[str, Any]:






					"""simple docstring"""







					a_						= 'http://images.cocodataset.org/val2017/000000039769.jpg'
					a_						= Image.open(requests.get(_A       ,	stream=_A			).raw			)
					return im

@torch.no_grad()
def 				a						( _UpperCAmelCase       ,	_UpperCAmelCase       ,	_UpperCAmelCase       ,	_UpperCAmelCase			)					->					Tuple:






					"""simple docstring"""







					a_				,       a_						= get_dpt_config(_A			)
					# load original state_dict from URL
					a_						= torch.hub.load_state_dict_from_url(_A       ,	map_location='cpu'			)
					# remove certain keys
					remove_ignore_keys_(_A			)
					# rename keys
					for key in state_dict.copy().keys():
										a_						= state_dict.pop(_A			)
										a_						= val
					# read in qkv matrices
					read_in_q_k_v(_A       ,	_A			)

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

					# Check outputs on an image
					a_						= 4_8_0 if 'ade' in checkpoint_url else 3_8_4
					a_						= DPTImageProcessor(size=_A			)

					a_						= prepare_img()
					a_						= image_processor(_A       ,	return_tensors='pt'			)

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

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

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


if __name__ == "__main__":
						__lowerCAmelCase				=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				=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

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


__lowercase        =    {
    '''configuration_chinese_clip''': [
        '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ChineseCLIPConfig''',
        '''ChineseCLIPOnnxConfig''',
        '''ChineseCLIPTextConfig''',
        '''ChineseCLIPVisionConfig''',
    ],
    '''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase        =    ['''ChineseCLIPFeatureExtractor''']
						__lowercase        =    ['''ChineseCLIPImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase        =    [
						    '''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''ChineseCLIPModel''',
						    '''ChineseCLIPPreTrainedModel''',
						    '''ChineseCLIPTextModel''',
						    '''ChineseCLIPVisionModel''',
						]

if TYPE_CHECKING:
						from .configuration_chinese_clip import (
						    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ChineseCLIPConfig,
						    ChineseCLIPOnnxConfig,
						    ChineseCLIPTextConfig,
						    ChineseCLIPVisionConfig,
						)
						from .processing_chinese_clip import ChineseCLIPProcessor

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_chinese_clip import (
												    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ChineseCLIPModel,
												    ChineseCLIPPreTrainedModel,
												    ChineseCLIPTextModel,
												    ChineseCLIPVisionModel,
												)

else:
						import sys

						__lowercase        =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




