


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()
snake_case							:	Tuple					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    Tuple       ):






							"""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 							__lowercase       (			__lowerCAmelCase							:    Dict       ):






							"""simple docstring"""




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






def 							__lowercase       (			__lowerCAmelCase							:    Tuple       ):






							"""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 							__lowercase       (			__lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Dict       ):






							"""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 							__lowercase       (			):






							"""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 							__lowercase       (			__lowerCAmelCase							:    Tuple ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    List[str]       ):






							"""simple docstring"""




							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_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:
														a__					=					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(_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__":
						snake_case							:	Tuple					      =				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.''',
						)

						snake_case							:	int					      =				parser.parse_args()
						convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:	Optional[int]					      =				logging.get_logger(__name__)

snake_case							:	Union[str, Any]					      =				{
    '''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Dict   				=      '''visual_bert'''
					def __init__(				self       :Optional[int]      ,__snake_case       :Any=3_05_22      ,__snake_case       :str=7_68      ,__snake_case       :Any=5_12      ,__snake_case       :Any=12      ,__snake_case       :int=12      ,__snake_case       :str=30_72      ,__snake_case       :int="gelu"      ,__snake_case       :Optional[int]=0.1      ,__snake_case       :str=0.1      ,__snake_case       :Union[str, Any]=5_12      ,__snake_case       :Tuple=2      ,__snake_case       :Union[str, Any]=0.02      ,__snake_case       :Optional[Any]=1E-12      ,__snake_case       :Optional[Any]=False      ,__snake_case       :int=True      ,__snake_case       :Any=1      ,__snake_case       :Optional[int]=0      ,__snake_case       :Tuple=2      ,**__snake_case       :Any      ,) ->   Union[str, Any]:
												super().__init__(pad_token_id=__snake_case      ,bos_token_id=__snake_case      ,eos_token_id=__snake_case      ,**__snake_case				)

												a__					=					vocab_size
												a__					=					max_position_embeddings
												a__					=					hidden_size
												a__					=					visual_embedding_dim
												a__					=					num_hidden_layers
												a__					=					num_attention_heads
												a__					=					intermediate_size
												a__					=					hidden_act
												a__					=					hidden_dropout_prob
												a__					=					attention_probs_dropout_prob
												a__					=					initializer_range
												a__					=					type_vocab_size
												a__					=					layer_norm_eps
												a__					=					bypass_transformer
												a__					=					special_visual_initialize




