

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

def 	lowerCamelCase__							(   a__)		->     List[Any]:



    """simple docstring"""







    _snake_case			:			List[Any]				       =		DPTConfig(embedding_type='hybrid')

    if "large" in checkpoint_url:
        _snake_case			:			Dict				       =		1_0_2_4
        _snake_case			:			Tuple				       =		4_0_9_6
        _snake_case			:			Optional[int]				       =		2_4
        _snake_case			:			List[str]				       =		1_6
        _snake_case			:			Optional[Any]				       =		[5, 1_1, 1_7, 2_3]
        _snake_case			:			Tuple				       =		[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
        _snake_case			:			Tuple				       =		(1, 3_8_4, 3_8_4)

    if "nyu" or "midas" in checkpoint_url:
        _snake_case			:			str				       =		7_6_8
        _snake_case			:			str				       =		[1, 1, 1, 0.5]
        _snake_case			:			List[str]				       =		[2_5_6, 5_1_2, 7_6_8, 7_6_8]
        _snake_case			:			str				       =		1_5_0
        _snake_case			:			Any				       =		1_6
        _snake_case			:			Dict				       =		(1, 3_8_4, 3_8_4)
        _snake_case			:			Union[str, Any]				       =		False
        _snake_case			:			List[str]				       =		'project'

    if "ade" in checkpoint_url:
        _snake_case			:			Optional[Any]				       =		True
        _snake_case			:			List[Any]				       =		7_6_8
        _snake_case			:			str				       =		[1, 1, 1, 0.5]
        _snake_case			:			Tuple				       =		1_5_0
        _snake_case			:			Tuple				       =		1_6
        _snake_case			:			List[Any]				       =		'huggingface/label-files'
        _snake_case			:			List[str]				       =		'ade20k-id2label.json'
        _snake_case			:			Optional[Any]				       =		json.load(open(cached_download(hf_hub_url(__lowerCamelCase      ,   __lowerCamelCase      ,   repo_type='dataset'))      ,   'r'))
        _snake_case			:			Union[str, Any]				       =		{int(__lowerCamelCase): v for k, v in idalabel.items()}
        _snake_case			:			List[Any]				       =		idalabel
        _snake_case			:			List[str]				       =		{v: k for k, v in idalabel.items()}
        _snake_case			:			int				       =		[1, 1_5_0, 4_8_0, 4_8_0]

    return config, expected_shape

def 	lowerCamelCase__							(   a__)		->     int:



    """simple docstring"""







    _snake_case			:			Optional[Any]				       =		['pretrained.model.head.weight', 'pretrained.model.head.bias']
    for k in ignore_keys:
        state_dict.pop(__lowerCamelCase      ,   __lowerCamelCase)

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

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

    if "stem.conv" in name:
        _snake_case			:			Optional[int]				       =		name.replace('stem.conv'      ,   'bit.embedder.convolution')
    if "blocks" in name:
        _snake_case			:			Any				       =		name.replace('blocks'      ,   'layers')
    if "convolution" in name and "backbone" in name:
        _snake_case			:			Dict				       =		name.replace('convolution'      ,   'conv')
    if "layer" in name and "backbone" in name:
        _snake_case			:			List[Any]				       =		name.replace('layer'      ,   'layers')
    if "backbone.bit.encoder.bit" in name:
        _snake_case			:			int				       =		name.replace('backbone.bit.encoder.bit'      ,   'backbone.bit')
    if "embedder.conv" in name:
        _snake_case			:			Optional[int]				       =		name.replace('embedder.conv'      ,   'embedder.convolution')
    if "backbone.bit.encoder.stem.norm" in name:
        _snake_case			:			int				       =		name.replace('backbone.bit.encoder.stem.norm'      ,   'backbone.bit.embedder.norm')
    return name

def 	lowerCamelCase__							(   a__      ,   a__)		->     str:



    """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)
        _snake_case			:			Optional[Any]				       =		state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight""")
        _snake_case			:			int				       =		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			:			Dict				       =		in_proj_weight[: config.hidden_size, :]
        _snake_case			:			Tuple				       =		in_proj_bias[: config.hidden_size]
        _snake_case			:			List[Any]				       =		in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        _snake_case			:			int				       =		in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        _snake_case			:			str				       =		in_proj_weight[
            -config.hidden_size :, :
        ]
        _snake_case			:			Any				       =		in_proj_bias[-config.hidden_size :]

def 	lowerCamelCase__							(   )		->     List[Any]:



    """simple docstring"""







    _snake_case			:			Optional[int]				       =		'http://images.cocodataset.org/val2017/000000039769.jpg'
    _snake_case			:			Union[str, Any]				       =		Image.open(requests.get(__lowerCamelCase      ,   stream=__lowerCamelCase).raw)
    return im

@torch.no_grad()
def 	lowerCamelCase__							(   a__      ,   a__      ,   a__      ,   a__      ,   a__)		->     Any:



    """simple docstring"""







    _snake_case       ,				_snake_case			:			Tuple				       =		get_dpt_config(__lowerCamelCase)
    # load original state_dict from URL
    # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    _snake_case			:			List[str]				       =		torch.load(__lowerCamelCase      ,   map_location='cpu')
    # remove certain keys
    remove_ignore_keys_(__lowerCamelCase)
    # rename keys
    for key in state_dict.copy().keys():
        _snake_case			:			List[Any]				       =		state_dict.pop(__lowerCamelCase)
        _snake_case			:			Optional[int]				       =		val
    # read in qkv matrices
    read_in_q_k_v(__lowerCamelCase      ,   __lowerCamelCase)

    # load HuggingFace model
    _snake_case			:			str				       =		DPTForSemanticSegmentation(__lowerCamelCase) if 'ade' in checkpoint_url else DPTForDepthEstimation(__lowerCamelCase)
    model.load_state_dict(__lowerCamelCase)
    model.eval()

    # Check outputs on an image
    _snake_case			:			Optional[int]				       =		4_8_0 if 'ade' in checkpoint_url else 3_8_4
    _snake_case			:			Dict				       =		DPTImageProcessor(size=__lowerCamelCase)

    _snake_case			:			List[str]				       =		prepare_img()
    _snake_case			:			Tuple				       =		image_processor(__lowerCamelCase      ,   return_tensors='pt')

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

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

        Image.fromarray((prediction / prediction.max()) * 2_5_5).show()

    if pytorch_dump_folder_path is not None:
        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:
        model.push_to_hub('ybelkada/dpt-hybrid-midas')
        image_processor.push_to_hub('ybelkada/dpt-hybrid-midas')


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_     = 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",
   )

   SCREAMING_SNAKE_CASE_     = 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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    ImageTextPipelineOutput,
						    UniDiffuserPipeline,
						)
else:
						from .modeling_text_decoder import UniDiffuserTextDecoder
						from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
						from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
