

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_    :						Any    		=      logging.get_logger(__name__)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]	)		->							Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			DPTConfig()

 if "large" in checkpoint_url:
  SCREAMING_SNAKE_CASE								=			10_24
  SCREAMING_SNAKE_CASE								=			40_96
  SCREAMING_SNAKE_CASE								=			24
  SCREAMING_SNAKE_CASE								=			16
  SCREAMING_SNAKE_CASE								=			[5, 11, 17, 23]
  SCREAMING_SNAKE_CASE								=			[2_56, 5_12, 10_24, 10_24]
  SCREAMING_SNAKE_CASE								=			(1, 3_84, 3_84)

 if "ade" in checkpoint_url:
  SCREAMING_SNAKE_CASE								=			True

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

 return config, expected_shape


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]	)		->							Union[str, Any]:
 '''simple docstring'''


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


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

 return name


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  Optional[Any]	)		->							Any:
 '''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)
  SCREAMING_SNAKE_CASE								=			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""	)
  SCREAMING_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
  SCREAMING_SNAKE_CASE								=			in_proj_weight[: config.hidden_size, :]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[: config.hidden_size]
  SCREAMING_SNAKE_CASE								=			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  SCREAMING_SNAKE_CASE								=			in_proj_weight[
      -config.hidden_size :, :
  ]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[-config.hidden_size :]


def        __lowerCAmelCase    (       )		->							Tuple:
 '''simple docstring'''


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


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  int	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			get_dpt_config(_UpperCamelCase	)
 # load original state_dict from URL
 SCREAMING_SNAKE_CASE								=			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():
  SCREAMING_SNAKE_CASE								=			state_dict.pop(_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			val
 # read in qkv matrices
 read_in_q_k_v(_UpperCamelCase		,							_UpperCamelCase	)

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

 # Check outputs on an image
 SCREAMING_SNAKE_CASE								=			4_80 if 'ade' in checkpoint_url else 3_84
 SCREAMING_SNAKE_CASE								=			DPTImageProcessor(size=_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			prepare_img()
 SCREAMING_SNAKE_CASE								=			image_processor(_UpperCamelCase		,							return_tensors='pt'	)

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

 # Assert logits
 SCREAMING_SNAKE_CASE								=			torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]	)
 if "ade" in checkpoint_url:
  SCREAMING_SNAKE_CASE								=			torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]	)
 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_    :						Dict    		=      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_    :						Any    		=      parser.parse_args()
      convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




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

