"""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       lowercase_      (      _lowerCamelCase:		Any					)       ->					List[Any]:



							'''simple docstring'''

							__lowerCamelCase    :						Union[str, Any]						      =   DPTConfig(embedding_type="hybrid"					)

							if "large" in checkpoint_url:
														__lowerCamelCase    :						Dict						      =   1024
														__lowerCamelCase    :						Union[str, Any]						      =   4096
														__lowerCamelCase    :						Optional[int]						      =   24
														__lowerCamelCase    :						Any						      =   16
														__lowerCamelCase    :						List[str]						      =   [5, 11, 17, 23]
														__lowerCamelCase    :						List[str]						      =   [256, 512, 1024, 1024]
														__lowerCamelCase    :						Optional[int]						      =   (1, 384, 384)

							if "nyu" or "midas" in checkpoint_url:
														__lowerCamelCase    :						Any						      =   768
														__lowerCamelCase    :						Optional[int]						      =   [1, 1, 1, 0.5]
														__lowerCamelCase    :						int						      =   [256, 512, 768, 768]
														__lowerCamelCase    :						Any						      =   150
														__lowerCamelCase    :						Tuple						      =   16
														__lowerCamelCase    :						Union[str, Any]						      =   (1, 384, 384)
														__lowerCamelCase    :						Optional[Any]						      =   False
														__lowerCamelCase    :						Optional[Any]						      =   "project"

							if "ade" in checkpoint_url:
														__lowerCamelCase    :						str						      =   True
														__lowerCamelCase    :						int						      =   768
														__lowerCamelCase    :						List[Any]						      =   [1, 1, 1, 0.5]
														__lowerCamelCase    :						Union[str, Any]						      =   150
														__lowerCamelCase    :						Tuple						      =   16
														__lowerCamelCase    :						Optional[Any]						      =   "huggingface/label-files"
														__lowerCamelCase    :						Optional[Any]						      =   "ade20k-id2label.json"
														__lowerCamelCase    :						List[str]						      =   json.load(open(cached_download(hf_hub_url(_lowerCamelCase  ,      _lowerCamelCase  ,      repo_type="dataset"					)					)  ,      "r"					)					)
														__lowerCamelCase    :						List[Any]						      =   {int(_lowerCamelCase					): v for k, v in idalabel.items()}
														__lowerCamelCase    :						Optional[Any]						      =   idalabel
														__lowerCamelCase    :						Optional[Any]						      =   {v: k for k, v in idalabel.items()}
														__lowerCamelCase    :						Optional[Any]						      =   [1, 150, 480, 480]

							return config, expected_shape


def       lowercase_      (      _lowerCamelCase:		Tuple					)       ->					List[str]:



							'''simple docstring'''

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


def       lowercase_      (      _lowerCamelCase:		List[Any]					)       ->					Optional[int]:



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

							if ".." in name:
														__lowerCamelCase    :						Optional[int]						      =   name.replace(".."  ,      "."					)

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


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


def       lowercase_      (      )       ->					List[str]:



							'''simple docstring'''

							__lowerCamelCase    :						Any						      =   "http://images.cocodataset.org/val2017/000000039769.jpg"
							__lowerCamelCase    :						Optional[Any]						      =   Image.open(requests.get(_lowerCamelCase  ,      stream=_lowerCamelCase					).raw					)
							return im


@torch.no_grad()
def       lowercase_      (      _lowerCamelCase:		int  ,      _lowerCamelCase:		Tuple  ,      _lowerCamelCase:		List[Any]  ,      _lowerCamelCase:		Union[str, Any]  ,      _lowerCamelCase:		Dict					)       ->					int:



							'''simple docstring'''

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

							# load HuggingFace model
							__lowerCamelCase    :						List[str]						      =   DPTForSemanticSegmentation(_lowerCamelCase					) if "ade" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase					)
							model.load_state_dict(_lowerCamelCase					)
							model.eval()

							# Check outputs on an image
							__lowerCamelCase    :						Union[str, Any]						      =   480 if "ade" in checkpoint_url else 384
							__lowerCamelCase    :						Union[str, Any]						      =   DPTImageProcessor(size=_lowerCamelCase					)

							__lowerCamelCase    :						Any						      =   prepare_img()
							__lowerCamelCase    :						Dict						      =   image_processor(_lowerCamelCase  ,      return_tensors="pt"					)

							# forward pass
							__lowerCamelCase    :						Any						      =   model(**_lowerCamelCase					).logits if "ade" in checkpoint_url else model(**_lowerCamelCase					).predicted_depth

							if show_prediction:
														__lowerCamelCase    :						List[str]						      =   (
														    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()) * 255					).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__":
				__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=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''',
				)

				__A     = 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 typing import TYPE_CHECKING

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


lowerCAmelCase__  :  str						     =	{
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  Any						     =	[
						    'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MCTCTForCTC',
						    'MCTCTModel',
						    'MCTCTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
						from .feature_extraction_mctct import MCTCTFeatureExtractor
						from .processing_mctct import MCTCTProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
						import sys

						lowerCAmelCase__  :  Any						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



