


"""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     :   str			    =			logging.get_logger(__name__)


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			DPTConfig(embedding_type="hybrid"      )

			if "large" in checkpoint_url:
						__lowerCAmelCase				    =			1024
						__lowerCAmelCase				    =			4096
						__lowerCAmelCase				    =			24
						__lowerCAmelCase				    =			16
						__lowerCAmelCase				    =			[5, 11, 17, 23]
						__lowerCAmelCase				    =			[256, 512, 1024, 1024]
						__lowerCAmelCase				    =			(1, 384, 384)

			if "nyu" or "midas" in checkpoint_url:
						__lowerCAmelCase				    =			768
						__lowerCAmelCase				    =			[1, 1, 1, 0.5]
						__lowerCAmelCase				    =			[256, 512, 768, 768]
						__lowerCAmelCase				    =			150
						__lowerCAmelCase				    =			16
						__lowerCAmelCase				    =			(1, 384, 384)
						__lowerCAmelCase				    =			False
						__lowerCAmelCase				    =			"project"

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

			return config, expected_shape


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase      ):

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

			if ".." in name:
						__lowerCAmelCase				    =			name.replace(".."    , "."      )

			if "stem.conv" in name:
						__lowerCAmelCase				    =			name.replace("stem.conv"    , "bit.embedder.convolution"      )
			if "blocks" in name:
						__lowerCAmelCase				    =			name.replace("blocks"    , "layers"      )
			if "convolution" in name and "backbone" in name:
						__lowerCAmelCase				    =			name.replace("convolution"    , "conv"      )
			if "layer" in name and "backbone" in name:
						__lowerCAmelCase				    =			name.replace("layer"    , "layers"      )
			if "backbone.bit.encoder.bit" in name:
						__lowerCAmelCase				    =			name.replace("backbone.bit.encoder.bit"    , "backbone.bit"      )
			if "embedder.conv" in name:
						__lowerCAmelCase				    =			name.replace("embedder.conv"    , "embedder.convolution"      )
			if "backbone.bit.encoder.stem.norm" in name:
						__lowerCAmelCase				    =			name.replace("backbone.bit.encoder.stem.norm"    , "backbone.bit.embedder.norm"      )
			return name


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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				    =			state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"      )
						__lowerCAmelCase				    =			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				    =			in_proj_weight[: config.hidden_size, :]
						__lowerCAmelCase				    =			in_proj_bias[: config.hidden_size]
						__lowerCAmelCase				    =			in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						__lowerCAmelCase				    =			in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						__lowerCAmelCase				    =			in_proj_weight[
						    -config.hidden_size :, :
						]
						__lowerCAmelCase				    =			in_proj_bias[-config.hidden_size :]


def _lowerCamelCase	(		):

			'''simple docstring'''




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


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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

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

			# Check outputs on an image
			__lowerCAmelCase				    =			480 if "ade" in checkpoint_url else 384
			__lowerCAmelCase				    =			DPTImageProcessor(size=_UpperCamelCase      )

			__lowerCAmelCase				    =			prepare_img()
			__lowerCAmelCase				    =			image_processor(_UpperCamelCase    , return_tensors="pt"      )

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

			if show_prediction:
						__lowerCAmelCase				    =			(
						    torch.nn.functional.interpolate(
						        outputs.unsqueeze(1      )    , size=(image.size[1], image.size[0])    , mode="bicubic"    , align_corners=_UpperCamelCase    , )
						    .squeeze()
						    .cpu()
						    .numpy()
						)

						Image.fromarray((prediction / prediction.max()) * 255      ).show()

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


if __name__ == "__main__":
	A     :   Any			    =			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     :   str			    =			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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :   Tuple			    =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Dict			    =			["BlenderbotTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   str			    =			[
	    "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BlenderbotForCausalLM",
	    "BlenderbotForConditionalGeneration",
	    "BlenderbotModel",
	    "BlenderbotPreTrainedModel",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "TFBlenderbotForConditionalGeneration",
	    "TFBlenderbotModel",
	    "TFBlenderbotPreTrainedModel",
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "FlaxBlenderbotForConditionalGeneration",
	    "FlaxBlenderbotModel",
	    "FlaxBlenderbotPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	A     :   Optional[Any]			    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




