


'''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()
_lowercase    :    Optional[Any]			     =  logging.get_logger(__name__)


def    lowerCamelCase__   ( A     :    int  ):




						'''simple docstring'''






						UpperCAmelCase         =			DPTConfig(embedding_type='''hybrid'''  )

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

						if "nyu" or "midas" in checkpoint_url:
												UpperCAmelCase         =			7_68
												UpperCAmelCase         =			[1, 1, 1, 0.5]
												UpperCAmelCase         =			[2_56, 5_12, 7_68, 7_68]
												UpperCAmelCase         =			1_50
												UpperCAmelCase         =			16
												UpperCAmelCase         =			(1, 3_84, 3_84)
												UpperCAmelCase         =			False
												UpperCAmelCase         =			'''project'''

						if "ade" in checkpoint_url:
												UpperCAmelCase         =			True
												UpperCAmelCase         =			7_68
												UpperCAmelCase         =			[1, 1, 1, 0.5]
												UpperCAmelCase         =			1_50
												UpperCAmelCase         =			16
												UpperCAmelCase         =			'''huggingface/label-files'''
												UpperCAmelCase         =			'''ade20k-id2label.json'''
												UpperCAmelCase         =			json.load(open(cached_download(hf_hub_url(A   ,     A   ,     repo_type='''dataset'''  )  )   ,     '''r'''  )  )
												UpperCAmelCase         =			{int(A  ): v for k, v in idalabel.items()}
												UpperCAmelCase         =			idalabel
												UpperCAmelCase         =			{v: k for k, v in idalabel.items()}
												UpperCAmelCase         =			[1, 1_50, 4_80, 4_80]

						return config, expected_shape


def    lowerCamelCase__   ( A     :    Union[str, Any]  ):




						'''simple docstring'''






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


def    lowerCamelCase__   ( A     :    List[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
						):
												UpperCAmelCase         =			name.replace('''pretrained.model'''   ,     '''dpt.encoder'''  )
						if "pretrained.model" in name:
												UpperCAmelCase         =			name.replace('''pretrained.model'''   ,     '''dpt.embeddings'''  )
						if "patch_embed" in name:
												UpperCAmelCase         =			name.replace('''patch_embed'''   ,     ''''''  )
						if "pos_embed" in name:
												UpperCAmelCase         =			name.replace('''pos_embed'''   ,     '''position_embeddings'''  )
						if "attn.proj" in name:
												UpperCAmelCase         =			name.replace('''attn.proj'''   ,     '''attention.output.dense'''  )
						if "proj" in name and "project" not in name:
												UpperCAmelCase         =			name.replace('''proj'''   ,     '''projection'''  )
						if "blocks" in name:
												UpperCAmelCase         =			name.replace('''blocks'''   ,     '''layer'''  )
						if "mlp.fc1" in name:
												UpperCAmelCase         =			name.replace('''mlp.fc1'''   ,     '''intermediate.dense'''  )
						if "mlp.fc2" in name:
												UpperCAmelCase         =			name.replace('''mlp.fc2'''   ,     '''output.dense'''  )
						if "norm1" in name and "backbone" not in name:
												UpperCAmelCase         =			name.replace('''norm1'''   ,     '''layernorm_before'''  )
						if "norm2" in name and "backbone" not in name:
												UpperCAmelCase         =			name.replace('''norm2'''   ,     '''layernorm_after'''  )
						if "scratch.output_conv" in name:
												UpperCAmelCase         =			name.replace('''scratch.output_conv'''   ,     '''head'''  )
						if "scratch" in name:
												UpperCAmelCase         =			name.replace('''scratch'''   ,     '''neck'''  )
						if "layer1_rn" in name:
												UpperCAmelCase         =			name.replace('''layer1_rn'''   ,     '''convs.0'''  )
						if "layer2_rn" in name:
												UpperCAmelCase         =			name.replace('''layer2_rn'''   ,     '''convs.1'''  )
						if "layer3_rn" in name:
												UpperCAmelCase         =			name.replace('''layer3_rn'''   ,     '''convs.2'''  )
						if "layer4_rn" in name:
												UpperCAmelCase         =			name.replace('''layer4_rn'''   ,     '''convs.3'''  )
						if "refinenet" in name:
												UpperCAmelCase         =			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
												UpperCAmelCase         =			name.replace(f"""refinenet{layer_idx}"""   ,     f"""fusion_stage.layers.{abs(layer_idx-4  )}"""  )
						if "out_conv" in name:
												UpperCAmelCase         =			name.replace('''out_conv'''   ,     '''projection'''  )
						if "resConfUnit1" in name:
												UpperCAmelCase         =			name.replace('''resConfUnit1'''   ,     '''residual_layer1'''  )
						if "resConfUnit2" in name:
												UpperCAmelCase         =			name.replace('''resConfUnit2'''   ,     '''residual_layer2'''  )
						if "conv1" in name:
												UpperCAmelCase         =			name.replace('''conv1'''   ,     '''convolution1'''  )
						if "conv2" in name:
												UpperCAmelCase         =			name.replace('''conv2'''   ,     '''convolution2'''  )
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess1.0.project.0'''   ,     '''neck.reassemble_stage.readout_projects.0.0'''  )
						if "pretrained.act_postprocess2.0.project.0" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess2.0.project.0'''   ,     '''neck.reassemble_stage.readout_projects.1.0'''  )
						if "pretrained.act_postprocess3.0.project.0" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess3.0.project.0'''   ,     '''neck.reassemble_stage.readout_projects.2.0'''  )
						if "pretrained.act_postprocess4.0.project.0" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess4.0.project.0'''   ,     '''neck.reassemble_stage.readout_projects.3.0'''  )

						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess1.3'''   ,     '''neck.reassemble_stage.layers.0.projection'''  )
						if "pretrained.act_postprocess1.4" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess1.4'''   ,     '''neck.reassemble_stage.layers.0.resize'''  )
						if "pretrained.act_postprocess2.3" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess2.3'''   ,     '''neck.reassemble_stage.layers.1.projection'''  )
						if "pretrained.act_postprocess2.4" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess2.4'''   ,     '''neck.reassemble_stage.layers.1.resize'''  )
						if "pretrained.act_postprocess3.3" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess3.3'''   ,     '''neck.reassemble_stage.layers.2.projection'''  )
						if "pretrained.act_postprocess4.3" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess4.3'''   ,     '''neck.reassemble_stage.layers.3.projection'''  )
						if "pretrained.act_postprocess4.4" in name:
												UpperCAmelCase         =			name.replace('''pretrained.act_postprocess4.4'''   ,     '''neck.reassemble_stage.layers.3.resize'''  )
						if "pretrained" in name:
												UpperCAmelCase         =			name.replace('''pretrained'''   ,     '''dpt'''  )
						if "bn" in name:
												UpperCAmelCase         =			name.replace('''bn'''   ,     '''batch_norm'''  )
						if "head" in name:
												UpperCAmelCase         =			name.replace('''head'''   ,     '''head.head'''  )
						if "encoder.norm" in name:
												UpperCAmelCase         =			name.replace('''encoder.norm'''   ,     '''layernorm'''  )
						if "auxlayer" in name:
												UpperCAmelCase         =			name.replace('''auxlayer'''   ,     '''auxiliary_head.head'''  )
						if "backbone" in name:
												UpperCAmelCase         =			name.replace('''backbone'''   ,     '''backbone.bit.encoder'''  )

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

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


def    lowerCamelCase__   ( A     :    Any   ,     A     :    Union[str, 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)
												UpperCAmelCase         =			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""  )
												UpperCAmelCase         =			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""  )
												# next, add query, keys and values (in that order) to the state dict
												UpperCAmelCase         =			in_proj_weight[: config.hidden_size, :]
												UpperCAmelCase         =			in_proj_bias[: config.hidden_size]
												UpperCAmelCase         =			in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												UpperCAmelCase         =			in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												UpperCAmelCase         =			in_proj_weight[
												    -config.hidden_size :, :
												]
												UpperCAmelCase         =			in_proj_bias[-config.hidden_size :]


def    lowerCamelCase__   ( ):




						'''simple docstring'''






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


@torch.no_grad()
def    lowerCamelCase__   ( A     :    Dict   ,     A     :    List[str]   ,     A     :    Any   ,     A     :    Dict   ,     A     :    Optional[int]  ):




						'''simple docstring'''






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

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

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

						UpperCAmelCase         =			prepare_img()
						UpperCAmelCase         =			image_processor(A   ,     return_tensors='''pt'''  )

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

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

												Image.fromarray((prediction / prediction.max()) * 2_55  ).show()

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


if __name__ == "__main__":
	_lowercase    :    Optional[int]			     =  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""",
	)

	_lowercase    :    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'''



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    :    int			     =  {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    str			     =  [
	    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XmodForCausalLM""",
	    """XmodForMaskedLM""",
	    """XmodForMultipleChoice""",
	    """XmodForQuestionAnswering""",
	    """XmodForSequenceClassification""",
	    """XmodForTokenClassification""",
	    """XmodModel""",
	    """XmodPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xmod import (
		    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XmodForCausalLM,
		    XmodForMaskedLM,
		    XmodForMultipleChoice,
		    XmodForQuestionAnswering,
		    XmodForSequenceClassification,
		    XmodForTokenClassification,
		    XmodModel,
		    XmodPreTrainedModel,
		)

else:
	import sys

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





