


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()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      DPTConfig(embedding_type='''hybrid'''     )

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

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

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

		return config, expected_shape
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




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


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

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

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

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


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


		"""simple docstring"""




		lowercase__            =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__            =      Image.open(requests.get(SCREAMING_SNAKE_CASE						,				stream=SCREAMING_SNAKE_CASE     ).raw     )
		return im
@torch.no_grad()
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




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

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

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

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

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

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

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

		if pytorch_dump_folder_path is not None:
				Path(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
				print(f'Saving model to {pytorch_dump_folder_path}'     )
				model.save_pretrained(SCREAMING_SNAKE_CASE     )
				print(f'Saving image processor to {pytorch_dump_folder_path}'     )
				image_processor.save_pretrained(SCREAMING_SNAKE_CASE     )

		if push_to_hub:
				model.push_to_hub('''ybelkada/dpt-hybrid-midas'''     )
				image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''     )


if __name__ == "__main__":
			lowerCAmelCase       =							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',
			)

			lowerCAmelCase       =							parser.parse_args()
			convert_dpt_checkpoint(
			    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
			)






from typing import TYPE_CHECKING

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


lowerCAmelCase       =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TapasForMaskedLM',
			    'TapasForQuestionAnswering',
			    'TapasForSequenceClassification',
			    'TapasModel',
			    'TapasPreTrainedModel',
			    'load_tf_weights_in_tapas',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFTapasForMaskedLM',
			    'TFTapasForQuestionAnswering',
			    'TFTapasForSequenceClassification',
			    'TFTapasModel',
			    'TFTapasPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
			from .tokenization_tapas import TapasTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tapas import (
						    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TapasForMaskedLM,
						    TapasForQuestionAnswering,
						    TapasForSequenceClassification,
						    TapasModel,
						    TapasPreTrainedModel,
						    load_tf_weights_in_tapas,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_tapas import (
						    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFTapasForMaskedLM,
						    TFTapasForQuestionAnswering,
						    TFTapasForSequenceClassification,
						    TFTapasModel,
						    TFTapasPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase       =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


