


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






def 		lowercase							(     _snake_case						:      List[str]     )     ->Tuple:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				DPTConfig()

	if "large" in checkpoint_url:
		__snake_case	:						Optional[Any]						=				1_024
		__snake_case	:						List[Any]						=				4_096
		__snake_case	:						Optional[int]						=				24
		__snake_case	:						int						=				16
		__snake_case	:						Union[str, Any]						=				[5, 11, 17, 23]
		__snake_case	:						List[str]						=				[256, 512, 1_024, 1_024]
		__snake_case	:						Optional[Any]						=				(1, 384, 384)

	if "ade" in checkpoint_url:
		__snake_case	:						Union[str, Any]						=				True

		__snake_case	:						Dict						=				150
		__snake_case	:						Union[str, Any]						=				'''huggingface/label-files'''
		__snake_case	:						Optional[int]						=				'''ade20k-id2label.json'''
		__snake_case	:						Optional[int]						=				json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   repo_type='''dataset'''     )     )     ,   '''r'''     )     )
		__snake_case	:						Optional[int]						=				{int(SCREAMING_SNAKE_CASE__     ): v for k, v in idalabel.items()}
		__snake_case	:						Union[str, Any]						=				idalabel
		__snake_case	:						Tuple						=				{v: k for k, v in idalabel.items()}
		__snake_case	:						int						=				[1, 150, 480, 480]

	return config, expected_shape






def 		lowercase							(     _snake_case						:      Optional[Any]     )     ->List[Any]:
	"""simple docstring"""





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






def 		lowercase							(     _snake_case						:      int     )     ->Union[str, 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
	):
		__snake_case	:						List[str]						=				name.replace('''pretrained.model'''     ,   '''dpt.encoder'''     )
	if "pretrained.model" in name:
		__snake_case	:						Tuple						=				name.replace('''pretrained.model'''     ,   '''dpt.embeddings'''     )
	if "patch_embed" in name:
		__snake_case	:						Any						=				name.replace('''patch_embed'''     ,   '''patch_embeddings'''     )
	if "pos_embed" in name:
		__snake_case	:						Tuple						=				name.replace('''pos_embed'''     ,   '''position_embeddings'''     )
	if "attn.proj" in name:
		__snake_case	:						List[str]						=				name.replace('''attn.proj'''     ,   '''attention.output.dense'''     )
	if "proj" in name and "project" not in name:
		__snake_case	:						Any						=				name.replace('''proj'''     ,   '''projection'''     )
	if "blocks" in name:
		__snake_case	:						Tuple						=				name.replace('''blocks'''     ,   '''layer'''     )
	if "mlp.fc1" in name:
		__snake_case	:						Optional[Any]						=				name.replace('''mlp.fc1'''     ,   '''intermediate.dense'''     )
	if "mlp.fc2" in name:
		__snake_case	:						Tuple						=				name.replace('''mlp.fc2'''     ,   '''output.dense'''     )
	if "norm1" in name:
		__snake_case	:						Any						=				name.replace('''norm1'''     ,   '''layernorm_before'''     )
	if "norm2" in name:
		__snake_case	:						Tuple						=				name.replace('''norm2'''     ,   '''layernorm_after'''     )
	if "scratch.output_conv" in name:
		__snake_case	:						int						=				name.replace('''scratch.output_conv'''     ,   '''head'''     )
	if "scratch" in name:
		__snake_case	:						int						=				name.replace('''scratch'''     ,   '''neck'''     )
	if "layer1_rn" in name:
		__snake_case	:						Dict						=				name.replace('''layer1_rn'''     ,   '''convs.0'''     )
	if "layer2_rn" in name:
		__snake_case	:						Dict						=				name.replace('''layer2_rn'''     ,   '''convs.1'''     )
	if "layer3_rn" in name:
		__snake_case	:						Optional[Any]						=				name.replace('''layer3_rn'''     ,   '''convs.2'''     )
	if "layer4_rn" in name:
		__snake_case	:						Dict						=				name.replace('''layer4_rn'''     ,   '''convs.3'''     )
	if "refinenet" in name:
		__snake_case	:						Optional[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
		__snake_case	:						Optional[int]						=				name.replace(f"""refinenet{layer_idx}"""     ,   f"""fusion_stage.layers.{abs(layer_idx-4     )}"""     )
	if "out_conv" in name:
		__snake_case	:						List[str]						=				name.replace('''out_conv'''     ,   '''projection'''     )
	if "resConfUnit1" in name:
		__snake_case	:						Any						=				name.replace('''resConfUnit1'''     ,   '''residual_layer1'''     )
	if "resConfUnit2" in name:
		__snake_case	:						Optional[int]						=				name.replace('''resConfUnit2'''     ,   '''residual_layer2'''     )
	if "conv1" in name:
		__snake_case	:						Any						=				name.replace('''conv1'''     ,   '''convolution1'''     )
	if "conv2" in name:
		__snake_case	:						Optional[Any]						=				name.replace('''conv2'''     ,   '''convolution2'''     )
	# readout blocks
	if "pretrained.act_postprocess1.0.project.0" in name:
		__snake_case	:						Dict						=				name.replace('''pretrained.act_postprocess1.0.project.0'''     ,   '''neck.reassemble_stage.readout_projects.0.0'''     )
	if "pretrained.act_postprocess2.0.project.0" in name:
		__snake_case	:						Dict						=				name.replace('''pretrained.act_postprocess2.0.project.0'''     ,   '''neck.reassemble_stage.readout_projects.1.0'''     )
	if "pretrained.act_postprocess3.0.project.0" in name:
		__snake_case	:						Dict						=				name.replace('''pretrained.act_postprocess3.0.project.0'''     ,   '''neck.reassemble_stage.readout_projects.2.0'''     )
	if "pretrained.act_postprocess4.0.project.0" in name:
		__snake_case	:						Optional[int]						=				name.replace('''pretrained.act_postprocess4.0.project.0'''     ,   '''neck.reassemble_stage.readout_projects.3.0'''     )
	# resize blocks
	if "pretrained.act_postprocess1.3" in name:
		__snake_case	:						List[str]						=				name.replace('''pretrained.act_postprocess1.3'''     ,   '''neck.reassemble_stage.layers.0.projection'''     )
	if "pretrained.act_postprocess1.4" in name:
		__snake_case	:						List[str]						=				name.replace('''pretrained.act_postprocess1.4'''     ,   '''neck.reassemble_stage.layers.0.resize'''     )
	if "pretrained.act_postprocess2.3" in name:
		__snake_case	:						List[str]						=				name.replace('''pretrained.act_postprocess2.3'''     ,   '''neck.reassemble_stage.layers.1.projection'''     )
	if "pretrained.act_postprocess2.4" in name:
		__snake_case	:						List[Any]						=				name.replace('''pretrained.act_postprocess2.4'''     ,   '''neck.reassemble_stage.layers.1.resize'''     )
	if "pretrained.act_postprocess3.3" in name:
		__snake_case	:						Any						=				name.replace('''pretrained.act_postprocess3.3'''     ,   '''neck.reassemble_stage.layers.2.projection'''     )
	if "pretrained.act_postprocess4.3" in name:
		__snake_case	:						str						=				name.replace('''pretrained.act_postprocess4.3'''     ,   '''neck.reassemble_stage.layers.3.projection'''     )
	if "pretrained.act_postprocess4.4" in name:
		__snake_case	:						Any						=				name.replace('''pretrained.act_postprocess4.4'''     ,   '''neck.reassemble_stage.layers.3.resize'''     )
	if "pretrained" in name:
		__snake_case	:						str						=				name.replace('''pretrained'''     ,   '''dpt'''     )
	if "bn" in name:
		__snake_case	:						Any						=				name.replace('''bn'''     ,   '''batch_norm'''     )
	if "head" in name:
		__snake_case	:						Tuple						=				name.replace('''head'''     ,   '''head.head'''     )
	if "encoder.norm" in name:
		__snake_case	:						List[str]						=				name.replace('''encoder.norm'''     ,   '''layernorm'''     )
	if "auxlayer" in name:
		__snake_case	:						Optional[int]						=				name.replace('''auxlayer'''     ,   '''auxiliary_head.head'''     )

	return name






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






def 		lowercase							(     )     ->Optional[int]:
	"""simple docstring"""





	__snake_case	:						str						=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	__snake_case	:						Union[str, Any]						=				Image.open(requests.get(SCREAMING_SNAKE_CASE__     ,   stream=SCREAMING_SNAKE_CASE__     ).raw     )
	return im






@torch.no_grad()
def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      int     ,   _snake_case						:      List[str]     ,   _snake_case						:      Any     )     ->List[Any]:
	"""simple docstring"""





	__snake_case	,							__snake_case	:						Tuple						=				get_dpt_config(SCREAMING_SNAKE_CASE__     )
	# load original state_dict from URL
	__snake_case	:						Union[str, Any]						=				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__     ,   map_location='''cpu'''     )
	# remove certain keys
	remove_ignore_keys_(SCREAMING_SNAKE_CASE__     )
	# rename keys
	for key in state_dict.copy().keys():
		__snake_case	:						List[str]						=				state_dict.pop(SCREAMING_SNAKE_CASE__     )
		__snake_case	:						Union[str, Any]						=				val
	# read in qkv matrices
	read_in_q_k_v(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     )

	# load HuggingFace model
	__snake_case	:						List[Any]						=				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
	__snake_case	:						Optional[int]						=				480 if '''ade''' in checkpoint_url else 384
	__snake_case	:						Any						=				DPTImageProcessor(size=SCREAMING_SNAKE_CASE__     )

	__snake_case	:						List[str]						=				prepare_img()
	__snake_case	:						Dict						=				image_processor(SCREAMING_SNAKE_CASE__     ,   return_tensors='''pt'''     )

	# forward pass
	__snake_case	:						Any						=				model(**SCREAMING_SNAKE_CASE__     ).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE__     ).predicted_depth

	# Assert logits
	__snake_case	:						Dict						=				torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]     )
	if "ade" in checkpoint_url:
		__snake_case	:						int						=				torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]     )
	assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE__     )
	assert (
	    torch.allclose(outputs[0, 0, :3, :3]     ,   SCREAMING_SNAKE_CASE__     ,   atol=1e-4     )
	    if "ade" in checkpoint_url
	    else torch.allclose(outputs[0, :3, :3]     ,   SCREAMING_SNAKE_CASE__     )
	)

	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:
		print('''Pushing model to hub...'''     )
		model.push_to_hub(
		    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     )     ,   organization='''nielsr'''     ,   commit_message='''Add model'''     ,   use_temp_dir=SCREAMING_SNAKE_CASE__     ,   )
		image_processor.push_to_hub(
		    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     )     ,   organization='''nielsr'''     ,   commit_message='''Add image processor'''     ,   use_temp_dir=SCREAMING_SNAKE_CASE__     ,   )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    str									=				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.""",
			)

			SCREAMING_SNAKE_CASE :    Dict									=				parser.parse_args()
			convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				pyspark.sql.DataFrame							,   _UpperCamelCase :				Optional[NamedSplit] = None							,   _UpperCamelCase :				Optional[Features] = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = "arrow"							,   **_UpperCamelCase :				Tuple							,   )      ->str:
									super().__init__(
									    split=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   keep_in_memory=_UpperCamelCase							,   streaming=_UpperCamelCase							,   **_UpperCamelCase							,   )
									snake_case_   =	load_from_cache_file
									snake_case_   =	file_format
									snake_case_   =	Spark(
									    df=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   working_dir=_UpperCamelCase							,   **_UpperCamelCase							,   )







			def 				snake_case__(     self :				int							)      ->Tuple:
									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split							)
									snake_case_   =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_UpperCamelCase							,   file_format=self._file_format							,   )
									return self.builder.as_dataset(split=self.split							)