





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       :   Dict					= logging.get_logger(__name__)







def   __lowerCamelCase   (				__a  :List[str]       )						->							int:






		"""simple docstring"""

		A__     =  DPTConfig(embedding_type="""hybrid"""       )

		if "large" in checkpoint_url:
				A__     =  1_0_2_4
				A__     =  4_0_9_6
				A__     =  2_4
				A__     =  1_6
				A__     =  [5, 1_1, 1_7, 2_3]
				A__     =  [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
				A__     =  (1, 3_8_4, 3_8_4)

		if "nyu" or "midas" in checkpoint_url:
				A__     =  7_6_8
				A__     =  [1, 1, 1, 0.5]
				A__     =  [2_5_6, 5_1_2, 7_6_8, 7_6_8]
				A__     =  1_5_0
				A__     =  1_6
				A__     =  (1, 3_8_4, 3_8_4)
				A__     =  False
				A__     =  """project"""

		if "ade" in checkpoint_url:
				A__     =  True
				A__     =  7_6_8
				A__     =  [1, 1, 1, 0.5]
				A__     =  1_5_0
				A__     =  1_6
				A__     =  """huggingface/label-files"""
				A__     =  """ade20k-id2label.json"""
				A__     =  json.load(open(cached_download(hf_hub_url(__a       ,	__a       ,	repo_type="""dataset"""       )       )       ,	"""r"""       )       )
				A__     =  {int(__a       ): v for k, v in idalabel.items()}
				A__     =  idalabel
				A__     =  {v: k for k, v in idalabel.items()}
				A__     =  [1, 1_5_0, 4_8_0, 4_8_0]

		return config, expected_shape







def   __lowerCamelCase   (				__a  :Optional[int]       )						->							Optional[int]:






		"""simple docstring"""

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







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

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

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

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







def   __lowerCamelCase   (				__a  :List[str]       ,	__a  :Optional[int]       )						->							List[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)
				A__     =  state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'       )
				A__     =  state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias'       )
				# next, add query, keys and values (in that order) to the state dict
				A__     =  in_proj_weight[: config.hidden_size, :]
				A__     =  in_proj_bias[: config.hidden_size]
				A__     =  in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				A__     =  in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				A__     =  in_proj_weight[
				    -config.hidden_size :, :
				]
				A__     =  in_proj_bias[-config.hidden_size :]







def   __lowerCamelCase   (				)						->							Tuple:






		"""simple docstring"""

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







@torch.no_grad()
def   __lowerCamelCase   (				__a  :Dict       ,	__a  :Tuple       ,	__a  :List[Any]       ,	__a  :List[str]       ,	__a  :str       )						->							str:






		"""simple docstring"""

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

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

		# Check outputs on an image
		A__     =  4_8_0 if """ade""" in checkpoint_url else 3_8_4
		A__     =  DPTImageProcessor(size=__a       )

		A__     =  prepare_img()
		A__     =  image_processor(__a       ,	return_tensors="""pt"""       )

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

		if show_prediction:
				A__     =  (
				    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_5_5       ).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__":
					A       :   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''',
					)

					A       :   Optional[Any]					= parser.parse_args()
					convert_dpt_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
					)










def   __lowerCamelCase   (				__a  :Optional[Any]       )						->							Tuple:






		"""simple docstring"""

		A__     =  len(__a       )
		while cur > 1:
				# Find the maximum number in arr
				A__     =  arr.index(max(arr[0:cur]       )       )
				# Reverse from 0 to mi
				A__     =  arr[mi::-1] + arr[mi + 1 : len(__a       )]
				# Reverse whole list
				A__     =  arr[cur - 1 :: -1] + arr[cur : len(__a       )]
				cur -= 1
		return arr


if __name__ == "__main__":
					A       :   List[str]					= input('''Enter numbers separated by a comma:\n''').strip()
					A       :   int					= [int(item) for item in user_input.split(''',''')]
					print(pancake_sort(unsorted))



