"""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()
_UpperCamelCase								=		logging.get_logger(__name__)

def  _A(		lowerCAmelCase							):
							A__       :     List[str]								=			DPTConfig()

							if "large" in checkpoint_url:
														A__       :     Optional[int]								=			1024
														A__       :     Optional[Any]								=			4096
														A__       :     Union[str, Any]								=			24
														A__       :     Any								=			16
														A__       :     str								=			[5, 11, 17, 23]
														A__       :     str								=			[256, 512, 1024, 1024]
														A__       :     Any								=			(1, 384, 384)

							if "ade" in checkpoint_url:
														A__       :     int								=			True

														A__       :     List[Any]								=			150
														A__       :     int								=			"""huggingface/label-files"""
														A__       :     List[Any]								=			"""ade20k-id2label.json"""
														A__       :     Union[str, Any]								=			json.load(open(cached_download(hf_hub_url(lowerCAmelCase			,  lowerCAmelCase			,  repo_type="""dataset"""							)							)			,  """r"""							)							)
														A__       :     List[Any]								=			{int(lowerCAmelCase							): v for k, v in idalabel.items()}
														A__       :     Union[str, Any]								=			idalabel
														A__       :     List[Any]								=			{v: k for k, v in idalabel.items()}
														A__       :     Any								=			[1, 150, 480, 480]

							return config, expected_shape

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

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

							return name

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							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__       :     Optional[Any]								=			state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''							)
														A__       :     Optional[int]								=			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__       :     Dict								=			in_proj_weight[: config.hidden_size, :]
														A__       :     List[Any]								=			in_proj_bias[: config.hidden_size]
														A__       :     List[Any]								=			in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														A__       :     Any								=			in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														A__       :     Optional[Any]								=			in_proj_weight[
														    -config.hidden_size :, :
														]
														A__       :     List[Any]								=			in_proj_bias[-config.hidden_size :]

def  _A(		):
							A__       :     int								=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							A__       :     Union[str, Any]								=			Image.open(requests.get(lowerCAmelCase			,  stream=lowerCAmelCase							).raw							)
							return im

@torch.no_grad()
def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__				,							A__       :     List[Any]								=			get_dpt_config(lowerCAmelCase							)
							# load original state_dict from URL
							A__       :     Union[str, Any]								=			torch.hub.load_state_dict_from_url(lowerCAmelCase			,  map_location="""cpu"""							)
							# remove certain keys
							remove_ignore_keys_(lowerCAmelCase							)
							# rename keys
							for key in state_dict.copy().keys():
														A__       :     str								=			state_dict.pop(lowerCAmelCase							)
														A__       :     Any								=			val
							# read in qkv matrices
							read_in_q_k_v(lowerCAmelCase			,  lowerCAmelCase							)

							# load HuggingFace model
							A__       :     List[str]								=			DPTForSemanticSegmentation(lowerCAmelCase							) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase							)
							model.load_state_dict(lowerCAmelCase							)
							model.eval()

							# Check outputs on an image
							A__       :     Union[str, Any]								=			480 if """ade""" in checkpoint_url else 384
							A__       :     Any								=			DPTImageProcessor(size=lowerCAmelCase							)

							A__       :     Union[str, Any]								=			prepare_img()
							A__       :     str								=			image_processor(lowerCAmelCase			,  return_tensors="""pt"""							)

							# forward pass
							A__       :     List[Any]								=			model(**lowerCAmelCase							).logits if """ade""" in checkpoint_url else model(**lowerCAmelCase							).predicted_depth

							# Assert logits
							A__       :     Tuple								=			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:
														A__       :     Dict								=			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(lowerCAmelCase							)
							assert (
							    torch.allclose(outputs[0, 0, :3, :3]			,  lowerCAmelCase			,  atol=1E-4							)
							    if "ade" in checkpoint_url
							    else torch.allclose(outputs[0, :3, :3]			,  lowerCAmelCase							)
							)

							Path(lowerCAmelCase							).mkdir(exist_ok=lowerCAmelCase							)
							print(F'''Saving model to {pytorch_dump_folder_path}'''							)
							model.save_pretrained(lowerCAmelCase							)
							print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
							image_processor.save_pretrained(lowerCAmelCase							)

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


if __name__ == "__main__":
				_UpperCamelCase								=		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.",
				)

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






"""simple docstring"""



from collections import defaultdict
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Tuple								=			total  # total no of tasks (N)

										# DP table will have a dimension of (2^M)*N
										# initially all values are set to -1
										A__       :     Optional[int]								=			[
										    [-1 for i in range(total + 1							)] for j in range(2 ** len(snake_case_							)							)
										]

										A__       :     Optional[int]								=			defaultdict(snake_case_							)  # stores the list of persons for each task

										# final_mask is used to check if all persons are included by setting all bits
										# to 1
										A__       :     Optional[int]								=			(1 << len(snake_case_							)) - 1





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if mask == self.final_mask:
																	return 1

										# if not everyone gets the task and no more tasks are available, return 0
										if task_no > self.total_tasks:
																	return 0

										# if case already considered
										if self.dp[mask][task_no] != -1:
																	return self.dp[mask][task_no]

										# Number of ways when we don't this task in the arrangement
										A__       :     Optional[int]								=			self.count_ways_until(snake_case_   ,      task_no + 1							)

										# now assign the tasks one by one to all possible persons and recursively
										# assign for the remaining tasks.
										if task_no in self.task:
																	for p in self.task[task_no]:
																								# if p is already given a task
																								if mask & (1 << p):
																															continue

																								# assign this task to p and change the mask value. And recursively
																								# assign tasks with the new mask value.
																								total_ways_util += self.count_ways_until(mask | (1 << p)   ,      task_no + 1							)

        # save the value.
										A__       :     List[str]								=			total_ways_util

										return self.dp[mask][task_no]





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										for i in range(len(snake_case_							)							):
																	for j in task_performed[i]:
																								self.task[j].append(snake_case_							)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
										return self.count_ways_until(0   ,      1							)


if __name__ == "__main__":
				_UpperCamelCase								=		5  # total no of tasks (the value of N)

				# the list of tasks that can be done by M persons.
				_UpperCamelCase								=		[[1, 3, 4], [1, 2, 5], [3, 4]]
				print(
				    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
				        task_performed
				    )
				)





