
"""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__      :    List[Any]    							= logging.get_logger(__name__)

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   DPTConfig()

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

							if "ade" in checkpoint_url:
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   True

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

							return config, expected_shape

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
							for k in ignore_keys:
														state_dict.pop(_snake_case   ,_snake_case						)

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

							return name

def lowercase_  (   _snake_case   ,_snake_case						):
							for i in range(config.num_hidden_layers						):
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE__						:       List[str]           =   state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''						)
														SCREAMING_SNAKE_CASE__						:       str           =   state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''						)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   in_proj_weight[: config.hidden_size, :]
														SCREAMING_SNAKE_CASE__						:       Any           =   in_proj_bias[: config.hidden_size]
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														SCREAMING_SNAKE_CASE__						:       Dict           =   in_proj_weight[
														    -config.hidden_size :, :
														]
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   in_proj_bias[-config.hidden_size :]

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
							SCREAMING_SNAKE_CASE__						:       str           =   Image.open(requests.get(_snake_case   ,stream=_snake_case						).raw						)
							return im

@torch.no_grad()
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   get_dpt_config(_snake_case						)
							# load original state_dict from URL
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.hub.load_state_dict_from_url(_snake_case   ,map_location="""cpu"""						)
							# remove certain keys
							remove_ignore_keys_(_snake_case						)
							# rename keys
							for key in state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__						:       Tuple           =   state_dict.pop(_snake_case						)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   val
							# read in qkv matrices
							read_in_q_k_v(_snake_case   ,_snake_case						)

							# load HuggingFace model
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   DPTForSemanticSegmentation(_snake_case						) if """ade""" in checkpoint_url else DPTForDepthEstimation(_snake_case						)
							model.load_state_dict(_snake_case						)
							model.eval()

							# Check outputs on an image
							SCREAMING_SNAKE_CASE__						:       Tuple           =   480 if """ade""" in checkpoint_url else 384
							SCREAMING_SNAKE_CASE__						:       str           =   DPTImageProcessor(size=_snake_case						)

							SCREAMING_SNAKE_CASE__						:       List[str]           =   prepare_img()
							SCREAMING_SNAKE_CASE__						:       Dict           =   image_processor(_snake_case   ,return_tensors="""pt"""						)

							# forward pass
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   model(**_snake_case						).logits if """ade""" in checkpoint_url else model(**_snake_case						).predicted_depth

							# Assert logits
							SCREAMING_SNAKE_CASE__						:       Any           =   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:
														SCREAMING_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(_snake_case						)
							assert (
							    torch.allclose(outputs[0, 0, :3, :3]   ,_snake_case   ,atol=1E-4						)
							    if "ade" in checkpoint_url
							    else torch.allclose(outputs[0, :3, :3]   ,_snake_case						)
							)

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

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


if __name__ == "__main__":
					UpperCAmelCase__      :    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=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__      :    Any    							= parser.parse_args()
					convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









'''simple docstring'''





def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  int(__A)

     # Initialize Result
     _a      =  []

     # Traverse through all denomination
     for denomination in reversed(__A):
          # Find denominations
          while int(__A) >= int(__A):
               total_value -= int(__A)
               answer.append(__A)  # Append the "answers" array

     return answer


# Driver Code
if __name__ == "__main__":
  lowercase_		       =  []
  lowercase_		       =  "0"

  if (
      input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
      == "y"
  ):
    lowercase_		       =  int(input("Enter the number of denominations you want to add: ").strip())

    for i in range(0, n):
      denominations.append(int(input(F"""Denomination {i}: """).strip()))
    lowercase_		       =  input("Enter the change you want to make in Indian Currency: ").strip()
  else:
    # All denominations of Indian Currency if user does not enter
    lowercase_		       =  [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
    lowercase_		       =  input("Enter the change you want to make: ").strip()

  if int(value) == 0 or int(value) < 0:
    print("The total value cannot be zero or negative.")

  else:
    print(F"""Following is minimal change for {value}: """)
    lowercase_		       =  find_minimum_change(denominations, value)
    # Print result
    for i in range(len(answer)):
      print(answer[i], end=" ")



