





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase :Dict     =    logging.get_logger(__name__)







def    A				(							UpperCAmelCase				):
					_snake_case       :					Optional[int]				       =   OrderedDict()
					for key, value in state_dict.items():
										if key.startswith("module.encoder"				):
															_snake_case       :					Any				       =   key.replace("module.encoder"  ,  "glpn.encoder"				)
										if key.startswith("module.decoder"				):
															_snake_case       :					Tuple				       =   key.replace("module.decoder"  ,  "decoder.stages"				)
										if "patch_embed" in key:
															# replace for example patch_embed1 by patch_embeddings.0
															_snake_case       :					Union[str, Any]				       =   key[key.find("patch_embed"				) + len("patch_embed"				)]
															_snake_case       :					Union[str, Any]				       =   key.replace(F"""patch_embed{idx}"""  ,  F"""patch_embeddings.{int(UpperCAmelCase				)-1}"""				)
										if "norm" in key:
															_snake_case       :					Optional[int]				       =   key.replace("norm"  ,  "layer_norm"				)
										if "glpn.encoder.layer_norm" in key:
															# replace for example layer_norm1 by layer_norm.0
															_snake_case       :					Optional[int]				       =   key[key.find("glpn.encoder.layer_norm"				) + len("glpn.encoder.layer_norm"				)]
															_snake_case       :					Optional[int]				       =   key.replace(F"""layer_norm{idx}"""  ,  F"""layer_norm.{int(UpperCAmelCase				)-1}"""				)
										if "layer_norm1" in key:
															_snake_case       :					Tuple				       =   key.replace("layer_norm1"  ,  "layer_norm_1"				)
										if "layer_norm2" in key:
															_snake_case       :					str				       =   key.replace("layer_norm2"  ,  "layer_norm_2"				)
										if "block" in key:
															# replace for example block1 by block.0
															_snake_case       :					List[str]				       =   key[key.find("block"				) + len("block"				)]
															_snake_case       :					Optional[int]				       =   key.replace(F"""block{idx}"""  ,  F"""block.{int(UpperCAmelCase				)-1}"""				)
										if "attn.q" in key:
															_snake_case       :					List[str]				       =   key.replace("attn.q"  ,  "attention.self.query"				)
										if "attn.proj" in key:
															_snake_case       :					List[Any]				       =   key.replace("attn.proj"  ,  "attention.output.dense"				)
										if "attn" in key:
															_snake_case       :					Any				       =   key.replace("attn"  ,  "attention.self"				)
										if "fc1" in key:
															_snake_case       :					Tuple				       =   key.replace("fc1"  ,  "dense1"				)
										if "fc2" in key:
															_snake_case       :					List[Any]				       =   key.replace("fc2"  ,  "dense2"				)
										if "linear_pred" in key:
															_snake_case       :					Dict				       =   key.replace("linear_pred"  ,  "classifier"				)
										if "linear_fuse" in key:
															_snake_case       :					str				       =   key.replace("linear_fuse.conv"  ,  "linear_fuse"				)
															_snake_case       :					Optional[int]				       =   key.replace("linear_fuse.bn"  ,  "batch_norm"				)
										if "linear_c" in key:
															# replace for example linear_c4 by linear_c.3
															_snake_case       :					Optional[int]				       =   key[key.find("linear_c"				) + len("linear_c"				)]
															_snake_case       :					str				       =   key.replace(F"""linear_c{idx}"""  ,  F"""linear_c.{int(UpperCAmelCase				)-1}"""				)
										if "bot_conv" in key:
															_snake_case       :					Any				       =   key.replace("bot_conv"  ,  "0.convolution"				)
										if "skip_conv1" in key:
															_snake_case       :					List[Any]				       =   key.replace("skip_conv1"  ,  "1.convolution"				)
										if "skip_conv2" in key:
															_snake_case       :					Tuple				       =   key.replace("skip_conv2"  ,  "2.convolution"				)
										if "fusion1" in key:
															_snake_case       :					int				       =   key.replace("fusion1"  ,  "1.fusion"				)
										if "fusion2" in key:
															_snake_case       :					int				       =   key.replace("fusion2"  ,  "2.fusion"				)
										if "fusion3" in key:
															_snake_case       :					Any				       =   key.replace("fusion3"  ,  "3.fusion"				)
										if "fusion" in key and "conv" in key:
															_snake_case       :					str				       =   key.replace("conv"  ,  "convolutional_layer"				)
										if key.startswith("module.last_layer_depth"				):
															_snake_case       :					Any				       =   key.replace("module.last_layer_depth"  ,  "head.head"				)
										_snake_case       :					List[Any]				       =   value

					return new_state_dict







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					# for each of the encoder blocks:
					for i in range(config.num_encoder_blocks				):
										for j in range(config.depths[i]				):
															# read in weights + bias of keys and values (which is a single matrix in the original implementation)
															_snake_case       :					str				       =   state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""				)
															_snake_case       :					Union[str, Any]				       =   state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""				)
															# next, add keys and values (in that order) to the state dict
															_snake_case       :					List[str]				       =   kv_weight[
															    : config.hidden_sizes[i], :
															]
															_snake_case       :					Tuple				       =   kv_bias[: config.hidden_sizes[i]]
															_snake_case       :					Tuple				       =   kv_weight[
															    config.hidden_sizes[i] :, :
															]
															_snake_case       :					Any				       =   kv_bias[config.hidden_sizes[i] :]







def    A				(							):
					_snake_case       :					Union[str, Any]				       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
					_snake_case       :					List[str]				       =   Image.open(requests.get(UpperCAmelCase  ,  stream=UpperCAmelCase				).raw				)

					return image







@torch.no_grad()
def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False  ,  UpperCAmelCase=None				):
					_snake_case       :					Optional[int]				       =   GLPNConfig(hidden_sizes=[64, 128, 320, 512]  ,  decoder_hidden_size=64  ,  depths=[3, 8, 27, 3]				)

					# load image processor (only resize + rescale)
					_snake_case       :					List[Any]				       =   GLPNImageProcessor()

					# prepare image
					_snake_case       :					List[str]				       =   prepare_img()
					_snake_case       :					Dict				       =   image_processor(images=UpperCAmelCase  ,  return_tensors="pt"				).pixel_values

					logger.info("Converting model..."				)

					# load original state dict
					_snake_case       :					Any				       =   torch.load(UpperCAmelCase  ,  map_location=torch.device("cpu"				)				)

					# rename keys
					_snake_case       :					List[str]				       =   rename_keys(UpperCAmelCase				)

					# key and value matrices need special treatment
					read_in_k_v(UpperCAmelCase  ,  UpperCAmelCase				)

					# create HuggingFace model and load state dict
					_snake_case       :					Optional[int]				       =   GLPNForDepthEstimation(UpperCAmelCase				)
					model.load_state_dict(UpperCAmelCase				)
					model.eval()

					# forward pass
					_snake_case       :					str				       =   model(UpperCAmelCase				)
					_snake_case       :					str				       =   outputs.predicted_depth

					# verify output
					if model_name is not None:
										if "nyu" in model_name:
															_snake_case       :					Optional[Any]				       =   torch.tensor(
															    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]				)
										elif "kitti" in model_name:
															_snake_case       :					List[str]				       =   torch.tensor(
															    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]				)
										else:
															raise ValueError(F"""Unknown model name: {model_name}"""				)

										_snake_case       :					Any				       =   torch.Size([1, 480, 640]				)

										assert predicted_depth.shape == expected_shape
										assert torch.allclose(predicted_depth[0, :3, :3]  ,  UpperCAmelCase  ,  atol=1e-4				)
										print("Looks ok!"				)

					# finally, push to hub if required
					if push_to_hub:
										logger.info("Pushing model and image processor to the hub..."				)
										model.push_to_hub(
										    repo_path_or_name=Path(UpperCAmelCase  ,  UpperCAmelCase				)  ,  organization="nielsr"  ,  commit_message="Add model"  ,  use_temp_dir=UpperCAmelCase  ,  )
										image_processor.push_to_hub(
										    repo_path_or_name=Path(UpperCAmelCase  ,  UpperCAmelCase				)  ,  organization="nielsr"  ,  commit_message="Add image processor"  ,  use_temp_dir=UpperCAmelCase  ,  )


if __name__ == "__main__":
							__lowerCAmelCase :List[str]     =    argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_path',
							    default=None,
							    type=str,
							    help='Path to the original PyTorch checkpoint (.pth file).',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
							)
							parser.add_argument(
							    '--model_name',
							    default='glpn-kitti',
							    type=str,
							    help='Name of the model in case you\'re pushing to the hub.',
							)
							__lowerCAmelCase :Any     =    parser.parse_args()
							convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






import math

from numpy import inf
from scipy.integrate import quad







def    A				(							UpperCAmelCase				):
					if num <= 0:
										raise ValueError("math domain error"				)

					return quad(UpperCAmelCase  ,  0  ,  UpperCAmelCase  ,  args=(UpperCAmelCase)				)[0]







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return math.pow(UpperCAmelCase  ,  z - 1				) * math.exp(-x				)


if __name__ == "__main__":
							from doctest import testmod

							testmod()