


'''simple docstring'''



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()
_lowercase      :      str            =					logging.get_logger(__name__)


def 				lowerCamelCase__				(	A       :      Any					):




						'''simple docstring'''






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

						return new_state_dict


def 				lowerCamelCase__				(	A       :      int				,       A       :      Any					):




						'''simple docstring'''






						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)
																		UpperCAmelCase					    =     state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""					)
																		UpperCAmelCase					    =     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
																		UpperCAmelCase					    =     kv_weight[
																		    : config.hidden_sizes[i], :
																		]
																		UpperCAmelCase					    =     kv_bias[: config.hidden_sizes[i]]
																		UpperCAmelCase					    =     kv_weight[
																		    config.hidden_sizes[i] :, :
																		]
																		UpperCAmelCase					    =     kv_bias[config.hidden_sizes[i] :]


def 				lowerCamelCase__				(	):




						'''simple docstring'''






						UpperCAmelCase					    =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						UpperCAmelCase					    =     Image.open(requests.get(A				,       stream=A					).raw					)

						return image


@torch.no_grad()
def 				lowerCamelCase__				(	A       :      str				,       A       :      Optional[Any]				,       A       :      Dict=False				,       A       :      int=None					):




						'''simple docstring'''






						UpperCAmelCase					    =     GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]				,       decoder_hidden_size=64				,       depths=[3, 8, 27, 3]					)

						# load image processor (only resize + rescale)
						UpperCAmelCase					    =     GLPNImageProcessor()

						# prepare image
						UpperCAmelCase					    =     prepare_img()
						UpperCAmelCase					    =     image_processor(images=A				,       return_tensors='''pt'''					).pixel_values

						logger.info('''Converting model...'''					)

						# load original state dict
						UpperCAmelCase					    =     torch.load(A				,       map_location=torch.device('''cpu'''					)					)

						# rename keys
						UpperCAmelCase					    =     rename_keys(A					)

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

						# create HuggingFace model and load state dict
						UpperCAmelCase					    =     GLPNForDepthEstimation(A					)
						model.load_state_dict(A					)
						model.eval()

						# forward pass
						UpperCAmelCase					    =     model(A					)
						UpperCAmelCase					    =     outputs.predicted_depth

						# verify output
						if model_name is not None:
												if "nyu" in model_name:
																		UpperCAmelCase					    =     torch.tensor(
																		    [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]					)
												elif "kitti" in model_name:
																		UpperCAmelCase					    =     torch.tensor(
																		    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]					)
												else:
																		raise ValueError(f"""Unknown model name: {model_name}"""					)

												UpperCAmelCase					    =     torch.Size([1, 4_80, 6_40]					)

												assert predicted_depth.shape == expected_shape
												assert torch.allclose(predicted_depth[0, :3, :3]				,       A				,       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(A				,       A					)				,       organization='''nielsr'''				,       commit_message='''Add model'''				,       use_temp_dir=A				,       )
												image_processor.push_to_hub(
												    repo_path_or_name=Path(A				,       A					)				,       organization='''nielsr'''				,       commit_message='''Add image processor'''				,       use_temp_dir=A				,       )


if __name__ == "__main__":
	_lowercase      :      Optional[Any]            =					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.""",
	)
	_lowercase      :      Any            =					parser.parse_args()
	convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









'''simple docstring'''



_lowercase      :      Any            =					range(2, 20 + 1)
_lowercase      :      str            =					[10**k for k in range(ks[-1] + 1)]
_lowercase      :      dict[int, dict[int, list[list[int]]]]            =					{}


def 				lowerCamelCase__				(	A       :      int				,       A       :      str				,       A       :      List[Any]				,       A       :      Union[str, Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     sum(a_i[j] for j in range(A				,       len(A					)					)					)
						UpperCAmelCase					    =     sum(a_i[j] * base[j] for j in range(min(len(A					)				,       A					)					)					)

						UpperCAmelCase    ,    UpperCAmelCase					    =     0, 0
						UpperCAmelCase					    =     n - i

						UpperCAmelCase					    =     memo.get(A					)

						if sub_memo is not None:
												UpperCAmelCase					    =     sub_memo.get(A					)

												if jumps is not None and len(A					) > 0:
																		# find and make the largest jump without going over
																		UpperCAmelCase					    =     -1
																		for _k in range(len(A					) - 1				,       -1				,       -1					):
																								if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																														UpperCAmelCase					    =     _k
																														break

																		if max_jump >= 0:
																								UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     jumps[max_jump]
																								# since the difference between jumps is cached, add c
																								UpperCAmelCase					    =     diff + c
																								for j in range(min(A				,       len(A					)					)					):
																														UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
																								if new_c > 0:
																														add(A				,       A				,       A					)

												else:
																		UpperCAmelCase					    =     []
						else:
												UpperCAmelCase					    =     {c: []}
												UpperCAmelCase					    =     sub_memo

						if dn >= max_dn or c + diff >= base[k]:
												return diff, dn

						if k > ks[0]:
												while True:
																		# keep doing smaller jumps
																		UpperCAmelCase    ,    UpperCAmelCase					    =     next_term(A				,       k - 1				,       i + dn				,       A					)
																		diff += _diff
																		dn += terms_jumped

																		if dn >= max_dn or c + diff >= base[k]:
																								break
						else:
												# would be too small a jump, just compute sequential terms instead
												UpperCAmelCase    ,    UpperCAmelCase					    =     compute(A				,       A				,       i + dn				,       A					)
												diff += _diff
												dn += terms_jumped

						UpperCAmelCase					    =     sub_memo[c]

						# keep jumps sorted by # of terms skipped
						UpperCAmelCase					    =     0
						while j < len(A					):
												if jumps[j][1] > dn:
																		break
												j += 1

						# cache the jump for this value digitsum(b) and c
						sub_memo[c].insert(A				,       (diff, dn, k)					)
						return (diff, dn)


def 				lowerCamelCase__				(	A       :      Dict				,       A       :      Optional[int]				,       A       :      List[Any]				,       A       :      int					):




						'''simple docstring'''






						if i >= n:
												return 0, i
						if k > len(A					):
												a_i.extend([0 for _ in range(k - len(A					)					)]					)

						# note: a_i -> b * 10^k + c
						# ds_b -> digitsum(b)
						# ds_c -> digitsum(c)
						UpperCAmelCase					    =     i
						UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     0, 0, 0
						for j in range(len(A					)					):
												if j >= k:
																		ds_b += a_i[j]
												else:
																		ds_c += a_i[j]

						while i < n:
												i += 1
												UpperCAmelCase					    =     ds_c + ds_b
												diff += addend
												UpperCAmelCase					    =     0
												for j in range(A					):
																		UpperCAmelCase					    =     a_i[j] + addend
																		UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)

																		ds_c += a_i[j]

												if addend > 0:
																		break

						if addend > 0:
												add(A				,       A				,       A					)
						return diff, i - start_i


def 				lowerCamelCase__				(	A       :      List[str]				,       A       :      Optional[int]				,       A       :      Optional[Any]					):




						'''simple docstring'''






						for j in range(A				,       len(A					)					):
												UpperCAmelCase					    =     digits[j] + addend
												if s >= 10:
																		UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
																		UpperCAmelCase					    =     addend // 10 + quotient
												else:
																		UpperCAmelCase					    =     s
																		UpperCAmelCase					    =     addend // 10

												if addend == 0:
																		break

						while addend > 0:
												UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
												digits.append(A					)


def 				lowerCamelCase__				(	A       :      int = 10**15					):




						'''simple docstring'''






						UpperCAmelCase					    =     [1]
						UpperCAmelCase					    =     1
						UpperCAmelCase					    =     0
						while True:
												UpperCAmelCase    ,    UpperCAmelCase					    =     next_term(A				,       20				,       i + dn				,       A					)
												dn += terms_jumped
												if dn == n - i:
																		break

						UpperCAmelCase					    =     0
						for j in range(len(A					)					):
												a_n += digits[j] * 10**j
						return a_n


if __name__ == "__main__":
	print(F"""{solution() = }""")





