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



def 						a_		(      lowerCamelCase ):
				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(lowerCamelCase )-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(lowerCamelCase )-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(lowerCamelCase )-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(lowerCamelCase )-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 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				# 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)
												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 						a_		(      ):
				UpperCAmelCase__    =							'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__    =							Image.open(requests.get(lowerCamelCase      ,    stream=lowerCamelCase ).raw )

				return image



@torch.no_grad()
def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase=False      ,    lowerCamelCase=None ):
				UpperCAmelCase__    =							GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,    decoder_hidden_size=6_4      ,    depths=[3, 8, 2_7, 3] )

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

				# prepare image
				UpperCAmelCase__    =							prepare_img()
				UpperCAmelCase__    =							image_processor(images=lowerCamelCase      ,    return_tensors='pt' ).pixel_values

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

				# load original state dict
				UpperCAmelCase__    =							torch.load(lowerCamelCase      ,    map_location=torch.device('cpu' ) )

				# rename keys
				UpperCAmelCase__    =							rename_keys(lowerCamelCase )

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

				# create HuggingFace model and load state dict
				UpperCAmelCase__    =							GLPNForDepthEstimation(lowerCamelCase )
				model.load_state_dict(lowerCamelCase )
				model.eval()

				# forward pass
				UpperCAmelCase__    =							model(lowerCamelCase )
				UpperCAmelCase__    =							outputs.predicted_depth

				# verify output
				if model_name is not None:
								if "nyu" in model_name:
												UpperCAmelCase__    =							torch.tensor(
												    [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
								elif "kitti" in model_name:
												UpperCAmelCase__    =							torch.tensor(
												    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
								else:
												raise ValueError(f'''Unknown model name: {model_name}''' )

								UpperCAmelCase__    =							torch.Size([1, 4_8_0, 6_4_0] )

								assert predicted_depth.shape == expected_shape
								assert torch.allclose(predicted_depth[0, :3, :3]      ,    lowerCamelCase      ,    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(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__":
						lowerCAmelCase__						:						int		  =			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__						:						str		  =			parser.parse_args()
						convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




"""simple docstring"""






from __future__ import annotations

from math import ceil, floor, sqrt



def 						a_		(      lowerCamelCase = 2_0_0_0_0_0_0 ):
				UpperCAmelCase__    =							[0]
				UpperCAmelCase__    =							42

				for idx in range(1      ,    ceil(sqrt(target * 2 ) * 1.1 ) ):
								triangle_numbers.append(triangle_numbers[-1] + idx )

				# we want this to be as close as possible to target
				UpperCAmelCase__    =							0
				# the area corresponding to the grid that gives the product closest to target
				UpperCAmelCase__    =							0
				# an estimate of b, using the quadratic formula
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_floor
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_ceil
				UpperCAmelCase__    =							42

				for idx_a, triangle_a in enumerate(triangle_numbers[1:]      ,    1 ):
								UpperCAmelCase__    =							(-1 + sqrt(1 + 8 * target / triangle_a )) / 2
								UpperCAmelCase__    =							floor(lowerCamelCase )
								UpperCAmelCase__    =							ceil(lowerCamelCase )
								UpperCAmelCase__    =							triangle_numbers[b_floor]
								UpperCAmelCase__    =							triangle_numbers[b_ceil]

								if abs(target - triangle_b_first_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_first_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_floor

								if abs(target - triangle_b_second_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_second_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_ceil

				return area


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



