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


def    lowercase_   (   _lowerCamelCase:	int      ) ->      Tuple:



							'''simple docstring'''

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

							return new_state_dict


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	int      ) ->      List[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)
																					__lowerCamelCase					:		Any		   =     state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""      )
																					__lowerCamelCase					:		Optional[int]		   =     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
																					__lowerCamelCase					:		Tuple		   =     kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					__lowerCamelCase					:		List[Any]		   =     kv_bias[: config.hidden_sizes[i]]
																					__lowerCamelCase					:		Optional[int]		   =     kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					__lowerCamelCase					:		Optional[int]		   =     kv_bias[config.hidden_sizes[i] :]


def    lowercase_   (   ) ->      Any:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[int]		   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
							__lowerCamelCase					:		str		   =     Image.open(requests.get(_lowerCamelCase						,   stream=_lowerCamelCase      ).raw      )

							return image


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	List[str]=False						,   _lowerCamelCase:	Optional[Any]=None      ) ->      str:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,   decoder_hidden_size=64						,   depths=[3, 8, 27, 3]      )

							# load image processor (only resize + rescale)
							__lowerCamelCase					:		int		   =     GLPNImageProcessor()

							# prepare image
							__lowerCamelCase					:		List[str]		   =     prepare_img()
							__lowerCamelCase					:		Any		   =     image_processor(images=_lowerCamelCase						,   return_tensors="pt"      ).pixel_values

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

							# load original state dict
							__lowerCamelCase					:		Union[str, Any]		   =     torch.load(_lowerCamelCase						,   map_location=torch.device("cpu"      )      )

							# rename keys
							__lowerCamelCase					:		int		   =     rename_keys(_lowerCamelCase      )

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

							# create HuggingFace model and load state dict
							__lowerCamelCase					:		Dict		   =     GLPNForDepthEstimation(_lowerCamelCase      )
							model.load_state_dict(_lowerCamelCase      )
							model.eval()

							# forward pass
							__lowerCamelCase					:		Union[str, Any]		   =     model(_lowerCamelCase      )
							__lowerCamelCase					:		Optional[int]		   =     outputs.predicted_depth

							# verify output
							if model_name is not None:
														if "nyu" in model_name:
																					__lowerCamelCase					:		List[str]		   =     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:
																					__lowerCamelCase					:		Union[str, Any]		   =     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}"""      )

														__lowerCamelCase					:		Union[str, Any]		   =     torch.Size([1, 480, 640]      )

														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__":
				__A  =				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.''',
				)
				__A  =				parser.parse_args()
				convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
"""simple docstring"""





import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__A  =				(720, 1280)  # Height, Width
__A  =				(0.4, 0.6)  # if height or width lower than this scale, drop it.
__A  =				1 / 100
__A  =				''''''
__A  =				''''''
__A  =				''''''
__A  =				250


def    lowercase_   (   ) ->      None:



							'''simple docstring'''

							__lowerCamelCase   ,  __lowerCamelCase					:		List[Any]		   =     get_dataset(_lowerCamelCase						,   _lowerCamelCase      )
							for index in range(_lowerCamelCase      ):
														__lowerCamelCase					:		Optional[Any]		   =     random.sample(range(len(_lowerCamelCase      )      )						,   4      )
														__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     update_image_and_anno(
														    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   filter_scale=_lowerCamelCase						,   )

														# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
														__lowerCamelCase					:		Tuple		   =     random_chars(32      )
														__lowerCamelCase					:		Dict		   =     path.split(os.sep      )[-1].rsplit("."						,   1      )[0]
														__lowerCamelCase					:		List[str]		   =     F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
														cva.imwrite(F"""{file_root}.jpg"""						,   _lowerCamelCase						,   [cva.IMWRITE_JPEG_QUALITY, 85]      )
														print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""      )
														__lowerCamelCase					:		List[Any]		   =     []
														for anno in new_annos:
																					__lowerCamelCase					:		Any		   =     anno[3] - anno[1]
																					__lowerCamelCase					:		Optional[int]		   =     anno[4] - anno[2]
																					__lowerCamelCase					:		Optional[int]		   =     anno[1] + width / 2
																					__lowerCamelCase					:		Union[str, Any]		   =     anno[2] + height / 2
																					__lowerCamelCase					:		int		   =     F"""{anno[0]} {x_center} {y_center} {width} {height}"""
																					annos_list.append(_lowerCamelCase      )
														with open(F"""{file_root}.txt"""						,   "w"      ) as outfile:
																					outfile.write("\n".join(line for line in annos_list      )      )


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	str      ) ->      tuple[list, list]:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[int]		   =     []
							__lowerCamelCase					:		Any		   =     []
							for label_file in glob.glob(os.path.join(_lowerCamelCase						,   "*.txt"      )      ):
														__lowerCamelCase					:		List[Any]		   =     label_file.split(os.sep      )[-1].rsplit("."						,   1      )[0]
														with open(_lowerCamelCase      ) as in_file:
																					__lowerCamelCase					:		Tuple		   =     in_file.readlines()
														__lowerCamelCase					:		List[str]		   =     os.path.join(_lowerCamelCase						,   F"""{label_name}.jpg"""      )

														__lowerCamelCase					:		Union[str, Any]		   =     []
														for obj_list in obj_lists:
																					__lowerCamelCase					:		str		   =     obj_list.rstrip("\n"      ).split(" "      )
																					__lowerCamelCase					:		Union[str, Any]		   =     float(obj[1]      ) - float(obj[3]      ) / 2
																					__lowerCamelCase					:		Tuple		   =     float(obj[2]      ) - float(obj[4]      ) / 2
																					__lowerCamelCase					:		Union[str, Any]		   =     float(obj[1]      ) + float(obj[3]      ) / 2
																					__lowerCamelCase					:		Any		   =     float(obj[2]      ) + float(obj[4]      ) / 2

																					boxes.append([int(obj[0]      ), xmin, ymin, xmax, ymax]      )
														if not boxes:
																					continue
														img_paths.append(_lowerCamelCase      )
														labels.append(_lowerCamelCase      )
							return img_paths, labels


def    lowercase_   (   _lowerCamelCase:	list						,   _lowerCamelCase:	list						,   _lowerCamelCase:	list[int]						,   _lowerCamelCase:	tuple[int, int]						,   _lowerCamelCase:	tuple[float, float]						,   _lowerCamelCase:	float = 0.0						,   ) ->      tuple[list, list, str]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     np.zeros([output_size[0], output_size[1], 3]						,   dtype=np.uinta      )
							__lowerCamelCase					:		Optional[Any]		   =     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
							__lowerCamelCase					:		List[str]		   =     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
							__lowerCamelCase					:		int		   =     int(scale_x * output_size[1]      )
							__lowerCamelCase					:		Optional[Any]		   =     int(scale_y * output_size[0]      )

							__lowerCamelCase					:		List[Any]		   =     []
							__lowerCamelCase					:		Optional[Any]		   =     []
							for i, index in enumerate(_lowerCamelCase      ):
														__lowerCamelCase					:		List[str]		   =     all_img_list[index]
														path_list.append(_lowerCamelCase      )
														__lowerCamelCase					:		Optional[Any]		   =     all_annos[index]
														__lowerCamelCase					:		List[str]		   =     cva.imread(_lowerCamelCase      )
														if i == 0:  # top-left
																					__lowerCamelCase					:		List[str]		   =     cva.resize(_lowerCamelCase						,   (divid_point_x, divid_point_y)      )
																					__lowerCamelCase					:		Any		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		str		   =     bbox[1] * scale_x
																												__lowerCamelCase					:		Union[str, Any]		   =     bbox[2] * scale_y
																												__lowerCamelCase					:		Optional[int]		   =     bbox[3] * scale_x
																												__lowerCamelCase					:		Union[str, Any]		   =     bbox[4] * scale_y
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
														elif i == 1:  # top-right
																					__lowerCamelCase					:		str		   =     cva.resize(_lowerCamelCase						,   (output_size[1] - divid_point_x, divid_point_y)      )
																					__lowerCamelCase					:		Any		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		List[Any]		   =     scale_x + bbox[1] * (1 - scale_x)
																												__lowerCamelCase					:		List[Any]		   =     bbox[2] * scale_y
																												__lowerCamelCase					:		Tuple		   =     scale_x + bbox[3] * (1 - scale_x)
																												__lowerCamelCase					:		Union[str, Any]		   =     bbox[4] * scale_y
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
														elif i == 2:  # bottom-left
																					__lowerCamelCase					:		Any		   =     cva.resize(_lowerCamelCase						,   (divid_point_x, output_size[0] - divid_point_y)      )
																					__lowerCamelCase					:		List[str]		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		Any		   =     bbox[1] * scale_x
																												__lowerCamelCase					:		Optional[int]		   =     scale_y + bbox[2] * (1 - scale_y)
																												__lowerCamelCase					:		Dict		   =     bbox[3] * scale_x
																												__lowerCamelCase					:		Tuple		   =     scale_y + bbox[4] * (1 - scale_y)
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
														else:  # bottom-right
																					__lowerCamelCase					:		int		   =     cva.resize(
																					    _lowerCamelCase						,   (output_size[1] - divid_point_x, output_size[0] - divid_point_y)      )
																					__lowerCamelCase					:		Optional[Any]		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		Union[str, Any]		   =     scale_x + bbox[1] * (1 - scale_x)
																												__lowerCamelCase					:		Union[str, Any]		   =     scale_y + bbox[2] * (1 - scale_y)
																												__lowerCamelCase					:		int		   =     scale_x + bbox[3] * (1 - scale_x)
																												__lowerCamelCase					:		int		   =     scale_y + bbox[4] * (1 - scale_y)
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )

    # Remove bounding box small than scale of filter
							if filter_scale > 0:
														__lowerCamelCase					:		str		   =     [
														    anno
														    for anno in new_anno
														    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
														]

							return output_img, new_anno, path_list[0]


def    lowercase_   (   _lowerCamelCase:	int      ) ->      str:



							'''simple docstring'''

							assert number_char > 1, "The number of character should greater than 1"
							__lowerCamelCase					:		Tuple		   =     ascii_lowercase + digits
							return "".join(random.choice(_lowerCamelCase      ) for _ in range(_lowerCamelCase      )      )


if __name__ == "__main__":
				main()
				print('''DONE ✅''')