




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()
__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

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

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE_   	=    Image.open(requests.get(_lowerCAmelCase  ,    stream=_lowerCAmelCase     ).raw     )

							return image

@torch.no_grad()
def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase=False  ,    _lowerCAmelCase=None     ):
							SCREAMING_SNAKE_CASE_   	=    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)
							SCREAMING_SNAKE_CASE_   	=    GLPNImageProcessor()

							# prepare image
							SCREAMING_SNAKE_CASE_   	=    prepare_img()
							SCREAMING_SNAKE_CASE_   	=    image_processor(images=_lowerCAmelCase  ,    return_tensors='''pt'''     ).pixel_values

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

							# load original state dict
							SCREAMING_SNAKE_CASE_   	=    torch.load(_lowerCAmelCase  ,    map_location=torch.device('''cpu'''     )     )

							# rename keys
							SCREAMING_SNAKE_CASE_   	=    rename_keys(_lowerCAmelCase     )

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

							# create HuggingFace model and load state dict
							SCREAMING_SNAKE_CASE_   	=    GLPNForDepthEstimation(_lowerCAmelCase     )
							model.load_state_dict(_lowerCAmelCase     )
							model.eval()

							# forward pass
							SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    outputs.predicted_depth

							# verify output
							if model_name is not None:
														if "nyu" in model_name:
																					SCREAMING_SNAKE_CASE_   	=    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:
																					SCREAMING_SNAKE_CASE_   	=    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}"     )

														SCREAMING_SNAKE_CASE_   	=    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__":
			__SCREAMING_SNAKE_CASE 						=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.""",
			)
			__SCREAMING_SNAKE_CASE 						=parser.parse_args()
			convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    [], []
							while len(_lowerCAmelCase     ) > 1:
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    min(_lowerCAmelCase     ), max(_lowerCAmelCase     )
														start.append(_lowerCAmelCase     )
														end.append(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
							end.reverse()
							return start + collection + end


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=input("""Enter numbers separated by a comma:\n""").strip()
			__SCREAMING_SNAKE_CASE 						=[int(item) for item in user_input.split(""",""")]
			print(*merge_sort(unsorted), sep=""",""")


