






'''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()
UpperCamelCase    :      int  			=		logging.get_logger(__name__)


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

						return new_state_dict


def 						A__			(				__lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			Union[str, Any]						):


						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__         =  state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''						)
																		lowerCamelCase__         =  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__         =  kv_weight[
																		    : config.hidden_sizes[i], :
																		]
																		lowerCamelCase__         =  kv_bias[: config.hidden_sizes[i]]
																		lowerCamelCase__         =  kv_weight[
																		    config.hidden_sizes[i] :, :
																		]
																		lowerCamelCase__         =  kv_bias[config.hidden_sizes[i] :]


def 						A__			(				):
						lowerCamelCase__         =  "http://images.cocodataset.org/val2017/000000039769.jpg"
						lowerCamelCase__         =  Image.open(requests.get(__snake_case   ,    stream=__snake_case						).raw						)

						return image


@torch.no_grad()
def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			Dict=False   ,    __lowerCAmelCase		:			Tuple=None						):
						lowerCamelCase__         =  GLPNConfig(hidden_sizes=[64, 128, 320, 512]   ,    decoder_hidden_size=64   ,    depths=[3, 8, 27, 3]						)

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

						# prepare image
						lowerCamelCase__         =  prepare_img()
						lowerCamelCase__         =  image_processor(images=__snake_case   ,    return_tensors="""pt"""						).pixel_values

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

						# load original state dict
						lowerCamelCase__         =  torch.load(__snake_case   ,    map_location=torch.device("""cpu"""						)						)

						# rename keys
						lowerCamelCase__         =  rename_keys(__snake_case						)

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

						# create HuggingFace model and load state dict
						lowerCamelCase__         =  GLPNForDepthEstimation(__snake_case						)
						model.load_state_dict(__snake_case						)
						model.eval()

						# forward pass
						lowerCamelCase__         =  model(__snake_case						)
						lowerCamelCase__         =  outputs.predicted_depth

						# verify output
						if model_name is not None:
												if "nyu" in model_name:
																		lowerCamelCase__         =  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__         =  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__         =  torch.Size([1, 480, 640]						)

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


if __name__ == "__main__":
							UpperCamelCase    :      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.',
							)
							UpperCamelCase    :      str  			=		parser.parse_args()
							convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









'''simple docstring'''







import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase    :      Optional[Any]  			=		['small', 'medium', 'large']

UpperCamelCase    :      Dict  			=		'lm_head.decoder.weight'
UpperCamelCase    :      int  			=		'lm_head.weight'
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  torch.load(__lowerCAmelCase						)
						lowerCamelCase__         =  d.pop(__lowerCAmelCase						)
						os.makedirs(__lowerCAmelCase   ,    exist_ok=__lowerCAmelCase						)
						torch.save(__lowerCAmelCase   ,    os.path.join(__lowerCAmelCase   ,    __lowerCAmelCase						)						)


if __name__ == "__main__":
							UpperCamelCase    :      Any  			=		argparse.ArgumentParser()
							parser.add_argument('--dialogpt_path', default='.', type=str)
							UpperCamelCase    :      Dict  			=		parser.parse_args()
							for MODEL in DIALOGPT_MODELS:
														UpperCamelCase    :      Any  			=		os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
														UpperCamelCase    :      str  			=		F'./DialoGPT-{MODEL}'
														convert_dialogpt_checkpoint(
														    checkpoint_path,
														    pytorch_dump_folder_path,
														)

