





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()
snake_case_							=			logging.get_logger(__name__)


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

		return new_state_dict


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     str:







		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   : List[str]								=state_dict.pop(F"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"		)
						lowerCamelCase   : Union[str, Any]								=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   : Optional[int]								=kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowerCamelCase   : Union[str, Any]								=kv_bias[: config.hidden_sizes[i]]
						lowerCamelCase   : Tuple								=kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowerCamelCase   : Any								=kv_bias[config.hidden_sizes[i] :]


def 					A__	(       )   ->     Optional[Any]:
		lowerCamelCase   : str								="http://images.cocodataset.org/val2017/000000039769.jpg"
		lowerCamelCase   : Dict								=Image.open(requests.get(SCREAMING_SNAKE_CASE_							,       stream=SCREAMING_SNAKE_CASE_		).raw		)

		return image


@torch.no_grad()
def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=None		)   ->     Dict:
		lowerCamelCase   : Optional[int]								=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)
		lowerCamelCase   : Dict								=GLPNImageProcessor()

		# prepare image
		lowerCamelCase   : List[str]								=prepare_img()
		lowerCamelCase   : Dict								=image_processor(images=SCREAMING_SNAKE_CASE_							,       return_tensors='''pt'''		).pixel_values

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

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

		# rename keys
		lowerCamelCase   : Tuple								=rename_keys(SCREAMING_SNAKE_CASE_		)

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

		# create HuggingFace model and load state dict
		lowerCamelCase   : Optional[Any]								=GLPNForDepthEstimation(SCREAMING_SNAKE_CASE_		)
		model.load_state_dict(SCREAMING_SNAKE_CASE_		)
		model.eval()

		# forward pass
		lowerCamelCase   : int								=model(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : Optional[Any]								=outputs.predicted_depth

		# verify output
		if model_name is not None:
				if "nyu" in model_name:
						lowerCamelCase   : Any								=torch.tensor(
						    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]		)
				elif "kitti" in model_name:
						lowerCamelCase   : Tuple								=torch.tensor(
						    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]		)
				else:
						raise ValueError(F"Unknown model name: {model_name}"		)

				lowerCamelCase   : Any								=torch.Size([1, 4_8_0, 6_4_0]		)

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


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








from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class        snake_case_		(			_A):







		def __init__(		self			,     __lowercase = None			,     __lowercase = None			,     __lowercase = None			,     __lowercase = None			,     __lowercase = False			,     __lowercase = False			,     __lowercase = None			,     **__lowercase			,     )				->   List[Any]:
				lowerCamelCase   : Dict								=path_or_paths
				lowerCamelCase   : Optional[int]								=split if split or isinstance(__lowercase			,     __lowercase   ) else '''train'''
				lowerCamelCase   : Tuple								=features
				lowerCamelCase   : Tuple								=cache_dir
				lowerCamelCase   : Optional[Any]								=keep_in_memory
				lowerCamelCase   : Any								=streaming
				lowerCamelCase   : Dict								=num_proc
				lowerCamelCase   : List[Any]								=kwargs







		@abstractmethod
		def 			__lowercase   (		self   )				->   Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
				pass

class        snake_case_		(			_A):







		def __init__(		self			,     __lowercase = None			,     __lowercase = None			,     __lowercase = False			,     __lowercase = False			,     __lowercase = None			,     **__lowercase			,     )				->   List[str]:
				lowerCamelCase   : Tuple								=features
				lowerCamelCase   : Union[str, Any]								=cache_dir
				lowerCamelCase   : Optional[int]								=keep_in_memory
				lowerCamelCase   : List[str]								=streaming
				lowerCamelCase   : List[str]								=num_proc
				lowerCamelCase   : List[str]								=kwargs







		@abstractmethod
		def 			__lowercase   (		self   )				->   Union[Dataset, IterableDataset]:
				pass

