





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 lowerCAmelCase__(__snake_case							)       ->   Tuple:







	'''simple docstring'''







	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 lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Union[str, 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__              =	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 lowerCAmelCase__()       ->   Optional[Any]:







	'''simple docstring'''







	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 lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case=False ,__snake_case=None							)       ->   int:







	'''simple docstring'''







	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(
			    [[4.4_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__              =	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__              =	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__":
			_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)










import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_a						=   logging.get_logger(__name__)






class       __A					:

			'''simple docstring'''







			lowerCAmelCase_							  =		None





@experimental
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Tuple:







	'''simple docstring'''







	if ParallelBackendConfig.backend_name is None:
		return _map_with_multiprocessing_pool(
		    __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)

	return _map_with_joblib(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	num_proc if num_proc <= len(__snake_case							) else len(__snake_case							)
	lowerCamelCase__              =	[]  # We organize the splits ourselve (contiguous splits)
	for index in range(__snake_case							):
		lowerCamelCase__              =	len(__snake_case							) // num_proc
		lowerCamelCase__              =	len(__snake_case							) % num_proc
		lowerCamelCase__              =	div * index + min(__snake_case ,__snake_case							)
		lowerCamelCase__              =	start + div + (1 if index < mod else 0)
		split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)							)

	if len(__snake_case							) != sum(len(i[1]							) for i in split_kwds							):
		raise ValueError(
		    F'Error dividing inputs iterable among processes. '
		    F'Total number of objects {len(__snake_case							)}, '
		    F'length: {sum(len(i[1]							) for i in split_kwds							)}'							)

	logger.info(
	    F'Spawning {num_proc} processes for {len(__snake_case							)} objects in slices of {[len(i[1]							) for i in split_kwds]}'							)
	lowerCamelCase__       ,			lowerCamelCase__              =	None, None
	if not disable_tqdm:
		lowerCamelCase__       ,			lowerCamelCase__              =	(RLock(),), tqdm.set_lock
	with Pool(__snake_case ,initargs=__snake_case ,initializer=__snake_case							) as pool:
		lowerCamelCase__              =	pool.map(__snake_case ,__snake_case							)
	logger.info(F'Finished {num_proc} processes'							)
	lowerCamelCase__              =	[obj for proc_res in mapped for obj in proc_res]
	logger.info(F'Unpacked {len(__snake_case							)} objects'							)

	return mapped





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   List[str]:







	'''simple docstring'''







	import joblib

	with joblib.parallel_backend(ParallelBackendConfig.backend_name ,n_jobs=__snake_case							):
		return joblib.Parallel()(
		    joblib.delayed(__snake_case							)((function, obj, types, None, True, None)							) for obj in iterable							)





@experimental
@contextlib.contextmanager
def lowerCAmelCase__(__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	backend_name

	if backend_name == "spark":
		from joblibspark import register_spark

		register_spark()

		# TODO: call create_cache_and_write_probe if "download" in steps
		# TODO: raise NotImplementedError when Dataset.map etc is called

	try:
		yield
	finally:
		lowerCamelCase__              =	None



