
"""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       =  logging.get_logger(__name__)






def 						_snake_case       (	lowercase__		:  Optional[Any]		)  ->				str:


			'''simple docstring'''





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

			return new_state_dict






def 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  int		)  ->				str:


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






def 						_snake_case       (	)  ->				Any:


			'''simple docstring'''





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

			return image






@torch.no_grad()
def 						_snake_case       (	lowercase__		:  List[Any]      ,     lowercase__		:  str      ,     lowercase__		:  Dict=False      ,     lowercase__		:  List[Any]=None		)  ->				int:


			'''simple docstring'''





			lowerCAmelCase_    :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_    :Union[str, Any]			     =  GLPNImageProcessor()

			# prepare image
			lowerCAmelCase_    :List[Any]			     =  prepare_img()
			lowerCAmelCase_    :int			     =  image_processor(images=lowercase__      ,     return_tensors="""pt"""		).pixel_values

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

			# load original state dict
			lowerCAmelCase_    :Tuple			     =  torch.load(lowercase__      ,     map_location=torch.device("""cpu"""		)		)

			# rename keys
			lowerCAmelCase_    :Union[str, Any]			     =  rename_keys(lowercase__		)

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

			# create HuggingFace model and load state dict
			lowerCAmelCase_    :List[Any]			     =  GLPNForDepthEstimation(lowercase__		)
			model.load_state_dict(lowercase__		)
			model.eval()

			# forward pass
			lowerCAmelCase_    :Dict			     =  model(lowercase__		)
			lowerCAmelCase_    :Tuple			     =  outputs.predicted_depth

			# verify output
			if model_name is not None:
						if "nyu" in model_name:
									lowerCAmelCase_    :Optional[Any]			     =  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_    :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, 4_8_0, 6_4_0]		)

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


if __name__ == "__main__":
				__UpperCAmelCase       =  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       =  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 shlex

import runhouse as rh


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
      # setup instructions, if using on-demand hardware

      # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
      # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
      # Throw an error if user passes both BYO and on-demand cluster args
      # Otherwise, use default values
      lowercase_					= argparse.ArgumentParser()
      parser.add_argument("""--user""", type=str, default="""ubuntu""")
      parser.add_argument("""--host""", type=str, default="""localhost""")
      parser.add_argument("""--key_path""", type=str, default=None)
      parser.add_argument("""--instance""", type=str, default="""V100:1""")
      parser.add_argument("""--provider""", type=str, default="""cheapest""")
      parser.add_argument("""--use_spot""", type=bool, default=False)
      parser.add_argument("""--example""", type=str, default="""pytorch/text-generation/run_generation.py""")
      lowercase_				,			lowercase_					= parser.parse_known_args()
      if args.host != "localhost":
            if args.instance != "V100:1" or args.provider != "cheapest":
                  raise ValueError("""Cannot specify both BYO and on-demand cluster args""")
            lowercase_					= rh.cluster(
                name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
            )
      else:
            lowercase_					= rh.cluster(
                name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
            )
      lowercase_					= args.example.rsplit("""/""", 1)[0]

      # Set up remote environment
      cluster.install_packages(["""pip:./"""])  # Installs transformers from local source
      # Note transformers is copied into the home directory on the remote machine, so we can install from there
      cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
      cluster.run(["""pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"""])

      # Run example. You can bypass the CLI wrapper and paste your own code here.
      cluster.run([f"""python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}"""])

      # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
      # from my_script... import train
      # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
      # launch_train_gpu = rh.function(fn=train,
      #                                system=gpu,
      #                                reqs=reqs,
      #                                name='train_bert_glue')
      #
      # We can pass in arguments just like we would to a function:
      # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
      #                  stream_logs=True)



