

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()
__lowerCAmelCase			     =       logging.get_logger(__name__)



def    _lowercase			(	a__						:			List[str]					)							->					List[Any]:





	"""simple docstring"""


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

	return new_state_dict



def    _lowercase			(	a__						:			Optional[Any]							,  a__						:			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)
			_UpperCamelCase			=   state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''					)
			_UpperCamelCase			=   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
			_UpperCamelCase			=   kv_weight[
			    : config.hidden_sizes[i], :
			]
			_UpperCamelCase			=   kv_bias[: config.hidden_sizes[i]]
			_UpperCamelCase			=   kv_weight[
			    config.hidden_sizes[i] :, :
			]
			_UpperCamelCase			=   kv_bias[config.hidden_sizes[i] :]



def    _lowercase			(	)							->					str:





	"""simple docstring"""


	_UpperCamelCase			=   "http://images.cocodataset.org/val2017/000000039769.jpg"
	_UpperCamelCase			=   Image.open(requests.get(UpperCamelCase_							,  stream=UpperCamelCase_					).raw					)

	return image



@torch.no_grad()
def    _lowercase			(	a__						:			Union[str, Any]							,  a__						:			Optional[int]							,  a__						:			Any=False							,  a__						:			int=None					)							->					Dict:





	"""simple docstring"""


	_UpperCamelCase			=   GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]							,  decoder_hidden_size=64							,  depths=[3, 8, 27, 3]					)

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

	# prepare image
	_UpperCamelCase			=   prepare_img()
	_UpperCamelCase			=   image_processor(images=UpperCamelCase_							,  return_tensors="pt"					).pixel_values

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

	# load original state dict
	_UpperCamelCase			=   torch.load(UpperCamelCase_							,  map_location=torch.device("cpu"					)					)

	# rename keys
	_UpperCamelCase			=   rename_keys(UpperCamelCase_					)

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

	# create HuggingFace model and load state dict
	_UpperCamelCase			=   GLPNForDepthEstimation(UpperCamelCase_					)
	model.load_state_dict(UpperCamelCase_					)
	model.eval()

	# forward pass
	_UpperCamelCase			=   model(UpperCamelCase_					)
	_UpperCamelCase			=   outputs.predicted_depth

	# verify output
	if model_name is not None:
		if "nyu" in model_name:
			_UpperCamelCase			=   torch.tensor(
			    [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]					)
		elif "kitti" in model_name:
			_UpperCamelCase			=   torch.tensor(
			    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]					)
		else:
			raise ValueError(f'''Unknown model name: {model_name}'''					)

		_UpperCamelCase			=   torch.Size([1, 4_80, 6_40]					)

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


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









import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					0
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					3.0







class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()			,    {})
         self.assertDictEqual(MockClass(a=2).to_kwargs()			,    {'''a''': 2})
         self.assertDictEqual(MockClass(a=2			,    b=a__).to_kwargs()			,    {'''a''': 2, '''b''': True})
         self.assertDictEqual(MockClass(a=2			,    c=2.2_5).to_kwargs()			,    {'''a''': 2, '''c''': 2.2_5})




     @require_cuda
     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         A__					=       GradScalerKwargs(init_scale=1_0_2_4			,    growth_factor=2)
         AcceleratorState._reset_state()
         A__					=       Accelerator(mixed_precision='''fp16'''			,    kwargs_handlers=[scaler_handler])
         print(accelerator.use_fpaa)
         A__					=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale			,    1_0_2_4.0)
         self.assertEqual(scaler._growth_factor			,    2.0)

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor			,    0.5)
         self.assertEqual(scaler._growth_interval			,    2_0_0_0)
         self.assertEqual(scaler._enabled			,    a__)




     @require_multi_gpu
     def 		snake_case_		(					self):
         A__					=       ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__)]
         execute_subprocess_async(a__			,    env=os.environ.copy())


if __name__ == "__main__":
   _lowercase    	=		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _lowercase    	=		Accelerator(kwargs_handlers=[ddp_scaler])
   _lowercase    	=		torch.nn.Linear(100, 200)
   _lowercase    	=		accelerator.prepare(model)

   # Check the values changed in kwargs
   _lowercase    	=		""
   _lowercase    	=		model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)
