"""simple docstring"""







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    (					lowercase		):
				UpperCAmelCase       :   int					=	0
				UpperCAmelCase       :   bool					=	False
				UpperCAmelCase       :   float					=	3.0







class 	UpperCamelCase    (					unittest.TestCase		):



				def       _lowercase  (self       :   Optional[Any])					->			Tuple:
					# 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=2.25).to_kwargs()			,						{'a': 2, 'c': 2.25})



				@require_cuda
				def       _lowercase  (self       :   Union[str, Any])					->			Union[str, Any]:
					# If no defaults are changed, `to_kwargs` returns an empty dict.
					__snake_case    :							Any   	=				GradScalerKwargs(init_scale=10_24			,						growth_factor=2)
					AcceleratorState._reset_state()
					__snake_case    :							List[str]   	=				Accelerator(mixed_precision='fp16'			,						kwargs_handlers=[scaler_handler])
					print(accelerator.use_fpaa)
					__snake_case    :							Dict   	=				accelerator.scaler

					# Check the kwargs have been applied
					self.assertEqual(scaler._init_scale			,						1_024.0)
					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			,						20_00)
					self.assertEqual(scaler._enabled			,						_A)






				@require_multi_gpu
				def       _lowercase  (self       :   str)					->			Union[str, Any]:
					__snake_case    :							Any   	=				['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__)]
					execute_subprocess_async(_A			,						env=os.environ.copy())


if __name__ == "__main__":
	_a  :  List[str]=				DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
	_a  :  Optional[int]=				Accelerator(kwargs_handlers=[ddp_scaler])
	_a  :  Optional[Any]=				torch.nn.Linear(100, 200)
	_a  :  Tuple=				accelerator.prepare(model)

	# Check the values changed in kwargs
	_a  :  str=				""
	_a  :  List[Any]=				model.bucket_bytes_cap // (1_024 * 1_024)
	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)






"""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()
_a  :  Union[str, Any]=				logging.get_logger(__name__)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    List[Any]:

	'''simple docstring'''





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

	return new_state_dict
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]    ,							UpperCAmelCase_					:       List[Any]						)							->    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)
			__snake_case    :							Union[str, Any]   	=				state_dict.pop(F"glpn.encoder.block.{i}.{j}.attention.self.kv.weight"						)
			__snake_case    :							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
			__snake_case    :							int   	=				kv_weight[
			    : config.hidden_sizes[i], :
			]
			__snake_case    :							List[str]   	=				kv_bias[: config.hidden_sizes[i]]
			__snake_case    :							Dict   	=				kv_weight[
			    config.hidden_sizes[i] :, :
			]
			__snake_case    :							Any   	=				kv_bias[config.hidden_sizes[i] :]
def 						__UpperCAmelCase       (					)							->    Any:

	'''simple docstring'''





	__snake_case    :							Tuple   	=				'http://images.cocodataset.org/val2017/000000039769.jpg'
	__snake_case    :							Tuple   	=				Image.open(requests.get(UpperCAmelCase_    ,							stream=UpperCAmelCase_						).raw						)

	return image
@torch.no_grad()
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       List[str]    ,							UpperCAmelCase_					:       Dict=False    ,							UpperCAmelCase_					:       Optional[int]=None						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Dict   	=				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)
	__snake_case    :							Tuple   	=				GLPNImageProcessor()

	# prepare image
	__snake_case    :							Optional[Any]   	=				prepare_img()
	__snake_case    :							Any   	=				image_processor(images=UpperCAmelCase_    ,							return_tensors='pt'						).pixel_values

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

	# load original state dict
	__snake_case    :							Dict   	=				torch.load(UpperCAmelCase_    ,							map_location=torch.device('cpu'						)						)

	# rename keys
	__snake_case    :							Union[str, Any]   	=				rename_keys(UpperCAmelCase_						)

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

	# create HuggingFace model and load state dict
	__snake_case    :							Optional[int]   	=				GLPNForDepthEstimation(UpperCAmelCase_						)
	model.load_state_dict(UpperCAmelCase_						)
	model.eval()

	# forward pass
	__snake_case    :							List[Any]   	=				model(UpperCAmelCase_						)
	__snake_case    :							Optional[int]   	=				outputs.predicted_depth

	# verify output
	if model_name is not None:
		if "nyu" in model_name:
			__snake_case    :							int   	=				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:
			__snake_case    :							Union[str, Any]   	=				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}"						)

		__snake_case    :							Tuple   	=				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__":
	_a  :  List[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.",
	)
	_a  :  int=				parser.parse_args()
	convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





