






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 	__lowerCamelCase      (		snake_case__		)							->  List[Any]:






						"""simple docstring"""





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

						return new_state_dict

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Optional[int]:






						"""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)
																		_SCREAMING_SNAKE_CASE     				=  state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'		)
																		_SCREAMING_SNAKE_CASE     				=  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
																		_SCREAMING_SNAKE_CASE     				=  kv_weight[
																		    : config.hidden_sizes[i], :
																		]
																		_SCREAMING_SNAKE_CASE     				=  kv_bias[: config.hidden_sizes[i]]
																		_SCREAMING_SNAKE_CASE     				=  kv_weight[
																		    config.hidden_sizes[i] :, :
																		]
																		_SCREAMING_SNAKE_CASE     				=  kv_bias[config.hidden_sizes[i] :]

def 	__lowerCamelCase      (		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						_SCREAMING_SNAKE_CASE     				=  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		)							->  Any:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  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)
						_SCREAMING_SNAKE_CASE     				=  GLPNImageProcessor()

						# prepare image
						_SCREAMING_SNAKE_CASE     				=  prepare_img()
						_SCREAMING_SNAKE_CASE     				=  image_processor(images=snake_case__	,return_tensors="""pt"""		).pixel_values

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

						# load original state dict
						_SCREAMING_SNAKE_CASE     				=  torch.load(snake_case__	,map_location=torch.device("""cpu"""		)		)

						# rename keys
						_SCREAMING_SNAKE_CASE     				=  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
						_SCREAMING_SNAKE_CASE     				=  GLPNForDepthEstimation(snake_case__		)
						model.load_state_dict(snake_case__		)
						model.eval()

						# forward pass
						_SCREAMING_SNAKE_CASE     				=  model(snake_case__		)
						_SCREAMING_SNAKE_CASE     				=  outputs.predicted_depth

						# verify output
						if model_name is not None:
												if "nyu" in model_name:
																		_SCREAMING_SNAKE_CASE     				=  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:
																		_SCREAMING_SNAKE_CASE     				=  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}'		)

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

												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__":
		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)









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Any									=							"mobilenet_v1"







			def __init__(     self:  List[str]				,     UpperCAmelCase_:  List[Any]=3				,     UpperCAmelCase_:  List[str]=224				,     UpperCAmelCase_:  Optional[Any]=1.0				,     UpperCAmelCase_:  Dict=8				,     UpperCAmelCase_:  List[str]="relu6"				,     UpperCAmelCase_:  List[Any]=True				,     UpperCAmelCase_:  Dict=0.9_99				,     UpperCAmelCase_:  Union[str, Any]=0.02				,     UpperCAmelCase_:  Tuple=0.0_01				,     **UpperCAmelCase_:  List[str]				,     ):







									'''simple docstring'''




									super().__init__(**UpperCAmelCase_					)

									if depth_multiplier <= 0:
															raise ValueError("""depth_multiplier must be greater than zero."""					)

									_SCREAMING_SNAKE_CASE     				=  num_channels
									_SCREAMING_SNAKE_CASE     				=  image_size
									_SCREAMING_SNAKE_CASE     				=  depth_multiplier
									_SCREAMING_SNAKE_CASE     				=  min_depth
									_SCREAMING_SNAKE_CASE     				=  hidden_act
									_SCREAMING_SNAKE_CASE     				=  tf_padding
									_SCREAMING_SNAKE_CASE     				=  classifier_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  initializer_range
									_SCREAMING_SNAKE_CASE     				=  layer_norm_eps



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				List[Any]									=							version.parse("1.11"   )







			@property
			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									return OrderedDict([("""pixel_values""", {0: """batch"""})]					)







			@property
			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									if self.task == "image-classification":
															return OrderedDict([("""logits""", {0: """batch"""})]					)
									else:
															return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]					)







			@property
			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									return 1E-4

