

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 							A     (					_UpperCAmelCase    :       Tuple   )    ->			int:







			'''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 							A     (					_UpperCAmelCase    :       Dict							,       _UpperCAmelCase    :       Union[str, Any]   )    ->			Dict:







			'''simple docstring'''
			# for each of the encoder blocks:
			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 							A     (					)    ->			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 							A     (					_UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Optional[Any]=False							,       _UpperCAmelCase    :       List[Any]=None   )    ->			Any:







			'''simple docstring'''
			_UpperCAmelCase			=       GLPNConfig(hidden_sizes=[64, 128, 320, 512]							,       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(
									    [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]   )
						elif "kitti" in model_name:
									_UpperCAmelCase			=       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}"   )

						_UpperCAmelCase			=       torch.Size([1, 480, 640]   )

						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__":
							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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["DebertaTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DebertaForMaskedLM",
							    "DebertaForQuestionAnswering",
							    "DebertaForSequenceClassification",
							    "DebertaForTokenClassification",
							    "DebertaModel",
							    "DebertaPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFDebertaForMaskedLM",
							    "TFDebertaForQuestionAnswering",
							    "TFDebertaForSequenceClassification",
							    "TFDebertaForTokenClassification",
							    "TFDebertaModel",
							    "TFDebertaPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
							from .tokenization_deberta import DebertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_fast import DebertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta import (
														    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaForMaskedLM,
														    DebertaForQuestionAnswering,
														    DebertaForSequenceClassification,
														    DebertaForTokenClassification,
														    DebertaModel,
														    DebertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta import (
														    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaForMaskedLM,
														    TFDebertaForQuestionAnswering,
														    TFDebertaForSequenceClassification,
														    TFDebertaForTokenClassification,
														    TFDebertaModel,
														    TFDebertaPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
