

"""simple docstring"""


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
from ..auto import CONFIG_MAPPING


__A						   =			logging.get_logger(__name__)

__A						   =			{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}





class     snake_case	(				__lowercase  ):
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  '''table-transformer'''
				SCREAMING_SNAKE_CASE_   :      List[str]         =  ['''past_key_values''']
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  {
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}

				def __init__(	self							:				List[str]     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				List[Any]=None     ,  UpperCamelCase__							:				Union[str, Any]=3     ,  UpperCamelCase__							:				Tuple=1_0_0     ,  UpperCamelCase__							:				Any=6     ,  UpperCamelCase__							:				int=2_0_4_8     ,  UpperCamelCase__							:				List[str]=8     ,  UpperCamelCase__							:				Union[str, Any]=6     ,  UpperCamelCase__							:				Tuple=2_0_4_8     ,  UpperCamelCase__							:				int=8     ,  UpperCamelCase__							:				str=0.0     ,  UpperCamelCase__							:				str=0.0     ,  UpperCamelCase__							:				Optional[int]=True     ,  UpperCamelCase__							:				List[Any]="relu"     ,  UpperCamelCase__							:				Dict=2_5_6     ,  UpperCamelCase__							:				Any=0.1     ,  UpperCamelCase__							:				Union[str, Any]=0.0     ,  UpperCamelCase__							:				str=0.0     ,  UpperCamelCase__							:				Optional[Any]=0.02     ,  UpperCamelCase__							:				Union[str, Any]=1.0     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				List[Any]="sine"     ,  UpperCamelCase__							:				Optional[int]="resnet50"     ,  UpperCamelCase__							:				Union[str, Any]=True     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				int=1     ,  UpperCamelCase__							:				Dict=5     ,  UpperCamelCase__							:				Tuple=2     ,  UpperCamelCase__							:				List[Any]=1     ,  UpperCamelCase__							:				Optional[int]=1     ,  UpperCamelCase__							:				Dict=5     ,  UpperCamelCase__							:				List[Any]=2     ,  UpperCamelCase__							:				List[Any]=0.1     ,  **UpperCamelCase__							:				Union[str, Any]     ,  )->   Optional[int]:



						'''simple docstring'''


						if backbone_config is not None and use_timm_backbone:
								raise ValueError("You can\'t specify both `backbone_config` and `use_timm_backbone`.")

						if not use_timm_backbone:
								if backbone_config is None:
										logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
										__lowerCAmelCase:       Optional[int]          =			CONFIG_MAPPING["resnet"](out_features=["stage4"])
								elif isinstance(UpperCAmelCase__     ,  UpperCAmelCase__):
										__lowerCAmelCase:       str          =			backbone_config.get("model_type")
										__lowerCAmelCase:       Union[str, Any]          =			CONFIG_MAPPING[backbone_model_type]
										__lowerCAmelCase:       List[Any]          =			config_class.from_dict(UpperCAmelCase__)
								# set timm attributes to None
								__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       int          =			None, None, None

						__lowerCAmelCase:       Union[str, Any]          =			use_timm_backbone
						__lowerCAmelCase:       List[Any]          =			backbone_config
						__lowerCAmelCase:       Union[str, Any]          =			num_channels
						__lowerCAmelCase:       List[Any]          =			num_queries
						__lowerCAmelCase:       Tuple          =			d_model
						__lowerCAmelCase:       Any          =			encoder_ffn_dim
						__lowerCAmelCase:       Optional[Any]          =			encoder_layers
						__lowerCAmelCase:       Any          =			encoder_attention_heads
						__lowerCAmelCase:       Optional[int]          =			decoder_ffn_dim
						__lowerCAmelCase:       str          =			decoder_layers
						__lowerCAmelCase:       Union[str, Any]          =			decoder_attention_heads
						__lowerCAmelCase:       Dict          =			dropout
						__lowerCAmelCase:       Optional[int]          =			attention_dropout
						__lowerCAmelCase:       Union[str, Any]          =			activation_dropout
						__lowerCAmelCase:       str          =			activation_function
						__lowerCAmelCase:       Optional[Any]          =			init_std
						__lowerCAmelCase:       List[Any]          =			init_xavier_std
						__lowerCAmelCase:       List[Any]          =			encoder_layerdrop
						__lowerCAmelCase:       int          =			decoder_layerdrop
						__lowerCAmelCase:       Tuple          =			encoder_layers
						__lowerCAmelCase:       str          =			auxiliary_loss
						__lowerCAmelCase:       Dict          =			position_embedding_type
						__lowerCAmelCase:       Optional[int]          =			backbone
						__lowerCAmelCase:       List[Any]          =			use_pretrained_backbone
						__lowerCAmelCase:       str          =			dilation
						# Hungarian matcher
						__lowerCAmelCase:       List[str]          =			class_cost
						__lowerCAmelCase:       Union[str, Any]          =			bbox_cost
						__lowerCAmelCase:       Optional[int]          =			giou_cost
						# Loss coefficients
						__lowerCAmelCase:       Dict          =			mask_loss_coefficient
						__lowerCAmelCase:       List[Any]          =			dice_loss_coefficient
						__lowerCAmelCase:       str          =			bbox_loss_coefficient
						__lowerCAmelCase:       Optional[int]          =			giou_loss_coefficient
						__lowerCAmelCase:       Union[str, Any]          =			eos_coefficient
						super().__init__(is_encoder_decoder=UpperCAmelCase__     ,  **UpperCAmelCase__)

				@property
				def 							lowercase_     (	self							:				int)->   int:



						'''simple docstring'''


						return self.encoder_attention_heads




				@property
				def 							lowercase_     (	self							:				Optional[Any])->   int:



						'''simple docstring'''


						return self.d_model





class     snake_case	(				__lowercase  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  version.parse("""1.11"""  )

				@property
				def 							lowercase_     (	self							:				Any)->   Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						        ("pixel_mask", {0: "batch"}),
						    ])

				@property
				def 							lowercase_     (	self							:				Optional[int])->   float:



						'''simple docstring'''


						return 1e-5




				@property
				def 							lowercase_     (	self							:				Optional[int])->   int:



						'''simple docstring'''


						return 1_2






'''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()
__snake_case    						=logging.get_logger(__name__)





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

   return new_state_dict





def 	a_					( lowerCamelCase	:     List[str]	, lowerCamelCase	:     str				):
   # 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)
         lowerCAmelCase     =			state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''				)
         lowerCAmelCase     =			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     =			kv_weight[
             : config.hidden_sizes[i], :
         ]
         lowerCAmelCase     =			kv_bias[: config.hidden_sizes[i]]
         lowerCAmelCase     =			kv_weight[
             config.hidden_sizes[i] :, :
         ]
         lowerCAmelCase     =			kv_bias[config.hidden_sizes[i] :]





def 	a_					( ):
   lowerCAmelCase     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
   lowerCAmelCase     =			Image.open(requests.get(lowerCamelCase	, stream=lowerCamelCase				).raw				)

   return image





@torch.no_grad()
def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Tuple	, lowerCamelCase	:     Optional[Any]=False	, lowerCamelCase	:     List[str]=None				):
   lowerCAmelCase     =			GLPNConfig(hidden_sizes=[64, 128, 320, 512]	, decoder_hidden_size=64	, depths=[3, 8, 27, 3]				)

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

   # prepare image
   lowerCAmelCase     =			prepare_img()
   lowerCAmelCase     =			image_processor(images=lowerCamelCase	, return_tensors='pt'				).pixel_values

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

   # load original state dict
   lowerCAmelCase     =			torch.load(lowerCamelCase	, map_location=torch.device('cpu'				)				)

   # rename keys
   lowerCAmelCase     =			rename_keys(lowerCamelCase				)

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

   # create HuggingFace model and load state dict
   lowerCAmelCase     =			GLPNForDepthEstimation(lowerCamelCase				)
   model.load_state_dict(lowerCamelCase				)
   model.eval()

   # forward pass
   lowerCAmelCase     =			model(lowerCamelCase				)
   lowerCAmelCase     =			outputs.predicted_depth

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

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

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


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





