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()
_lowercase				:   Optional[Any]   				=logging.get_logger(__name__)



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

       return new_state_dict



def A__	(      lowercase:  Union[str, Any],		lowercase:  Any       )					->		Optional[int]:
       # 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)
                     A    :  str								=state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'       )
                     A    :  Optional[int]								=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
                     A    :  List[Any]								=kv_weight[
                         : config.hidden_sizes[i], :
                     ]
                     A    :  Dict								=kv_bias[: config.hidden_sizes[i]]
                     A    :  Optional[Any]								=kv_weight[
                         config.hidden_sizes[i] :, :
                     ]
                     A    :  str								=kv_bias[config.hidden_sizes[i] :]



def A__	(      )					->		Optional[int]:
       A    :  Union[str, Any]								="http://images.cocodataset.org/val2017/000000039769.jpg"
       A    :  Optional[int]								=Image.open(requests.get(_lowerCAmelCase,		stream=_lowerCAmelCase       ).raw       )

       return image



@torch.no_grad()
def A__	(      lowercase:  int,		lowercase:  Optional[int],		lowercase:  Optional[Any]=False,		lowercase:  Union[str, Any]=None       )					->		Union[str, Any]:
       A    :  Optional[int]								=GLPNConfig(hidden_sizes=[64, 128, 320, 512],		decoder_hidden_size=64,		depths=[3, 8, 27, 3]       )

       # load image processor (only resize + rescale)
       A    :  Union[str, Any]								=GLPNImageProcessor()

       # prepare image
       A    :  Union[str, Any]								=prepare_img()
       A    :  int								=image_processor(images=_lowerCAmelCase,		return_tensors='pt'       ).pixel_values

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

       # load original state dict
       A    :  List[str]								=torch.load(_lowerCAmelCase,		map_location=torch.device('cpu'       )       )

       # rename keys
       A    :  List[str]								=rename_keys(_lowerCAmelCase       )

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

       # create HuggingFace model and load state dict
       A    :  List[Any]								=GLPNForDepthEstimation(_lowerCAmelCase       )
       model.load_state_dict(_lowerCAmelCase       )
       model.eval()

       # forward pass
       A    :  Optional[Any]								=model(_lowerCAmelCase       )
       A    :  List[str]								=outputs.predicted_depth

       # verify output
       if model_name is not None:
              if "nyu" in model_name:
                     A    :  List[Any]								=torch.tensor(
                         [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]       )
              elif "kitti" in model_name:
                     A    :  Union[str, Any]								=torch.tensor(
                         [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]       )
              else:
                     raise ValueError(F'Unknown model name: {model_name}'       )

              A    :  Optional[int]								=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__":
    _lowercase				:   int   				=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.''',
    )
    _lowercase				:   Union[str, Any]   				=parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)













from __future__ import annotations

import math



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					float:
 UpperCamelCase      :   Tuple			      =  u
 for i in range(1				,  _lowerCAmelCase	):
  UpperCamelCase      :   Any			      =  temp * (u - i)
 return temp



def 						A_							(	)		->					None:
 UpperCamelCase      :   Union[str, Any]			      =  int(input("enter the numbers of values: "	)	)
 UpperCamelCase      :   list[list[float]]			      =  []
 for _ in range(_lowerCAmelCase	):
  y.append([]	)
 for i in range(_lowerCAmelCase	):
  for j in range(_lowerCAmelCase	):
   y[i].append(_lowerCAmelCase	)
   UpperCamelCase      :   List[str]			      =  0

 print("enter the values of parameters in a list: "	)
 UpperCamelCase      :   Optional[Any]			      =  list(map(_lowerCAmelCase				,  input().split()	)	)

 print("enter the values of corresponding parameters: "	)
 for i in range(_lowerCAmelCase	):
  UpperCamelCase      :   Tuple			      =  float(input()	)

 UpperCamelCase      :   Any			      =  int(input("enter the value to interpolate: "	)	)
 UpperCamelCase      :   List[Any]			      =  (value - x[0]) / (x[1] - x[0])

 # for calculating forward difference table

 for i in range(1				,  _lowerCAmelCase	):
  for j in range(n - i	):
   UpperCamelCase      :   Optional[Any]			      =  y[j + 1][i - 1] - y[j][i - 1]

 UpperCamelCase      :   List[str]			      =  y[0][0]
 for i in range(1				,  _lowerCAmelCase	):
  summ += (ucal(_lowerCAmelCase				,  _lowerCAmelCase	) * y[0][i]) / math.factorial(_lowerCAmelCase	)

 print(F"""the value at {value} is {summ}"""	)


if __name__ == "__main__":
    main()
