
"""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														=		logging.get_logger(__name__)



def       lowerCamelCase__		(			__snake_case	)							->		Any:


					"""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(_snake_case	)-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(_snake_case	)-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(_snake_case	)-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(_snake_case	)-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       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		Dict:


					"""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)
															_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       lowerCamelCase__		(			)							->		Dict:


					"""simple docstring"""





					_UpperCamelCase							 =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase							 =  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	)							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  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)
					_UpperCamelCase							 =  GLPNImageProcessor()

					# prepare image
					_UpperCamelCase							 =  prepare_img()
					_UpperCamelCase							 =  image_processor(images=_snake_case,     return_tensors='''pt'''	).pixel_values

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

					# load original state dict
					_UpperCamelCase							 =  torch.load(_snake_case,     map_location=torch.device('''cpu'''	)	)

					# rename keys
					_UpperCamelCase							 =  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
					_UpperCamelCase							 =  GLPNForDepthEstimation(_snake_case	)
					model.load_state_dict(_snake_case	)
					model.eval()

					# forward pass
					_UpperCamelCase							 =  model(_snake_case	)
					_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, 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__":
				_a														=		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														=		parser.parse_args()
				convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)










"""simple docstring"""





import math


def    _snake_case  (  ):
      lowerCAmelCase    :		Union[str, Any]				=      input('''Enter message: '''	)
      lowerCAmelCase    :		Optional[int]				=      int(input(f'''Enter key [2-{len(_snake_case	) - 1}]: '''	)	)
      lowerCAmelCase    :		str				=      input('''Encryption/Decryption [e/d]: '''	)

      if mode.lower().startswith('''e'''	):
            lowerCAmelCase    :		Any				=      encrypt_message(_snake_case       ,      _snake_case	)
      elif mode.lower().startswith('''d'''	):
            lowerCAmelCase    :		Union[str, Any]				=      decrypt_message(_snake_case       ,      _snake_case	)

      # Append pipe symbol (vertical bar) to identify spaces at the end.
      print(f'''Output:\n{text + "|"}'''	)


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	str	):
      lowerCAmelCase    :		Optional[Any]				=      [''''''] * key
      for col in range(_snake_case	):
            lowerCAmelCase    :		Optional[Any]				=      col
            while pointer < len(_snake_case	):
                  cipher_text[col] += message[pointer]
                  pointer += key
      return "".join(_snake_case	)


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	str	):
      lowerCAmelCase    :		Union[str, Any]				=      math.ceil(len(_snake_case	) / key	)
      lowerCAmelCase    :		str				=      key
      lowerCAmelCase    :		Any				=      (num_cols * num_rows) - len(_snake_case	)
      lowerCAmelCase    :		Dict				=      [''''''] * num_cols
      lowerCAmelCase    :		int				=      0
      lowerCAmelCase    :		int				=      0

      for symbol in message:
            plain_text[col] += symbol
            col += 1

            if (
                (col == num_cols)
                or (col == num_cols - 1)
                and (row >= num_rows - num_shaded_boxes)
            ):
                  lowerCAmelCase    :		int				=      0
                  row += 1

      return "".join(_snake_case	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()


