


"""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()
__SCREAMING_SNAKE_CASE							:    Dict		=    logging.get_logger(__name__)







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

							return new_state_dict







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[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)
																					snake_case_           =		state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""				)
																					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
																					snake_case_           =		kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					snake_case_           =		kv_bias[: config.hidden_sizes[i]]
																					snake_case_           =		kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					snake_case_           =		kv_bias[config.hidden_sizes[i] :]







def 						_a				(      )    ->				str:
							snake_case_           =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							snake_case_           =		Image.open(requests.get(_SCREAMING_SNAKE_CASE				, stream=_SCREAMING_SNAKE_CASE				).raw				)

							return image







@torch.no_grad()
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=False				, _SCREAMING_SNAKE_CASE=None				)    ->				Optional[Any]:
							snake_case_           =		GLPNConfig(hidden_sizes=[64, 128, 320, 512]				, decoder_hidden_size=64				, depths=[3, 8, 27, 3]				)

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

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

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

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

							# rename keys
							snake_case_           =		rename_keys(_SCREAMING_SNAKE_CASE				)

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

							# create HuggingFace model and load state dict
							snake_case_           =		GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE				)
							model.load_state_dict(_SCREAMING_SNAKE_CASE				)
							model.eval()

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

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

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

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    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.',
				)
				__SCREAMING_SNAKE_CASE							:    str		=    parser.parse_args()
				convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation







def 						_a				(      _SCREAMING_SNAKE_CASE = 8				)    ->				str:
							snake_case_           =		ascii_letters + digits + punctuation
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		i // 3
							snake_case_           =		i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							snake_case_           =		(
							    chars_incl
							    + random(_SCREAMING_SNAKE_CASE				, quotient + remainder				)
							    + random(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							    + random(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							)
							snake_case_           =		list(_SCREAMING_SNAKE_CASE				)
							shuffle(_SCREAMING_SNAKE_CASE				)
							return "".join(_SCREAMING_SNAKE_CASE				)

							# random is a generalised function for letters, characters and numbers







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = 8				)    ->				bool:

							if len(_SCREAMING_SNAKE_CASE				) < min_length:
														# Your Password must be at least 8 characters long
														return False

							snake_case_           =		any(char in ascii_uppercase for char in password				)
							snake_case_           =		any(char in ascii_lowercase for char in password				)
							snake_case_           =		any(char in digits for char in password				)
							snake_case_           =		any(char in punctuation for char in password				)

							return upper and lower and num and spec_char
							# Passwords should contain UPPERCASE, lowerase
							# numbers, and special characters







def 						_a				(      )    ->				str:
							snake_case_           =		int(input("""Please indicate the max length of your password: """				).strip()				)
							snake_case_           =		input(
							    """Please indicate the characters that must be in your password: """				).strip()
							print("""Password generated:"""				, password_generator(_SCREAMING_SNAKE_CASE				)				)
							print(
							    """Alternative Password generated:"""				, alternative_password_generator(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)				, )
							print("""[If you are thinking of using this passsword, You better save it.]"""				)


if __name__ == "__main__":
				main()



