

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



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






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

					return new_state_dict



def   lowercase_	(    _lowercase       :		Optional[int]							,      _lowercase       :		Optional[Any]	):




					'''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			:   List[str]      =				state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""	)
															UpperCAmelCase			:   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
															UpperCAmelCase			:   Optional[int]      =				kv_weight[
															    : config.hidden_sizes[i], :
															]
															UpperCAmelCase			:   Optional[Any]      =				kv_bias[: config.hidden_sizes[i]]
															UpperCAmelCase			:   Dict      =				kv_weight[
															    config.hidden_sizes[i] :, :
															]
															UpperCAmelCase			:   List[Any]      =				kv_bias[config.hidden_sizes[i] :]



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				"http://images.cocodataset.org/val2017/000000039769.jpg"
					UpperCAmelCase			:   Dict      =				Image.open(requests.get(_lowercase							,      stream=_lowercase	).raw	)

					return image



@torch.no_grad()
def   lowercase_	(    _lowercase       :		List[Any]							,      _lowercase       :		Tuple							,      _lowercase       :		List[Any]=False							,      _lowercase       :		str=None	):




					'''simple docstring'''






					UpperCAmelCase			:   Optional[Any]      =				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			:   Dict      =				GLPNImageProcessor()

					# prepare image
					UpperCAmelCase			:   Union[str, Any]      =				prepare_img()
					UpperCAmelCase			:   str      =				image_processor(images=_lowercase							,      return_tensors="pt"	).pixel_values

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

					# load original state dict
					UpperCAmelCase			:   Union[str, Any]      =				torch.load(_lowercase							,      map_location=torch.device("cpu"	)	)

					# rename keys
					UpperCAmelCase			:   Optional[int]      =				rename_keys(_lowercase	)

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

					# create HuggingFace model and load state dict
					UpperCAmelCase			:   Tuple      =				GLPNForDepthEstimation(_lowercase	)
					model.load_state_dict(_lowercase	)
					model.eval()

					# forward pass
					UpperCAmelCase			:   List[Any]      =				model(_lowercase	)
					UpperCAmelCase			:   Union[str, Any]      =				outputs.predicted_depth

					# verify output
					if model_name is not None:
										if "nyu" in model_name:
															UpperCAmelCase			:   int      =				torch.tensor(
															    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]	)
										elif "kitti" in model_name:
															UpperCAmelCase			:   Any      =				torch.tensor(
															    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]	)
										else:
															raise ValueError(F"""Unknown model name: {model_name}"""	)

										UpperCAmelCase			:   List[str]      =				torch.Size([1, 4_80, 6_40]	)

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


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





"""simple docstring"""






def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					if not all(x.isalpha() for x in string	):
										raise ValueError("String must only contain alphabetic characters."	)

					UpperCAmelCase			:   Optional[int]      =				sorted(string.lower()	)
					return len(_lowercase	) == len(set(_lowercase	)	)


if __name__ == "__main__":
	snake_case_   :   Tuple        =       input("""Enter a string """).strip()

	snake_case_   :   str        =       is_isogram(input_str)
	print(f'''{input_str} is {"an" if isogram else "not an"} isogram.''')


