

"""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	):
					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]	):






					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_	(    ):
					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	):
					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"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case_   :   Union[str, Any]        =       logging.get_logger(__name__)

snake_case_   :   Tuple        =       {"""vocab_file""": """sentencepiece.bpe.model"""}

snake_case_   :   List[str]        =       {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

snake_case_   :   List[Any]        =       {
    """camembert-base""": 5_1_2,
}

snake_case_   :   Any        =       """▁"""







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE__     				=						PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE__     				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE__     				=						['''input_ids''', '''attention_mask''']







			def __init__(			self			: int			,  lowercase			: Union[str, Any]			,  lowercase			: str="<s>"			,  lowercase			: str="</s>"			,  lowercase			: Optional[int]="</s>"			,  lowercase			: Dict="<s>"			,  lowercase			: Optional[Any]="<unk>"			,  lowercase			: List[Any]="<pad>"			,  lowercase			: Any="<mask>"			,  lowercase			: Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]			,  lowercase			: Optional[Dict[str, Any]] = None			,  **lowercase			: Dict			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				AddedToken(lowercase			,  lstrip=lowercase			,  rstrip=lowercase  ) if isinstance(lowercase			,  lowercase  ) else mask_token

								UpperCAmelCase			:   int      =				{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=lowercase			,  eos_token=lowercase			,  unk_token=lowercase			,  sep_token=lowercase			,  cls_token=lowercase			,  pad_token=lowercase			,  mask_token=lowercase			,  additional_special_tokens=lowercase			,  sp_model_kwargs=self.sp_model_kwargs			,  **lowercase			,  )
								UpperCAmelCase			:   Tuple      =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(str(lowercase  )  )
								UpperCAmelCase			:   str      =				vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								UpperCAmelCase			:   Union[str, Any]      =				{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
								UpperCAmelCase			:   Union[str, Any]      =				len(self.fairseq_tokens_to_ids  )
								UpperCAmelCase			:   Dict      =				len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
								UpperCAmelCase			:   List[Any]      =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}







			def 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: List[int]			,  lowercase			: Optional[List[int]] = None  ):





								'''simple docstring'''



								if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase			:   Dict      =				[self.cls_token_id]
								UpperCAmelCase			:   List[str]      =				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: List[int]			,  lowercase			: Optional[List[int]] = None			,  lowercase			: bool = False  ):





								'''simple docstring'''



								if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowercase			,  token_ids_a=lowercase			,  already_has_special_tokens=lowercase  )

								if token_ids_a is None:
													return [1] + ([0] * len(lowercase  )) + [1]
								return [1] + ([0] * len(lowercase  )) + [1, 1] + ([0] * len(lowercase  )) + [1]







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: List[int]			,  lowercase			: Optional[List[int]] = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				[self.sep_token_id]
								UpperCAmelCase			:   List[str]      =				[self.cls_token_id]

								if token_ids_a is None:
													return len(cls + token_ids_a + sep  ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]







			@property
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{self.convert_ids_to_tokens(lowercase  ): i for i in range(self.vocab_size  )}
								vocab.update(self.added_tokens_encoder  )
								return vocab







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: str  ):





								'''simple docstring'''



								return self.sp_model.encode(lowercase			,  out_type=lowercase  )







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(lowercase  ) == 0:
													# Convert sentence piece unk token to fairseq unk token index
													return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(lowercase  )







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: Dict  ):





								'''simple docstring'''



								if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset  )







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				[]
								UpperCAmelCase			:   Tuple      =				""
								UpperCAmelCase			:   Union[str, Any]      =				False
								for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																		if not prev_is_special:
																							out_string += " "
																		out_string += self.sp_model.decode(lowercase  ) + token
																		UpperCAmelCase			:   Optional[int]      =				True
																		UpperCAmelCase			:   str      =				[]
													else:
																		current_sub_tokens.append(lowercase  )
																		UpperCAmelCase			:   Any      =				False
								out_string += self.sp_model.decode(lowercase  )
								return out_string.strip()







			def __getstate__(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				self.__dict__.copy()
								UpperCAmelCase			:   Dict      =				None
								return state







			def __setstate__(			self			: List[str]			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				d

								# for backward compatibility
								if not hasattr(self			,  "sp_model_kwargs"  ):
													UpperCAmelCase			:   Any      =				{}

								UpperCAmelCase			:   List[Any]      =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(self.vocab_file  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: str			,  lowercase			: Optional[str] = None  ):





								'''simple docstring'''



								if not os.path.isdir(lowercase  ):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
													return
								UpperCAmelCase			:   Optional[Any]      =				os.path.join(
								    lowercase			,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowercase  ) and os.path.isfile(self.vocab_file  ):
													copyfile(self.vocab_file			,  lowercase  )
								elif not os.path.isfile(self.vocab_file  ):
													with open(lowercase			,  "wb"  ) as fi:
																		UpperCAmelCase			:   List[Any]      =				self.sp_model.serialized_model_proto()
																		fi.write(lowercase  )

								return (out_vocab_file,)


