


'''simple docstring'''


import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__					=   logging.get_logger(__name__)

lowerCamelCase__					=   '▁'

lowerCamelCase__					=   {'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}

lowerCamelCase__					=   {
    'sentencepiece_model_file': 'sentencepiece.bpe.model',
    'vocab_file': 'vocab.txt',
}

lowerCamelCase__					=   {
    'vocab_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
    },
    'sentencepiece_model_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
    },
}

lowerCamelCase__					=   {
    'ernie-m-base': 514,
    'ernie-m-large': 514,
}

lowerCamelCase__					=   {
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		List[str]				      =   ["input_ids"]

			lowerCAmelCase       :		Tuple				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Any				      =   PRETRAINED_INIT_CONFIGURATION
			lowerCAmelCase       :		Optional[int]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		Dict				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		Optional[Any]				      =   RESOURCE_FILES_NAMES




			def __init__(						self     :   Dict		,     lowerCamelCase__     :   List[Any]		,     lowerCamelCase__     :   Optional[Any]=None		,     lowerCamelCase__     :   Dict=False		,     lowerCamelCase__     :   Optional[Any]="utf8"		,     lowerCamelCase__     :   Union[str, Any]="[UNK]"		,     lowerCamelCase__     :   str="[SEP]"		,     lowerCamelCase__     :   List[Any]="[PAD]"		,     lowerCamelCase__     :   List[Any]="[CLS]"		,     lowerCamelCase__     :   List[str]="[MASK]"		,     lowerCamelCase__     :   Optional[Dict[str, Any]] = None		,     **lowerCamelCase__     :   List[Any]		,     )      ->None:



					'''simple docstring'''







					_UpperCAmelCase						:				int	    =					{} if sp_model_kwargs is None else sp_model_kwargs
					super().__init__(
					    do_lower_case=lowerCamelCase__		,     unk_token=lowerCamelCase__		,     sep_token=lowerCamelCase__		,     pad_token=lowerCamelCase__		,     cls_token=lowerCamelCase__		,     mask_token=lowerCamelCase__		,     vocab_file=lowerCamelCase__		,     encoding=lowerCamelCase__		,     sp_model_kwargs=self.sp_model_kwargs		,     **lowerCamelCase__		,     )
					_UpperCAmelCase						:				Union[str, Any]	    =					do_lower_case
					_UpperCAmelCase						:				int	    =					sentencepiece_model_ckpt
					_UpperCAmelCase						:				List[Any]	    =					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(lowerCamelCase__       )

					# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
					if vocab_file is not None:
							_UpperCAmelCase						:				str	    =					self.load_vocab(filepath=lowerCamelCase__       )
					else:
							_UpperCAmelCase						:				Optional[Any]	    =					{self.sp_model.id_to_piece(lowerCamelCase__       ): id for id in range(self.sp_model.get_piece_size()       )}
					_UpperCAmelCase						:				List[Any]	    =					{v: k for k, v in self.vocab.items()}




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   List[Any]       )      ->Any:



					'''simple docstring'''







					if text is None:
							return None

					_UpperCAmelCase						:				Optional[Any]	    =					self.tokenize(lowerCamelCase__       )
					_UpperCAmelCase    ,			_UpperCAmelCase						:				Union[str, Any]	    =					"", []

					for i, ch in enumerate(lowerCamelCase__       ):
							if ch in self.SP_CHAR_MAPPING:
									_UpperCAmelCase						:				Tuple	    =					self.SP_CHAR_MAPPING.get(lowerCamelCase__       )
							else:
									_UpperCAmelCase						:				str	    =					unicodedata.normalize("NFKC"		,     lowerCamelCase__       )
							if self.is_whitespace(lowerCamelCase__       ):
									continue
							normalized_text += ch
							char_mapping.extend([i] * len(lowerCamelCase__       )       )

					_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase						:				Dict	    =					normalized_text, [], 0

					if self.do_lower_case:
							_UpperCAmelCase						:				Union[str, Any]	    =					text.lower()

					for token in split_tokens:
							if token[:1] == "▁":
									_UpperCAmelCase						:				Union[str, Any]	    =					token[1:]
							_UpperCAmelCase						:				Optional[int]	    =					text[offset:].index(lowerCamelCase__       ) + offset
							_UpperCAmelCase						:				Tuple	    =					start + len(lowerCamelCase__       )

							token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)       )
							_UpperCAmelCase						:				Union[str, Any]	    =					end
					return token_mapping




			@property
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->Tuple:



					'''simple docstring'''







					return len(self.vocab       )




			def 							lowerCAmelCase__			(						self     :   Any       )      ->Dict:



					'''simple docstring'''







					return dict(self.vocab		,     **self.added_tokens_encoder       )




			def __getstate__(						self     :   Dict       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.__dict__.copy()
					_UpperCAmelCase						:				Union[str, Any]	    =					None
					return state




			def __setstate__(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Tuple       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					d

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

					_UpperCAmelCase						:				int	    =					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(self.sentencepiece_model_ckpt       )




			def 							lowerCAmelCase__			(						self     :   Optional[int]		,     lowerCamelCase__     :   Any       )      ->Tuple:



					'''simple docstring'''







					return "".join((self.SP_CHAR_MAPPING.get(lowerCamelCase__		,     lowerCamelCase__       ) for c in text)       )




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   Any		,     lowerCamelCase__     :   Optional[Any]=False		,     lowerCamelCase__     :   Any=64		,     lowerCamelCase__     :   Union[str, Any]=0.1       )      ->Tuple:



					'''simple docstring'''







					if self.sp_model_kwargs.get("enable_sampling"       ) is True:
							_UpperCAmelCase						:				Optional[Any]	    =					True
					if self.sp_model_kwargs.get("alpha"       ) is not None:
							_UpperCAmelCase						:				List[str]	    =					self.sp_model_kwargs.get("alpha"       )
					if self.sp_model_kwargs.get("nbest_size"       ) is not None:
							_UpperCAmelCase						:				Tuple	    =					self.sp_model_kwargs.get("nbest_size"       )

					if not enable_sampling:
							_UpperCAmelCase						:				Tuple	    =					self.sp_model.EncodeAsPieces(lowerCamelCase__       )
					else:
							_UpperCAmelCase						:				Dict	    =					self.sp_model.SampleEncodeAsPieces(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__       )
					_UpperCAmelCase						:				Optional[int]	    =					[]
					for pi, piece in enumerate(lowerCamelCase__       ):
							if piece == SPIECE_UNDERLINE:
									if not pieces[pi + 1].startswith(lowerCamelCase__       ) and pi != 0:
											new_pieces.append(lowerCamelCase__       )
											continue
									else:
											continue
							_UpperCAmelCase						:				Any	    =					0
							for i, chunk in enumerate(lowerCamelCase__       ):
									if chunk == SPIECE_UNDERLINE:
											continue
									if self.is_ch_char(lowerCamelCase__       ) or self.is_punct(lowerCamelCase__       ):
											if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
													new_pieces.append(piece[lst_i:i]       )
											new_pieces.append(lowerCamelCase__       )
											_UpperCAmelCase						:				List[Any]	    =					i + 1
									elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
											if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
													new_pieces.append(piece[lst_i:i]       )
											_UpperCAmelCase						:				int	    =					i
									elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
											if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
													new_pieces.append(piece[lst_i:i]       )
											_UpperCAmelCase						:				Optional[Any]	    =					i
							if len(lowerCamelCase__       ) > lst_i:
									new_pieces.append(piece[lst_i:]       )
					return new_pieces




			def 							lowerCAmelCase__			(						self     :   Optional[int]		,     lowerCamelCase__     :   Optional[Any]       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					"".join(lowerCamelCase__       ).replace(lowerCamelCase__		,     " "       ).strip()
					return out_string




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   Any       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					self.convert_ids_to_tokens(lowerCamelCase__       )
					_UpperCAmelCase						:				int	    =					"".join(lowerCamelCase__       ).replace(lowerCamelCase__		,     " "       ).strip()
					return out_string




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   Optional[Any]       )      ->Optional[int]:



					'''simple docstring'''







					return self.vocab.get(lowerCamelCase__		,     self.vocab.get(self.unk_token       )       )




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Tuple       )      ->int:



					'''simple docstring'''







					return self.reverse_vocab.get(lowerCamelCase__		,     self.unk_token       )




			def 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   int=None       )      ->Dict:



					'''simple docstring'''







					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					_UpperCAmelCase						:				List[str]	    =					[self.cls_token_id]
					_UpperCAmelCase						:				Dict	    =					[self.sep_token_id]
					return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep




			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   List[Any]		,     lowerCamelCase__     :   int=None       )      ->Dict:



					'''simple docstring'''







					if offset_mapping_a is None:
							return [(0, 0)] + offset_mapping_a + [(0, 0)]

					return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]




			def 							lowerCAmelCase__			(						self     :   Optional[int]		,     lowerCamelCase__     :   List[str]		,     lowerCamelCase__     :   Optional[Any]=None		,     lowerCamelCase__     :   Union[str, Any]=False       )      ->List[Any]:



					'''simple docstring'''







					if already_has_special_tokens:
							if token_ids_a is not None:
									raise ValueError(
									    "You should not supply a second sequence if the provided sequence of "
									    "ids is already formatted with special tokens for the model."       )
							return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

					if token_ids_a is not None:
							return [1] + ([0] * len(lowerCamelCase__       )) + [1, 1] + ([0] * len(lowerCamelCase__       )) + [1]
					return [1] + ([0] * len(lowerCamelCase__       )) + [1]




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					if token_ids_a is None:
							# [CLS] X [SEP]
							return (len(lowerCamelCase__       ) + 2) * [0]

					# [CLS] A [SEP] [SEP] B [SEP]
					return [0] * (len(lowerCamelCase__       ) + 1) + [1] * (len(lowerCamelCase__       ) + 3)




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Union[str, Any]       )      ->List[str]:



					'''simple docstring'''







					if "\u4e00" <= char <= "\u9fff":
							return True
					return False




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   List[Any]       )      ->Tuple:



					'''simple docstring'''







					if ("a" <= char <= "z") or ("A" <= char <= "Z"):
							return True
					return False




			def 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   List[str]       )      ->Optional[Any]:



					'''simple docstring'''







					if char in ",;:.?!~，；：。？！《》【】":
							return True
					return False




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   str       )      ->Union[str, Any]:



					'''simple docstring'''







					if char == " " or char == "\t" or char == "\n" or char == "\r":
							return True
					if len(lowerCamelCase__       ) == 1:
							_UpperCAmelCase						:				List[Any]	    =					unicodedata.category(lowerCamelCase__       )
							if cat == "Zs":
									return True
					return False




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   str       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					{}
					with io.open(lowerCamelCase__		,     "r"		,     encoding="utf-8"       ) as f:
							for index, line in enumerate(lowerCamelCase__       ):
									_UpperCAmelCase						:				Union[str, Any]	    =					line.rstrip("\n"       )
									_UpperCAmelCase						:				List[str]	    =					int(lowerCamelCase__       )

					return token_to_idx






			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = None       )      ->Tuple[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					0
					if os.path.isdir(lowerCamelCase__       ):
							_UpperCAmelCase						:				Any	    =					os.path.join(
							    lowerCamelCase__		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
					else:
							_UpperCAmelCase						:				int	    =					(filename_prefix + "-" if filename_prefix else "") + save_directory
					with open(lowerCamelCase__		,     "w"		,     encoding="utf-8"       ) as writer:
							for token, token_index in sorted(self.vocab.items()		,     key=lambda lowerCamelCase__       : kv[1]       ):
									if index != token_index:
											logger.warning(
											    F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
											    " Please check that the vocabulary is not corrupted!"       )
											_UpperCAmelCase						:				List[str]	    =					token_index
									writer.write(token + "\n"       )
									index += 1

					_UpperCAmelCase						:				str	    =					os.path.join(lowerCamelCase__		,     "sentencepiece.bpe.model"       )
					with open(lowerCamelCase__		,     "wb"       ) as fi:
							_UpperCAmelCase						:				List[str]	    =					self.sp_model.serialized_model_proto()
							fi.write(lowerCamelCase__       )

					return (vocab_file,)







'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__					=   logging.get_logger(__name__)






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					DPTConfig()

		if "large" in checkpoint_url:
				_UpperCAmelCase						:				List[str]	    =					1_024
				_UpperCAmelCase						:				Optional[int]	    =					4_096
				_UpperCAmelCase						:				Union[str, Any]	    =					24
				_UpperCAmelCase						:				List[Any]	    =					16
				_UpperCAmelCase						:				List[Any]	    =					[5, 11, 17, 23]
				_UpperCAmelCase						:				int	    =					[256, 512, 1_024, 1_024]
				_UpperCAmelCase						:				Optional[Any]	    =					(1, 384, 384)

		if "ade" in checkpoint_url:
				_UpperCAmelCase						:				Optional[int]	    =					True

				_UpperCAmelCase						:				List[Any]	    =					150
				_UpperCAmelCase						:				Optional[Any]	    =					"huggingface/label-files"
				_UpperCAmelCase						:				Optional[int]	    =					"ade20k-id2label.json"
				_UpperCAmelCase						:				Tuple	    =					json.load(open(cached_download(hf_hub_url(__lowerCAmelCase					,       __lowerCAmelCase					,       repo_type="dataset"      )      )					,       "r"      )      )
				_UpperCAmelCase						:				str	    =					{int(__lowerCAmelCase      ): v for k, v in idalabel.items()}
				_UpperCAmelCase						:				int	    =					idalabel
				_UpperCAmelCase						:				Union[str, Any]	    =					{v: k for k, v in idalabel.items()}
				_UpperCAmelCase						:				int	    =					[1, 150, 480, 480]

		return config, expected_shape






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					["pretrained.model.head.weight", "pretrained.model.head.bias"]
		for k in ignore_keys:
				state_dict.pop(__lowerCAmelCase					,       __lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		if (
		    "pretrained.model" in name
		    and "cls_token" not in name
		    and "pos_embed" not in name
		    and "patch_embed" not in name
		):
				_UpperCAmelCase						:				str	    =					name.replace("pretrained.model"					,       "dpt.encoder"      )
		if "pretrained.model" in name:
				_UpperCAmelCase						:				List[str]	    =					name.replace("pretrained.model"					,       "dpt.embeddings"      )
		if "patch_embed" in name:
				_UpperCAmelCase						:				Dict	    =					name.replace("patch_embed"					,       "patch_embeddings"      )
		if "pos_embed" in name:
				_UpperCAmelCase						:				int	    =					name.replace("pos_embed"					,       "position_embeddings"      )
		if "attn.proj" in name:
				_UpperCAmelCase						:				int	    =					name.replace("attn.proj"					,       "attention.output.dense"      )
		if "proj" in name and "project" not in name:
				_UpperCAmelCase						:				int	    =					name.replace("proj"					,       "projection"      )
		if "blocks" in name:
				_UpperCAmelCase						:				Tuple	    =					name.replace("blocks"					,       "layer"      )
		if "mlp.fc1" in name:
				_UpperCAmelCase						:				Union[str, Any]	    =					name.replace("mlp.fc1"					,       "intermediate.dense"      )
		if "mlp.fc2" in name:
				_UpperCAmelCase						:				Union[str, Any]	    =					name.replace("mlp.fc2"					,       "output.dense"      )
		if "norm1" in name:
				_UpperCAmelCase						:				Optional[Any]	    =					name.replace("norm1"					,       "layernorm_before"      )
		if "norm2" in name:
				_UpperCAmelCase						:				int	    =					name.replace("norm2"					,       "layernorm_after"      )
		if "scratch.output_conv" in name:
				_UpperCAmelCase						:				List[Any]	    =					name.replace("scratch.output_conv"					,       "head"      )
		if "scratch" in name:
				_UpperCAmelCase						:				List[str]	    =					name.replace("scratch"					,       "neck"      )
		if "layer1_rn" in name:
				_UpperCAmelCase						:				Union[str, Any]	    =					name.replace("layer1_rn"					,       "convs.0"      )
		if "layer2_rn" in name:
				_UpperCAmelCase						:				str	    =					name.replace("layer2_rn"					,       "convs.1"      )
		if "layer3_rn" in name:
				_UpperCAmelCase						:				int	    =					name.replace("layer3_rn"					,       "convs.2"      )
		if "layer4_rn" in name:
				_UpperCAmelCase						:				Tuple	    =					name.replace("layer4_rn"					,       "convs.3"      )
		if "refinenet" in name:
				_UpperCAmelCase						:				Optional[Any]	    =					int(name[len("neck.refinenet"      ) : len("neck.refinenet"      ) + 1]      )
				# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
				_UpperCAmelCase						:				List[str]	    =					name.replace(F"""refinenet{layer_idx}"""					,       F"""fusion_stage.layers.{abs(layer_idx-4      )}"""      )
		if "out_conv" in name:
				_UpperCAmelCase						:				Tuple	    =					name.replace("out_conv"					,       "projection"      )
		if "resConfUnit1" in name:
				_UpperCAmelCase						:				Optional[int]	    =					name.replace("resConfUnit1"					,       "residual_layer1"      )
		if "resConfUnit2" in name:
				_UpperCAmelCase						:				Optional[int]	    =					name.replace("resConfUnit2"					,       "residual_layer2"      )
		if "conv1" in name:
				_UpperCAmelCase						:				Optional[Any]	    =					name.replace("conv1"					,       "convolution1"      )
		if "conv2" in name:
				_UpperCAmelCase						:				Optional[Any]	    =					name.replace("conv2"					,       "convolution2"      )
		# readout blocks
		if "pretrained.act_postprocess1.0.project.0" in name:
				_UpperCAmelCase						:				int	    =					name.replace("pretrained.act_postprocess1.0.project.0"					,       "neck.reassemble_stage.readout_projects.0.0"      )
		if "pretrained.act_postprocess2.0.project.0" in name:
				_UpperCAmelCase						:				Optional[Any]	    =					name.replace("pretrained.act_postprocess2.0.project.0"					,       "neck.reassemble_stage.readout_projects.1.0"      )
		if "pretrained.act_postprocess3.0.project.0" in name:
				_UpperCAmelCase						:				Dict	    =					name.replace("pretrained.act_postprocess3.0.project.0"					,       "neck.reassemble_stage.readout_projects.2.0"      )
		if "pretrained.act_postprocess4.0.project.0" in name:
				_UpperCAmelCase						:				int	    =					name.replace("pretrained.act_postprocess4.0.project.0"					,       "neck.reassemble_stage.readout_projects.3.0"      )
		# resize blocks
		if "pretrained.act_postprocess1.3" in name:
				_UpperCAmelCase						:				int	    =					name.replace("pretrained.act_postprocess1.3"					,       "neck.reassemble_stage.layers.0.projection"      )
		if "pretrained.act_postprocess1.4" in name:
				_UpperCAmelCase						:				Dict	    =					name.replace("pretrained.act_postprocess1.4"					,       "neck.reassemble_stage.layers.0.resize"      )
		if "pretrained.act_postprocess2.3" in name:
				_UpperCAmelCase						:				Tuple	    =					name.replace("pretrained.act_postprocess2.3"					,       "neck.reassemble_stage.layers.1.projection"      )
		if "pretrained.act_postprocess2.4" in name:
				_UpperCAmelCase						:				Optional[int]	    =					name.replace("pretrained.act_postprocess2.4"					,       "neck.reassemble_stage.layers.1.resize"      )
		if "pretrained.act_postprocess3.3" in name:
				_UpperCAmelCase						:				Union[str, Any]	    =					name.replace("pretrained.act_postprocess3.3"					,       "neck.reassemble_stage.layers.2.projection"      )
		if "pretrained.act_postprocess4.3" in name:
				_UpperCAmelCase						:				Union[str, Any]	    =					name.replace("pretrained.act_postprocess4.3"					,       "neck.reassemble_stage.layers.3.projection"      )
		if "pretrained.act_postprocess4.4" in name:
				_UpperCAmelCase						:				Dict	    =					name.replace("pretrained.act_postprocess4.4"					,       "neck.reassemble_stage.layers.3.resize"      )
		if "pretrained" in name:
				_UpperCAmelCase						:				List[str]	    =					name.replace("pretrained"					,       "dpt"      )
		if "bn" in name:
				_UpperCAmelCase						:				Dict	    =					name.replace("bn"					,       "batch_norm"      )
		if "head" in name:
				_UpperCAmelCase						:				Tuple	    =					name.replace("head"					,       "head.head"      )
		if "encoder.norm" in name:
				_UpperCAmelCase						:				Optional[Any]	    =					name.replace("encoder.norm"					,       "layernorm"      )
		if "auxlayer" in name:
				_UpperCAmelCase						:				Dict	    =					name.replace("auxlayer"					,       "auxiliary_head.head"      )

		return name






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		for i in range(config.num_hidden_layers      ):
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				_UpperCAmelCase						:				int	    =					state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""      )
				_UpperCAmelCase						:				str	    =					state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias"""      )
				# next, add query, keys and values (in that order) to the state dict
				_UpperCAmelCase						:				List[str]	    =					in_proj_weight[: config.hidden_size, :]
				_UpperCAmelCase						:				Dict	    =					in_proj_bias[: config.hidden_size]
				_UpperCAmelCase						:				List[str]	    =					in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				_UpperCAmelCase						:				int	    =					in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				_UpperCAmelCase						:				Union[str, Any]	    =					in_proj_weight[
				    -config.hidden_size :, :
				]
				_UpperCAmelCase						:				str	    =					in_proj_bias[-config.hidden_size :]






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Optional[int]	    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
		_UpperCAmelCase						:				Any	    =					Image.open(requests.get(__lowerCAmelCase					,       stream=__lowerCAmelCase      ).raw      )
		return im






@torch.no_grad()
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Dict	    =					get_dpt_config(__lowerCAmelCase      )
		# load original state_dict from URL
		_UpperCAmelCase						:				List[Any]	    =					torch.hub.load_state_dict_from_url(__lowerCAmelCase					,       map_location="cpu"      )
		# remove certain keys
		remove_ignore_keys_(__lowerCAmelCase      )
		# rename keys
		for key in state_dict.copy().keys():
				_UpperCAmelCase						:				Tuple	    =					state_dict.pop(__lowerCAmelCase      )
				_UpperCAmelCase						:				List[Any]	    =					val
		# read in qkv matrices
		read_in_q_k_v(__lowerCAmelCase					,       __lowerCAmelCase      )

		# load HuggingFace model
		_UpperCAmelCase						:				Any	    =					DPTForSemanticSegmentation(__lowerCAmelCase      ) if "ade" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase      )
		model.load_state_dict(__lowerCAmelCase      )
		model.eval()

		# Check outputs on an image
		_UpperCAmelCase						:				Any	    =					480 if "ade" in checkpoint_url else 384
		_UpperCAmelCase						:				List[str]	    =					DPTImageProcessor(size=__lowerCAmelCase      )

		_UpperCAmelCase						:				Any	    =					prepare_img()
		_UpperCAmelCase						:				Dict	    =					image_processor(__lowerCAmelCase					,       return_tensors="pt"      )

		# forward pass
		_UpperCAmelCase						:				Tuple	    =					model(**__lowerCAmelCase      ).logits if "ade" in checkpoint_url else model(**__lowerCAmelCase      ).predicted_depth

		# Assert logits
		_UpperCAmelCase						:				Dict	    =					torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]      )
		if "ade" in checkpoint_url:
				_UpperCAmelCase						:				str	    =					torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]      )
		assert outputs.shape == torch.Size(__lowerCAmelCase      )
		assert (
		    torch.allclose(outputs[0, 0, :3, :3]					,       __lowerCAmelCase					,       atol=1e-4      )
		    if "ade" in checkpoint_url
		    else torch.allclose(outputs[0, :3, :3]					,       __lowerCAmelCase      )
		)

		Path(__lowerCAmelCase      ).mkdir(exist_ok=__lowerCAmelCase      )
		print(F"""Saving model to {pytorch_dump_folder_path}"""      )
		model.save_pretrained(__lowerCAmelCase      )
		print(F"""Saving image processor to {pytorch_dump_folder_path}"""      )
		image_processor.save_pretrained(__lowerCAmelCase      )

		if push_to_hub:
				print("Pushing model to 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__":
						lowerCamelCase__					=   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_url',
						    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
						    type=str,
						    help='URL of the original DPT checkpoint you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default=None,
						    type=str,
						    required=True,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						)
						parser.add_argument(
						    '--model_name',
						    default='dpt-large',
						    type=str,
						    help='Name of the model, in case you\'re pushing to the hub.',
						)

						lowerCamelCase__					=   parser.parse_args()
						convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



