




"""simple docstring"""




import os
import re
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


a_	   =    logging.get_logger(__name__)

a_	   =    {"""vocab_file""": """spiece.model"""}

a_	   =    {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

a_	   =    {
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}

class    UpperCAmelCase_  (       a_						):
		UpperCamelCase						 =VOCAB_FILES_NAMES
		UpperCamelCase						 =PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase						 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase						 =["input_ids", "attention_mask"]
		UpperCamelCase						 =[]
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_="<unk>"							,		UpperCamelCase_="<s>"							,		UpperCamelCase_="</s>"							,		UpperCamelCase_="<pad>"							,		UpperCamelCase_="[SEP]"							,		UpperCamelCase_="[MASK]"							,		UpperCamelCase_="[CLS]"							,		UpperCamelCase_ = None							,		**UpperCamelCase_							,		)		->    Optional[Any]:
					__lowercase       :   Dict        =    AddedToken(_a							,		lstrip=_a							,		rstrip=_a    ) if isinstance(_a							,		_a    ) else bos_token
					__lowercase       :   Any        =    AddedToken(_a							,		lstrip=_a							,		rstrip=_a    ) if isinstance(_a							,		_a    ) else eos_token
					__lowercase       :   Any        =    AddedToken(_a							,		lstrip=_a							,		rstrip=_a    ) if isinstance(_a							,		_a    ) else unk_token
					__lowercase       :   List[Any]        =    AddedToken(_a							,		lstrip=_a							,		rstrip=_a    ) if isinstance(_a							,		_a    ) else pad_token
					__lowercase       :   Any        =    AddedToken(_a							,		lstrip=_a							,		rstrip=_a    ) if isinstance(_a							,		_a    ) else cls_token
					__lowercase       :   Any        =    AddedToken(_a							,		lstrip=_a							,		rstrip=_a    ) if isinstance(_a							,		_a    ) else sep_token

					# Mask token behave like a normal word, i.e. include the space before it
					__lowercase       :   List[Any]        =    AddedToken(_a							,		lstrip=_a							,		rstrip=_a    ) if isinstance(_a							,		_a    ) else mask_token

					__lowercase       :   Optional[int]        =    {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=_a							,		eos_token=_a							,		unk_token=_a							,		pad_token=_a							,		sep_token=_a							,		mask_token=_a							,		cls_token=_a							,		sp_model_kwargs=self.sp_model_kwargs							,		**_a							,		)

					__lowercase       :   Optional[Any]        =    vocab_file

					__lowercase       :   List[str]        =    spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(_a    )
		@property
		def      _lowerCamelCase				(		self    )		->    Optional[Any]:
					return self.sp_model.get_piece_size()
		def      _lowerCamelCase				(		self    )		->    str:
					__lowercase       :   str        =    {self.convert_ids_to_tokens(_a    ): i for i in range(self.vocab_size    )}
					vocab.update(self.added_tokens_encoder    )
					return vocab
		def __getstate__(		self    )		->    List[Any]:
					__lowercase       :   Union[str, Any]        =    self.__dict__.copy()
					__lowercase       :   Dict        =    None
					return state
		def __setstate__(		self							,		UpperCamelCase_    )		->    List[Any]:
					__lowercase       :   str        =    d

					# for backward compatibility
					if not hasattr(self							,		'''sp_model_kwargs'''    ):
								__lowercase       :   List[str]        =    {}

					__lowercase       :   Any        =    spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(self.vocab_file    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Any:
					return self.sp_model.encode(_a							,		out_type=_a    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Union[str, Any]:
					return self.sp_model.piece_to_id(_a    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Tuple:
					__lowercase       :   Union[str, Any]        =    self.sp_model.IdToPiece(_a    )
					return token
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Optional[Any]:
					__lowercase       :   List[Any]        =    []
					__lowercase       :   List[Any]        =    ""
					__lowercase       :   List[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(_a    ) + token
											__lowercase       :   int        =    True
											__lowercase       :   Union[str, Any]        =    []
								else:
											current_sub_tokens.append(_a    )
											__lowercase       :   int        =    False
					out_string += self.sp_model.decode(_a    )
					return out_string.strip()
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_ = False							,		UpperCamelCase_ = None							,		UpperCamelCase_ = True							,		**UpperCamelCase_							,		)		->    Dict:
					__lowercase       :   int        =    kwargs.pop('''use_source_tokenizer'''							,		_a    )

					__lowercase       :   Union[str, Any]        =    self.convert_ids_to_tokens(_a							,		skip_special_tokens=_a    )

					# To avoid mixing byte-level and unicode for byte-level BPT
					# we need to build string separately for added tokens and byte-level tokens
					# cf. https://github.com/huggingface/transformers/issues/1133
					__lowercase       :   Any        =    []
					__lowercase       :   Dict        =    []
					for token in filtered_tokens:
								if skip_special_tokens and token in self.all_special_ids:
											continue
								if token in self.added_tokens_encoder:
											if current_sub_text:
														sub_texts.append(self.convert_tokens_to_string(_a    )    )
														__lowercase       :   int        =    []
											sub_texts.append(_a    )
								else:
											current_sub_text.append(_a    )
					if current_sub_text:
								sub_texts.append(self.convert_tokens_to_string(_a    )    )

					# Mimic the behavior of the Rust tokenizer:
					# No space before [MASK] and [SEP]
					if spaces_between_special_tokens:
								__lowercase       :   Dict        =    re.sub(R''' (\[(MASK|SEP)\])'''							,		R'''\1'''							,		''' '''.join(_a    )    )
					else:
								__lowercase       :   Union[str, Any]        =    "".join(_a    )

					__lowercase       :   int        =    (
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
								__lowercase       :   Union[str, Any]        =    self.clean_up_tokenization(_a    )
								return clean_text
					else:
								return text
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_ = None    )		->    List[Any]:
					if not os.path.isdir(_a    ):
								logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
								return
					__lowercase       :   List[Any]        =    os.path.join(
					    _a							,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

					if os.path.abspath(self.vocab_file    ) != os.path.abspath(_a    ) and os.path.isfile(self.vocab_file    ):
								copyfile(self.vocab_file							,		_a    )
					elif not os.path.isfile(self.vocab_file    ):
								with open(_a							,		'''wb'''    ) as fi:
											__lowercase       :   int        =    self.sp_model.serialized_model_proto()
											fi.write(_a    )

					return (out_vocab_file,)
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_ = None    )		->    List[Any]:
					if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__lowercase       :   List[str]        =    [self.cls_token_id]
					__lowercase       :   Tuple        =    [self.sep_token_id]
					return cls + token_ids_a + sep + token_ids_a + sep
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_ = None							,		UpperCamelCase_ = False    )		->    Union[str, Any]:
					if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=_a							,		token_ids_a=_a							,		already_has_special_tokens=_a    )

					if token_ids_a is None:
								return [1] + ([0] * len(_a    )) + [1]
					return [1] + ([0] * len(_a    )) + [1] + ([0] * len(_a    )) + [1]
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_ = None    )		->    Dict:
					__lowercase       :   Tuple        =    [self.sep_token_id]
					__lowercase       :   Tuple        =    [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    ) * [0] + len(token_ids_a + sep    ) * [1]





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


def   __magic_name__	(      __snake_case				:       Any  ) ->				Any:
       lowercase    :				Optional[Any]       		=							DPTConfig()

       if "large" in checkpoint_url:
              lowercase    :				Optional[int]       		=							1024
              lowercase    :				Dict       		=							4096
              lowercase    :				Union[str, Any]       		=							24
              lowercase    :				str       		=							16
              lowercase    :				Dict       		=							[5, 11, 17, 23]
              lowercase    :				Any       		=							[256, 512, 1024, 1024]
              lowercase    :				Optional[Any]       		=							(1, 384, 384)

       if "ade" in checkpoint_url:
              lowercase    :				List[Any]       		=							True

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

       return config, expected_shape


def   __magic_name__	(      __snake_case				:       Union[str, Any]  ) ->				Optional[int]:
       lowercase    :				Optional[Any]       		=							["pretrained.model.head.weight", "pretrained.model.head.bias"]
       for k in ignore_keys:
              state_dict.pop(__snake_case     , __snake_case  )


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

       return name


def   __magic_name__	(      __snake_case				:       str     , __snake_case				:       str  ) ->				Any:
       for i in range(config.num_hidden_layers  ):
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              lowercase    :				Union[str, Any]       		=							state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""  )
              lowercase    :				List[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
              lowercase    :				Optional[int]       		=							in_proj_weight[: config.hidden_size, :]
              lowercase    :				int       		=							in_proj_bias[: config.hidden_size]
              lowercase    :				Tuple       		=							in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowercase    :				Dict       		=							in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowercase    :				int       		=							in_proj_weight[
                  -config.hidden_size :, :
              ]
              lowercase    :				Tuple       		=							in_proj_bias[-config.hidden_size :]


def   __magic_name__	(      ) ->				int:
       lowercase    :				str       		=							"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowercase    :				str       		=							Image.open(requests.get(__snake_case     , stream=__snake_case  ).raw  )
       return im


@torch.no_grad()
def   __magic_name__	(      __snake_case				:       List[Any]     , __snake_case				:       str     , __snake_case				:       Tuple     , __snake_case				:       Tuple  ) ->				Tuple:
       lowercase		,    lowercase    :				Tuple       		=							get_dpt_config(__snake_case  )
       # load original state_dict from URL
       lowercase    :				List[Any]       		=							torch.hub.load_state_dict_from_url(__snake_case     , map_location="cpu"  )
       # remove certain keys
       remove_ignore_keys_(__snake_case  )
       # rename keys
       for key in state_dict.copy().keys():
              lowercase    :				Any       		=							state_dict.pop(__snake_case  )
              lowercase    :				Optional[int]       		=							val
       # read in qkv matrices
       read_in_q_k_v(__snake_case     , __snake_case  )

       # load HuggingFace model
       lowercase    :				List[str]       		=							DPTForSemanticSegmentation(__snake_case  ) if "ade" in checkpoint_url else DPTForDepthEstimation(__snake_case  )
       model.load_state_dict(__snake_case  )
       model.eval()

       # Check outputs on an image
       lowercase    :				Any       		=							480 if "ade" in checkpoint_url else 384
       lowercase    :				Optional[Any]       		=							DPTImageProcessor(size=__snake_case  )

       lowercase    :				Any       		=							prepare_img()
       lowercase    :				Union[str, Any]       		=							image_processor(__snake_case     , return_tensors="pt"  )

       # forward pass
       lowercase    :				Optional[int]       		=							model(**__snake_case  ).logits if "ade" in checkpoint_url else model(**__snake_case  ).predicted_depth

       # Assert logits
       lowercase    :				Optional[int]       		=							torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]  )
       if "ade" in checkpoint_url:
              lowercase    :				List[str]       		=							torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]  )
       assert outputs.shape == torch.Size(__snake_case  )
       assert (
           torch.allclose(outputs[0, 0, :3, :3]     , __snake_case     , atol=1E-4  )
           if "ade" in checkpoint_url
           else torch.allclose(outputs[0, :3, :3]     , __snake_case  )
       )

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

       if push_to_hub:
              print("Pushing model to 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			:       Dict   		=       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.""",
   )

   _A			:       Dict   		=       parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


