


"""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()
__snake_case    					=					logging.get_logger(__name__)


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]  ):
 lowercase__       :       Optional[Any]				=						DPTConfig()

 if "large" in checkpoint_url:
  lowercase__       :       str				=						10_24
  lowercase__       :       Tuple				=						40_96
  lowercase__       :       Any				=						24
  lowercase__       :       Tuple				=						16
  lowercase__       :       Optional[Any]				=						[5, 11, 17, 23]
  lowercase__       :       Dict				=						[2_56, 5_12, 10_24, 10_24]
  lowercase__       :       Dict				=						(1, 3_84, 3_84)

 if "ade" in checkpoint_url:
  lowercase__       :       str				=						True

  lowercase__       :       str				=						1_50
  lowercase__       :       List[str]				=						"""huggingface/label-files"""
  lowercase__       :       Dict				=						"""ade20k-id2label.json"""
  lowercase__       :       List[str]				=						json.load(open(cached_download(hf_hub_url(lowerCamelCase__       ,		lowerCamelCase__       ,		repo_type="""dataset"""  )  )       ,		"""r"""  )  )
  lowercase__       :       Dict				=						{int(lowerCamelCase__  ): v for k, v in idalabel.items()}
  lowercase__       :       Optional[int]				=						idalabel
  lowercase__       :       Tuple				=						{v: k for k, v in idalabel.items()}
  lowercase__       :       str				=						[1, 1_50, 4_80, 4_80]

 return config, expected_shape


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


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

 return name


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							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__       :       List[Any]				=						state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''  )
  lowercase__       :       Optional[Any]				=						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__       :       List[str]				=						in_proj_weight[: config.hidden_size, :]
  lowercase__       :       Union[str, Any]				=						in_proj_bias[: config.hidden_size]
  lowercase__       :       List[Any]				=						in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  lowercase__       :       Union[str, Any]				=						in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  lowercase__       :       Dict				=						in_proj_weight[
      -config.hidden_size :, :
  ]
  lowercase__       :       str				=						in_proj_bias[-config.hidden_size :]


def    _lowerCamelCase   (     ):
 lowercase__       :       List[Any]				=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowercase__       :       List[Any]				=						Image.open(requests.get(lowerCamelCase__       ,		stream=lowerCamelCase__  ).raw  )
 return im


@torch.no_grad()
def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							Optional[Any]       ,		lowerCamelCase__	:							Any  ):
 lowercase__	,       lowercase__       :       int				=						get_dpt_config(lowerCamelCase__  )
 # load original state_dict from URL
 lowercase__       :       Union[str, 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():
  lowercase__       :       Union[str, Any]				=						state_dict.pop(lowerCamelCase__  )
  lowercase__       :       int				=						val
 # read in qkv matrices
 read_in_q_k_v(lowerCamelCase__       ,		lowerCamelCase__  )

 # load HuggingFace model
 lowercase__       :       int				=						DPTForSemanticSegmentation(lowerCamelCase__  ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCamelCase__  )
 model.load_state_dict(lowerCamelCase__  )
 model.eval()

 # Check outputs on an image
 lowercase__       :       Dict				=						4_80 if """ade""" in checkpoint_url else 3_84
 lowercase__       :       Tuple				=						DPTImageProcessor(size=lowerCamelCase__  )

 lowercase__       :       Any				=						prepare_img()
 lowercase__       :       str				=						image_processor(lowerCamelCase__       ,		return_tensors="""pt"""  )

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

 # Assert logits
 lowercase__       :       List[str]				=						torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]  )
 if "ade" in checkpoint_url:
  lowercase__       :       List[Any]				=						torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]  )
 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__":
   __snake_case    					=					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.',
   )

   __snake_case    					=					parser.parse_args()
   convert_dpt_checkpoint(args.checkpoint_url, 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 PreTrainedTokenizer
from ...utils import logging


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					'▁'

__snake_case    					=					{'vocab_file': 'sentencepiece.bpe.model'}

__snake_case    					=					{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case    					=					{
    'facebook/xglm-564M': 2048,
}

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       Optional[int]									=    VOCAB_FILES_NAMES
   _a   :       Union[str, Any]									=    PRETRAINED_VOCAB_FILES_MAP
   _a   :       Dict									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a   :       Any									=    ['''input_ids''', '''attention_mask''']


   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="<unk>" ,     lowerCamelCase__="<pad>" ,     lowerCamelCase__ = None ,     **lowerCamelCase__ ,     )      ->							None:
    lowercase__       :       List[Any]				=						{} if sp_model_kwargs is None else sp_model_kwargs

    # Compatibility with the original tokenizer
    lowercase__       :       Any				=						7
    lowercase__       :       Dict				=						[F'''<madeupword{i}>''' for i in range(self.num_madeup_words						)]

    lowercase__       :       Tuple				=						kwargs.get("""additional_special_tokens""" ,     []						)
    kwargs["additional_special_tokens"] += [
        word for word in madeup_words if word not in kwargs["additional_special_tokens"]
    ]

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

    lowercase__       :       Optional[Any]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(str(lowerCamelCase__						)						)
    lowercase__       :       Optional[Any]				=						vocab_file

    # Original fairseq vocab and spm vocab must be "aligned":
    # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
    # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
    # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
    # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

    # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
    lowercase__       :       Optional[Any]				=						1

    # Mimic fairseq token-to-id alignment for the first 4 token
    lowercase__       :       Any				=						{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

    lowercase__       :       Optional[int]				=						len(self.sp_model						)
    lowercase__       :       Union[str, Any]				=						{F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words						)}
    self.fairseq_tokens_to_ids.update(lowerCamelCase__						)

    lowercase__       :       List[str]				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def __getstate__(						self						)      ->							Dict:
    lowercase__       :       Any				=						self.__dict__.copy()
    lowercase__       :       List[str]				=						None
    lowercase__       :       str				=						self.sp_model.serialized_model_proto()
    return state


   def __setstate__(						self ,     lowerCamelCase__						)      ->							Optional[int]:
    lowercase__       :       int				=						d

    # for backward compatibility
    if not hasattr(self ,     """sp_model_kwargs"""						):
     lowercase__       :       Union[str, Any]				=						{}

    lowercase__       :       Optional[int]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:

    if token_ids_a is None:
     return [self.sep_token_id] + token_ids_a
    lowercase__       :       List[str]				=						[self.sep_token_id]
    return sep + token_ids_a + sep + sep + token_ids_a


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None ,     lowerCamelCase__ = False						)      ->							List[int]:

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

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       str				=						[self.sep_token_id]

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


   @property
   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    return len(self.sp_model						) + self.fairseq_offset + self.num_madeup_words


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Optional[Any]				=						{self.convert_ids_to_tokens(lowerCamelCase__						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    return self.sp_model.encode(lowerCamelCase__ ,     out_type=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    lowercase__       :       List[Any]				=						self.sp_model.PieceToId(lowerCamelCase__						)

    # Need to return unknown token if the SP model returned 0
    return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    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 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Dict				=						"""""".join(lowerCamelCase__						).replace(lowerCamelCase__ ,     """ """						).strip()
    return out_string







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Tuple[str]:
    if not os.path.isdir(lowerCamelCase__						):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
     return
    lowercase__       :       int				=						os.path.join(
        lowerCamelCase__ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

    return (out_vocab_file,)