



'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__a  			=						logging.get_logger(__name__)

__a  			=						'▁'

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

__a  			=						{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

__a  			=						{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
__a  			=						['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']







class        UpperCAmelCase_			(			snake_case_      ):
    """simple docstring"""







    lowercase   =						VOCAB_FILES_NAMES
    lowercase   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase   =						PRETRAINED_VOCAB_FILES_MAP
    lowercase   =						['''input_ids''', '''attention_mask''']

    lowercase   =						[]
    lowercase   =						[]




    def __init__(       self   :	int					,	snake_case_   :	Tuple					,	snake_case_   :	Optional[int]=None					,	snake_case_   :	str=None					,	snake_case_   :	Dict="</s>"					,	snake_case_   :	Tuple="</s>"					,	snake_case_   :	Any="<s>"					,	snake_case_   :	Tuple="<unk>"					,	snake_case_   :	List[Any]="<pad>"					,	snake_case_   :	Dict="<mask>"					,	snake_case_   :	Optional[Dict[str, Any]] = None					,	**snake_case_   :	Tuple					,	):
          snake_case__    :       Any       		= AddedToken(lowercase__					,	lstrip=lowercase__					,	rstrip=lowercase__		) if isinstance(lowercase__					,	lowercase__		) else mask_token

          snake_case__    :       Union[str, Any]       		= {} if sp_model_kwargs is None else sp_model_kwargs

          snake_case__    :       int       		= kwargs.get("""additional_special_tokens"""					,	[]		)
          kwargs["additional_special_tokens"] += [
              code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=lowercase__					,	tgt_lang=lowercase__					,	eos_token=lowercase__					,	unk_token=lowercase__					,	sep_token=lowercase__					,	cls_token=lowercase__					,	pad_token=lowercase__					,	mask_token=lowercase__					,	sp_model_kwargs=self.sp_model_kwargs					,	**lowercase__					,	)

          snake_case__    :       Union[str, Any]       		= spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(str(lowercase__		)		)
          snake_case__    :       Union[str, 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'

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

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          snake_case__    :       List[str]       		= 1

          snake_case__    :       List[Any]       		= len(self.sp_model		)
          snake_case__    :       List[str]       		= {
              code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase__		)
          }
          snake_case__    :       str       		= {v: k for k, v in self.lang_code_to_id.items()}
          snake_case__    :       Any       		= len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

          self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
          snake_case__    :       int       		= {v: k for k, v in self.fairseq_tokens_to_ids.items()}

          snake_case__    :       List[Any]       		= src_lang if src_lang is not None else """en_XX"""
          snake_case__    :       Any       		= self.lang_code_to_id[self._src_lang]
          snake_case__    :       Union[str, Any]       		= tgt_lang
          self.set_src_lang_special_tokens(self._src_lang		)




    @property
    def lowerCamelCase			(       self   :	Union[str, Any]		):
          return len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset + 1  # Plus 1 for the mask token




    @property
    def lowerCamelCase			(       self   :	Tuple		):
          return self._src_lang




    @src_lang.setter
    def lowerCamelCase			(       self   :	Optional[Any]					,	snake_case_   :	str		):
          snake_case__    :       Tuple       		= new_src_lang
          self.set_src_lang_special_tokens(self._src_lang		)




    def __getstate__(       self   :	Any		):
          snake_case__    :       str       		= self.__dict__.copy()
          snake_case__    :       Dict       		= None
          return state




    def __setstate__(       self   :	Optional[Any]					,	snake_case_   :	Dict		):
          snake_case__    :       Any       		= d

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

          snake_case__    :       int       		= spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(self.vocab_file		)




    def lowerCamelCase			(       self   :	int		):
          snake_case__    :       Dict       		= {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[int]					,	snake_case_   :	str		):
          return self.sp_model.encode(lowercase__					,	out_type=lowercase__		)




    def lowerCamelCase			(       self   :	Tuple					,	snake_case_   :	str		):

          if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
          snake_case__    :       List[str]       		= self.sp_model.PieceToId(lowercase__		)

          # 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 lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	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 lowerCamelCase			(       self   :	Tuple					,	snake_case_   :	str		):
          snake_case__    :       Union[str, Any]       		= []
          snake_case__    :       List[str]       		= """"""
          snake_case__    :       Dict       		= 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
                      snake_case__    :       str       		= True
                      snake_case__    :       Optional[Any]       		= []
                else:
                      current_sub_tokens.append(lowercase__		)
                      snake_case__    :       Union[str, Any]       		= False
          out_string += self.sp_model.decode(lowercase__		)
          return out_string.strip()




    def lowerCamelCase			(       self   :	Tuple					,	snake_case_   :	str					,	snake_case_   :	Optional[str] = None		):

          if not os.path.isdir(lowercase__		):
                logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
                return
          snake_case__    :       Dict       		= 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:
                      snake_case__    :       Dict       		= self.sp_model.serialized_model_proto()
                      fi.write(lowercase__		)

          return (out_vocab_file,)




    def lowerCamelCase			(       self   :	List[str]					,	snake_case_   :	List[int]					,	snake_case_   :	Optional[List[int]] = None					,	snake_case_   :	bool = False		):

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

          snake_case__    :       Union[str, Any]       		= [1] * len(self.prefix_tokens		)
          snake_case__    :       List[Any]       		= [1] * len(self.suffix_tokens		)
          if token_ids_a is None:
                return prefix_ones + ([0] * len(lowercase__		)) + suffix_ones
          return prefix_ones + ([0] * len(lowercase__		)) + ([0] * len(lowercase__		)) + suffix_ones




    def lowerCamelCase			(       self   :	List[str]					,	snake_case_   :	List[int]					,	snake_case_   :	Optional[List[int]] = None		):

          if token_ids_a is None:
                return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Optional[int]					,	snake_case_   :	str					,	snake_case_   :	Optional[str]					,	snake_case_   :	Optional[str]					,	**snake_case_   :	Optional[int]		):

          if src_lang is None or tgt_lang is None:
                raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""		)
          snake_case__    :       Union[str, Any]       		= src_lang
          snake_case__    :       Dict       		= self(lowercase__					,	add_special_tokens=lowercase__					,	return_tensors=lowercase__					,	**lowercase__		)
          snake_case__    :       Tuple       		= self.convert_tokens_to_ids(lowercase__		)
          snake_case__    :       Optional[Any]       		= tgt_lang_id
          return inputs




    def lowerCamelCase			(       self   :	Optional[Any]					,	snake_case_   :	List[str]					,	snake_case_   :	str = "en_XX"					,	snake_case_   :	Optional[List[str]] = None					,	snake_case_   :	str = "ro_RO"					,	**snake_case_   :	Union[str, Any]					,	):
          snake_case__    :       str       		= src_lang
          snake_case__    :       Dict       		= tgt_lang
          return super().prepare_seqaseq_batch(lowercase__					,	lowercase__					,	**lowercase__		)




    def lowerCamelCase			(       self   :	List[Any]		):
          return self.set_src_lang_special_tokens(self.src_lang		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          return self.set_tgt_lang_special_tokens(self.tgt_lang		)




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	str		):
          snake_case__    :       Any       		= self.lang_code_to_id[src_lang]
          snake_case__    :       Dict       		= [self.cur_lang_code_id]
          snake_case__    :       Any       		= [self.eos_token_id]




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	str		):
          snake_case__    :       Union[str, Any]       		= self.lang_code_to_id[tgt_lang]
          snake_case__    :       str       		= [self.cur_lang_code_id]
          snake_case__    :       Union[str, Any]       		= [self.eos_token_id]






'''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_      :  Optional[int]				     =  logging.get_logger(__name__)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int]				):




     """simple docstring"""


     a_     :							List[Any]     						=  DPTConfig(embedding_type="""hybrid"""				)

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

     if "nyu" or "midas" in checkpoint_url:
          a_     :							Dict     						=  768
          a_     :							str     						=  [1, 1, 1, 0.5]
          a_     :							Dict     						=  [256, 512, 768, 768]
          a_     :							int     						=  150
          a_     :							Any     						=  16
          a_     :							Optional[int]     						=  (1, 384, 384)
          a_     :							Optional[Any]     						=  False
          a_     :							Dict     						=  """project"""

     if "ade" in checkpoint_url:
          a_     :							int     						=  True
          a_     :							int     						=  768
          a_     :							Optional[int]     						=  [1, 1, 1, 0.5]
          a_     :							Dict     						=  150
          a_     :							Any     						=  16
          a_     :							Optional[int]     						=  """huggingface/label-files"""
          a_     :							Optional[int]     						=  """ade20k-id2label.json"""
          a_     :							Union[str, Any]     						=  json.load(open(cached_download(hf_hub_url(UpperCamelCase__ ,    UpperCamelCase__ ,    repo_type="""dataset"""				)				) ,    """r"""				)				)
          a_     :							str     						=  {int(UpperCamelCase__				): v for k, v in idalabel.items()}
          a_     :							Optional[int]     						=  idalabel
          a_     :							Optional[Any]     						=  {v: k for k, v in idalabel.items()}
          a_     :							int     						=  [1, 150, 480, 480]

     return config, expected_shape



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int]				):




     """simple docstring"""


     a_     :							Dict     						=  ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
     for k in ignore_keys:
          state_dict.pop(UpperCamelCase__ ,    UpperCamelCase__				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					str				):




     """simple docstring"""


     if (
         "pretrained.model" in name
         and "cls_token" not in name
         and "pos_embed" not in name
         and "patch_embed" not in name
     ):
          a_     :							List[str]     						=  name.replace("""pretrained.model""" ,    """dpt.encoder"""				)
     if "pretrained.model" in name:
          a_     :							Union[str, Any]     						=  name.replace("""pretrained.model""" ,    """dpt.embeddings"""				)
     if "patch_embed" in name:
          a_     :							Optional[int]     						=  name.replace("""patch_embed""" ,    """"""				)
     if "pos_embed" in name:
          a_     :							List[str]     						=  name.replace("""pos_embed""" ,    """position_embeddings"""				)
     if "attn.proj" in name:
          a_     :							Any     						=  name.replace("""attn.proj""" ,    """attention.output.dense"""				)
     if "proj" in name and "project" not in name:
          a_     :							Dict     						=  name.replace("""proj""" ,    """projection"""				)
     if "blocks" in name:
          a_     :							Union[str, Any]     						=  name.replace("""blocks""" ,    """layer"""				)
     if "mlp.fc1" in name:
          a_     :							Optional[int]     						=  name.replace("""mlp.fc1""" ,    """intermediate.dense"""				)
     if "mlp.fc2" in name:
          a_     :							Any     						=  name.replace("""mlp.fc2""" ,    """output.dense"""				)
     if "norm1" in name and "backbone" not in name:
          a_     :							List[Any]     						=  name.replace("""norm1""" ,    """layernorm_before"""				)
     if "norm2" in name and "backbone" not in name:
          a_     :							int     						=  name.replace("""norm2""" ,    """layernorm_after"""				)
     if "scratch.output_conv" in name:
          a_     :							Tuple     						=  name.replace("""scratch.output_conv""" ,    """head"""				)
     if "scratch" in name:
          a_     :							str     						=  name.replace("""scratch""" ,    """neck"""				)
     if "layer1_rn" in name:
          a_     :							Optional[Any]     						=  name.replace("""layer1_rn""" ,    """convs.0"""				)
     if "layer2_rn" in name:
          a_     :							Dict     						=  name.replace("""layer2_rn""" ,    """convs.1"""				)
     if "layer3_rn" in name:
          a_     :							Tuple     						=  name.replace("""layer3_rn""" ,    """convs.2"""				)
     if "layer4_rn" in name:
          a_     :							str     						=  name.replace("""layer4_rn""" ,    """convs.3"""				)
     if "refinenet" in name:
          a_     :							List[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
          a_     :							List[str]     						=  name.replace(F"refinenet{layer_idx}" ,    F"fusion_stage.layers.{abs(layer_idx-4				)}"				)
     if "out_conv" in name:
          a_     :							Union[str, Any]     						=  name.replace("""out_conv""" ,    """projection"""				)
     if "resConfUnit1" in name:
          a_     :							Any     						=  name.replace("""resConfUnit1""" ,    """residual_layer1"""				)
     if "resConfUnit2" in name:
          a_     :							List[str]     						=  name.replace("""resConfUnit2""" ,    """residual_layer2"""				)
     if "conv1" in name:
          a_     :							str     						=  name.replace("""conv1""" ,    """convolution1"""				)
     if "conv2" in name:
          a_     :							Dict     						=  name.replace("""conv2""" ,    """convolution2"""				)
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          a_     :							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:
          a_     :							int     						=  name.replace("""pretrained.act_postprocess2.0.project.0""" ,    """neck.reassemble_stage.readout_projects.1.0"""				)
     if "pretrained.act_postprocess3.0.project.0" in name:
          a_     :							Optional[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:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess4.0.project.0""" ,    """neck.reassemble_stage.readout_projects.3.0"""				)

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess1.3""" ,    """neck.reassemble_stage.layers.0.projection"""				)
     if "pretrained.act_postprocess1.4" in name:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess1.4""" ,    """neck.reassemble_stage.layers.0.resize"""				)
     if "pretrained.act_postprocess2.3" in name:
          a_     :							str     						=  name.replace("""pretrained.act_postprocess2.3""" ,    """neck.reassemble_stage.layers.1.projection"""				)
     if "pretrained.act_postprocess2.4" in name:
          a_     :							Optional[int]     						=  name.replace("""pretrained.act_postprocess2.4""" ,    """neck.reassemble_stage.layers.1.resize"""				)
     if "pretrained.act_postprocess3.3" in name:
          a_     :							Optional[Any]     						=  name.replace("""pretrained.act_postprocess3.3""" ,    """neck.reassemble_stage.layers.2.projection"""				)
     if "pretrained.act_postprocess4.3" in name:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess4.3""" ,    """neck.reassemble_stage.layers.3.projection"""				)
     if "pretrained.act_postprocess4.4" in name:
          a_     :							Optional[Any]     						=  name.replace("""pretrained.act_postprocess4.4""" ,    """neck.reassemble_stage.layers.3.resize"""				)
     if "pretrained" in name:
          a_     :							int     						=  name.replace("""pretrained""" ,    """dpt"""				)
     if "bn" in name:
          a_     :							Any     						=  name.replace("""bn""" ,    """batch_norm"""				)
     if "head" in name:
          a_     :							int     						=  name.replace("""head""" ,    """head.head"""				)
     if "encoder.norm" in name:
          a_     :							Any     						=  name.replace("""encoder.norm""" ,    """layernorm"""				)
     if "auxlayer" in name:
          a_     :							Union[str, Any]     						=  name.replace("""auxlayer""" ,    """auxiliary_head.head"""				)
     if "backbone" in name:
          a_     :							Any     						=  name.replace("""backbone""" ,    """backbone.bit.encoder"""				)

     if ".." in name:
          a_     :							Any     						=  name.replace("""..""" ,    """."""				)

     if "stem.conv" in name:
          a_     :							Optional[int]     						=  name.replace("""stem.conv""" ,    """bit.embedder.convolution"""				)
     if "blocks" in name:
          a_     :							Dict     						=  name.replace("""blocks""" ,    """layers"""				)
     if "convolution" in name and "backbone" in name:
          a_     :							str     						=  name.replace("""convolution""" ,    """conv"""				)
     if "layer" in name and "backbone" in name:
          a_     :							Any     						=  name.replace("""layer""" ,    """layers"""				)
     if "backbone.bit.encoder.bit" in name:
          a_     :							Dict     						=  name.replace("""backbone.bit.encoder.bit""" ,    """backbone.bit"""				)
     if "embedder.conv" in name:
          a_     :							List[str]     						=  name.replace("""embedder.conv""" ,    """embedder.convolution"""				)
     if "backbone.bit.encoder.stem.norm" in name:
          a_     :							int     						=  name.replace("""backbone.bit.encoder.stem.norm""" ,    """backbone.bit.embedder.norm"""				)
     return name



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					str				):




     """simple docstring"""


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



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							Optional[Any]     						=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     a_     :							Union[str, Any]     						=  Image.open(requests.get(UpperCamelCase__ ,    stream=UpperCamelCase__				).raw				)
     return im



@torch.no_grad()
def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


     a_						,     a_     :							Optional[Any]     						=  get_dpt_config(UpperCamelCase__				)
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     a_     :							int     						=  torch.load(UpperCamelCase__ ,    map_location="""cpu"""				)
     # remove certain keys
     remove_ignore_keys_(UpperCamelCase__				)
     # rename keys
     for key in state_dict.copy().keys():
          a_     :							Any     						=  state_dict.pop(UpperCamelCase__				)
          a_     :							Tuple     						=  val
     # read in qkv matrices
     read_in_q_k_v(UpperCamelCase__ ,    UpperCamelCase__				)

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

     # Check outputs on an image
     a_     :							str     						=  480 if """ade""" in checkpoint_url else 384
     a_     :							Optional[int]     						=  DPTImageProcessor(size=UpperCamelCase__				)

     a_     :							Dict     						=  prepare_img()
     a_     :							Any     						=  image_processor(UpperCamelCase__ ,    return_tensors="""pt"""				)

     # forward pass
     a_     :							Dict     						=  model(**UpperCamelCase__				).logits if """ade""" in checkpoint_url else model(**UpperCamelCase__				).predicted_depth

     if show_prediction:
          a_     :							Any     						=  (
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1				) ,    size=(image.size[1], image.size[0]) ,    mode="""bicubic""" ,    align_corners=UpperCamelCase__ ,    )
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 255				).show()

     if pytorch_dump_folder_path is not None:
          Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
          print(F"Saving model to {pytorch_dump_folder_path}"				)
          model.save_pretrained(UpperCamelCase__				)
          print(F"Saving image processor to {pytorch_dump_folder_path}"				)
          image_processor.save_pretrained(UpperCamelCase__				)

     if push_to_hub:
          model.push_to_hub("""ybelkada/dpt-hybrid-midas"""				)
          image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""				)


if __name__ == "__main__":
 lowerCAmelCase_      :  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=False,
     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.',
 )
 parser.add_argument(
     '--show_prediction',
     action='store_true',
 )

 lowerCAmelCase_      :  Optional[Any]				     =  parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )
