


'''simple docstring'''




import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length





def   _a		(lowercase__			:   Optional[Any]						)			->			Optional[Any]:






     """simple docstring"""
     __snake_case           =						int(lowercase__						)
     __snake_case      ,       __snake_case      ,       __snake_case           =						t // 3_6_0_0, (t // 6_0) % 6_0, t % 6_0
     return f'{h}:{m:02d}:{s:02d}' if h != 0 else f'{m:02d}:{s:02d}'





def   _a		(lowercase__			:   Optional[int]							,   lowercase__			:   Optional[int]							,   lowercase__			:   Optional[int]							,   lowercase__			:   Tuple							,   lowercase__			:   List[str]=3_0_0						)			->			Optional[int]:






     """simple docstring"""
     # docstyle-ignore
     return f'\n    <div>\n      {prefix}\n      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>\n      {label}\n    </div>\n    '





def   _a		(lowercase__			:   Optional[int]						)			->			Optional[Any]:






     """simple docstring"""
     __snake_case           =						'<table border="1" class="dataframe">\n'
     html_code += """  <thead>\n <tr style="text-align: left;">\n"""
     for i in items[0]:
          html_code += f'      <th>{i}</th>\n'
     html_code += "    </tr>\n  </thead>\n  <tbody>\n"
     for line in items[1:]:
          html_code += "    <tr>\n"
          for elt in line:
               __snake_case           =						f'{elt:.6f}' if isinstance(lowercase__							,   lowercase__						) else str(lowercase__						)
               html_code += f'      <td>{elt}</td>\n'
          html_code += "    </tr>\n"
     html_code += "  </tbody>\n</table><p>"
     return html_code




class 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  Union[str, Any]          =	5
   _SCREAMING_SNAKE_CASE  :  List[str]          =	0.2
   def __init__(						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      Optional[str] = None       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   SCREAMING_SNAKE_CASE_       :      Optional["NotebookTrainingTracker"] = None       ,   SCREAMING_SNAKE_CASE_       :      int = 300       ,   ) ->							List[Any]:
        __snake_case           =						total
        __snake_case           =						'' if prefix is None else prefix
        __snake_case           =						leave
        __snake_case           =						parent
        __snake_case           =						width
        __snake_case           =						None
        __snake_case           =						None
        __snake_case           =						None
   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      int       ,   SCREAMING_SNAKE_CASE_       :      bool = False       ,   SCREAMING_SNAKE_CASE_       :      str = None      ) ->							Any:
        __snake_case           =						value
        if comment is not None:
             __snake_case           =						comment
        if self.last_value is None:
             __snake_case           =						__snake_case           =						time.time()
             __snake_case           =						__snake_case           =						value
             __snake_case           =						__snake_case           =						None
             __snake_case           =						self.warmup
             __snake_case           =						1
             self.update_bar(SCREAMING_SNAKE_CASE_      )
        elif value <= self.last_value and not force_update:
             return
        elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for       ,   self.total      ):
             if self.first_calls > 0:
                  self.first_calls -= 1
             __snake_case           =						time.time()
             __snake_case           =						current_time - self.start_time
             # We could have value = self.start_value if the update is called twixe with the same start value.
             if value > self.start_value:
                  __snake_case           =						self.elapsed_time / (value - self.start_value)
             else:
                  __snake_case           =						None
             if value >= self.total:
                  __snake_case           =						self.total
                  __snake_case           =						None
                  if not self.leave:
                       self.close()
             elif self.average_time_per_item is not None:
                  __snake_case           =						self.average_time_per_item * (self.total - value)
             self.update_bar(SCREAMING_SNAKE_CASE_      )
             __snake_case           =						value
             __snake_case           =						current_time
             if self.average_time_per_item is None:
                  __snake_case           =						1
             else:
                  __snake_case           =						max(int(self.update_every / self.average_time_per_item      )       ,   1      )
   def a  (						self       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      List[str]=None      ) ->							str:
        __snake_case           =						' ' * (len(str(self.total      )      ) - len(str(SCREAMING_SNAKE_CASE_      )      )) + str(SCREAMING_SNAKE_CASE_      )
        if self.elapsed_time is None:
             __snake_case           =						f'[{spaced_value}/{self.total} : < :'
        elif self.predicted_remaining is None:
             __snake_case           =						f'[{spaced_value}/{self.total} {format_time(self.elapsed_time      )}'
        else:
             __snake_case           =						(
                 f'[{spaced_value}/{self.total} {format_time(self.elapsed_time      )} <'
                 f' {format_time(self.predicted_remaining      )}'
             )
             self.label += f', {1/self.average_time_per_item:.2f} it/s'
        self.label += "]" if self.comment is None or len(self.comment      ) == 0 else f', {self.comment}]'
        self.display()
   def a  (						self       :      Optional[int]      ) ->							Any:
        __snake_case           =						html_progress_bar(self.value       ,   self.total       ,   self.prefix       ,   self.label       ,   self.width      )
        if self.parent is not None:
             # If this is a child bar, the parent will take care of the display.
             self.parent.display()
             return
        if self.output is None:
             __snake_case           =						disp.display(disp.HTML(self.html_code      )       ,   display_id=SCREAMING_SNAKE_CASE_      )
        else:
             self.output.update(disp.HTML(self.html_code      )      )





   def a  (						self       :      Dict      ) ->							Union[str, Any]:
        if self.parent is None and self.output is not None:
             self.output.update(disp.HTML(''      )      )




class 			_lowercase (	__lowercase							):
   def __init__(						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=None      ) ->							Union[str, Any]:
        super().__init__(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						None if column_names is None else [column_names]
        __snake_case           =						None
   def a  (						self       :      Dict      ) ->							Optional[Any]:
        __snake_case           =						html_progress_bar(self.value       ,   self.total       ,   self.prefix       ,   self.label       ,   self.width      )
        if self.inner_table is not None:
             self.html_code += text_to_html_table(self.inner_table      )
        if self.child_bar is not None:
             self.html_code += self.child_bar.html_code
        if self.output is None:
             __snake_case           =						disp.display(disp.HTML(self.html_code      )       ,   display_id=SCREAMING_SNAKE_CASE_      )
        else:
             self.output.update(disp.HTML(self.html_code      )      )
   def a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]      ) ->							Optional[Any]:
        if self.inner_table is None:
             __snake_case           =						[list(values.keys()      ), list(values.values()      )]
        else:
             __snake_case           =						self.inner_table[0]
             if len(self.inner_table      ) == 1:
                  # We give a chance to update the column names at the first iteration
                  for key in values.keys():
                       if key not in columns:
                            columns.append(SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						columns
             self.inner_table.append([values[c] for c in columns]      )
   def a  (						self       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      int=None       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=300      ) ->							Any:
        __snake_case           =						NotebookProgressBar(SCREAMING_SNAKE_CASE_       ,   prefix=SCREAMING_SNAKE_CASE_       ,   parent=self       ,   width=SCREAMING_SNAKE_CASE_      )
        return self.child_bar





   def a  (						self       :      Optional[int]      ) ->							Any:
        __snake_case           =						None
        self.display()




class 			_lowercase (	__lowercase							):
   def __init__(						self       :      Any      ) ->							List[Any]:
        __snake_case           =						None
        __snake_case           =						None
        __snake_case           =						False
   def a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Any       ,   SCREAMING_SNAKE_CASE_       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]       ,   **SCREAMING_SNAKE_CASE_       :      Any      ) ->							Union[str, Any]:
        __snake_case           =						'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
        __snake_case           =						0
        __snake_case           =						0
        __snake_case           =						[self.first_column] + ['Training Loss']
        if args.evaluation_strategy != IntervalStrategy.NO:
             column_names.append('Validation Loss'      )
        __snake_case           =						NotebookTrainingTracker(state.max_steps       ,   SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   **SCREAMING_SNAKE_CASE_       :      Tuple      ) ->							Any:
        __snake_case           =						int(state.epoch      ) if int(state.epoch      ) == state.epoch else f'{state.epoch:.2f}'
        self.training_tracker.update(
            state.global_step + 1       ,   comment=f'Epoch {epoch}/{state.num_train_epochs}'       ,   force_update=self._force_next_update       ,   )
        __snake_case           =						False
   def a  (						self       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=None       ,   **SCREAMING_SNAKE_CASE_       :      Tuple      ) ->							Tuple:
        if not has_length(SCREAMING_SNAKE_CASE_      ):
             return
        if self.prediction_bar is None:
             if self.training_tracker is not None:
                  __snake_case           =						self.training_tracker.add_child(len(SCREAMING_SNAKE_CASE_      )      )
             else:
                  __snake_case           =						NotebookProgressBar(len(SCREAMING_SNAKE_CASE_      )      )
             self.prediction_bar.update(1      )
        else:
             self.prediction_bar.update(self.prediction_bar.value + 1      )
   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Dict       ,   **SCREAMING_SNAKE_CASE_       :      List[str]      ) ->							Optional[int]:
        if self.prediction_bar is not None:
             self.prediction_bar.close()
        __snake_case           =						None
   def a  (						self       :      Any       ,   SCREAMING_SNAKE_CASE_       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      str       ,   SCREAMING_SNAKE_CASE_       :      List[str]=None       ,   **SCREAMING_SNAKE_CASE_       :      List[str]      ) ->							Optional[int]:
        # Only for when there is no evaluation
        if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
             __snake_case           =						{'Training Loss': logs['loss']}
             # First column is necessarily Step sine we're not in epoch eval strategy
             __snake_case           =						state.global_step
             self.training_tracker.write_line(SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Dict       ,   SCREAMING_SNAKE_CASE_       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      str       ,   SCREAMING_SNAKE_CASE_       :      Dict=None       ,   **SCREAMING_SNAKE_CASE_       :      List[str]      ) ->							List[Any]:
        if self.training_tracker is not None:
             __snake_case           =						{'Training Loss': 'No log', 'Validation Loss': 'No log'}
             for log in reversed(state.log_history      ):
                  if "loss" in log:
                       __snake_case           =						log['loss']
                       break

             if self.first_column == "Epoch":
                  __snake_case           =						int(state.epoch      )
             else:
                  __snake_case           =						state.global_step
             __snake_case           =						'eval'
             for k in metrics:
                  if k.endswith('_loss'      ):
                       __snake_case           =						re.sub(r'\_loss$'       ,   ''       ,   SCREAMING_SNAKE_CASE_      )
             __snake_case           =						metrics.pop('total_flos'       ,   SCREAMING_SNAKE_CASE_      )
             __snake_case           =						metrics.pop('epoch'       ,   SCREAMING_SNAKE_CASE_      )
             __snake_case           =						metrics.pop(f'{metric_key_prefix}_runtime'       ,   SCREAMING_SNAKE_CASE_      )
             __snake_case           =						metrics.pop(f'{metric_key_prefix}_samples_per_second'       ,   SCREAMING_SNAKE_CASE_      )
             __snake_case           =						metrics.pop(f'{metric_key_prefix}_steps_per_second'       ,   SCREAMING_SNAKE_CASE_      )
             __snake_case           =						metrics.pop(f'{metric_key_prefix}_jit_compilation_time'       ,   SCREAMING_SNAKE_CASE_      )
             for k, v in metrics.items():
                  if k == f'{metric_key_prefix}_loss':
                       __snake_case           =						v
                  else:
                       __snake_case           =						k.split('_'      )
                       __snake_case           =						' '.join([part.capitalize() for part in splits[1:]]      )
                       __snake_case           =						v
             self.training_tracker.write_line(SCREAMING_SNAKE_CASE_      )
             self.training_tracker.remove_child()
             __snake_case           =						None
             # Evaluation takes a long time so we should force the next update.
             __snake_case           =						True





   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      List[Any]       ,   **SCREAMING_SNAKE_CASE_       :      List[Any]      ) ->							Optional[Any]:
        self.training_tracker.update(
            state.global_step       ,   comment=f'Epoch {int(state.epoch      )}/{state.num_train_epochs}'       ,   force_update=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						None










'''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
 )
