





"""simple docstring"""


# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
#     ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
#     --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
#     --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
#    1. --tf32 0 --fp16 0
#    2. --tf32 0 --fp16 1
#    3. --tf32 0 --bf16 1
#    4. --tf32 1 --fp16 0
#    5. --tf32 1 --fp16 1
#    6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
#    --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
#    --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
#    1. --tf32 0
#    2. --tf32 0 --fp16
#    3. --tf32 0 --bf16
#    4. --tf32 1
#    5. --tf32 1 --fp16
#    6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
#    `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation       |     Train |   Diff |   Train |
# |                 |   samples |      % |    loss |
# |                 |       per |        |         |
# |                 |    second |        |         |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0        |    285.11 |      0 |    2.51 |
# | --tf32 1        |    342.09 |     20 |    2.51 |
# | --fp16 --tf32 0 |    423.49 |     49 |    2.51 |
# | --fp16 --tf32 1 |    423.13 |     48 |    2.51 |
# | --bf16 --tf32 0 |    416.80 |     46 |    2.52 |
# | --bf16 --tf32 1 |    415.87 |     46 |    2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
#    --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#

import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


lowerCamelCase__						= float("nan")



class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							Tuple       , __a :							Any						)		->					Optional[Any]:
        _UpperCamelCase   :						int    =							sys.stdout
        _UpperCamelCase   :						Optional[Any]    =							open(__a       , "a"						)

  def __getattr__(		self :							str       , __a :							List[str]						)		->					Optional[Any]:
        return getattr(self.stdout       , __a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Dict       , __a :							Optional[int]						)		->					List[str]:
        self.stdout.write(__a						)
        # strip tqdm codes
        self.file.write(re.sub(R"^.*\r"       , ""       , __a       , 0       , re.M						)						)





def        lowercase__     (       lowercase_=80		,lowercase_=False      )       ->		Any:




      """simple docstring"""

      _UpperCamelCase   :						Union[str, Any]    =							[]

      # deal with critical env vars
      _UpperCamelCase   :						List[str]    =							["CUDA_VISIBLE_DEVICES"]
      for key in env_keys:
            _UpperCamelCase   :						Tuple    =							os.environ.get(lowercase_		,lowercase_      )
            if val is not None:
                  cmd.append(F'''{key}={val}'''      )

    # python executable (not always needed if the script is executable)
      _UpperCamelCase   :						Optional[Any]    =							sys.executable if full_python_path else sys.executable.split("/"      )[-1]
      cmd.append(lowercase_      )

      # now the normal args
      cmd += list(map(shlex.quote		,sys.argv      )      )

      # split up into up to MAX_WIDTH lines with shell multi-line escapes
      _UpperCamelCase   :						Tuple    =							[]
      _UpperCamelCase   :						Dict    =							""
      while len(lowercase_      ) > 0:
            current_line += F'''{cmd.pop(0      )} '''
            if len(lowercase_      ) == 0 or len(lowercase_      ) + len(cmd[0]      ) + 1 > max_width - 1:
                  lines.append(lowercase_      )
                  _UpperCamelCase   :						List[Any]    =							""
      return "\\\n".join(lowercase_      )




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Any:




      """simple docstring"""

      _UpperCamelCase   :						int    =							re.sub(r"[\\\n]+"		," "		,args.base_cmd      )

      # remove --output_dir if any and set our own
      _UpperCamelCase   :						List[str]    =							re.sub("--output_dir\s+[^\s]+"		,""		,args.base_cmd      )
      args.base_cmd += F''' --output_dir {output_dir}'''

      # ensure we have --overwrite_output_dir
      _UpperCamelCase   :						Dict    =							re.sub("--overwrite_output_dir\s+"		,""		,args.base_cmd      )
      args.base_cmd += " --overwrite_output_dir"

      return [sys.executable] + shlex.split(args.base_cmd      )




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		List[str]:




      """simple docstring"""

      if 0:
            import random
            from time import sleep

            sleep(0      )
            return dict(
                {k: random.uniform(0		,100      ) for k in metric_keys}		,**{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.2222_2222]      )}		,)

      _UpperCamelCase   :						int    =							subprocess.run(lowercase_		,capture_output=lowercase_		,text=lowercase_      )

      if verbose:
            print("STDOUT"		,result.stdout      )
            print("STDERR"		,result.stderr      )

      # save the streams
      _UpperCamelCase   :						Union[str, Any]    =							variation.replace(" "		,"-"      )
      with open(Path(lowercase_      ) / F'''log.{prefix}.stdout.txt'''		,"w"      ) as f:
            f.write(result.stdout      )
      with open(Path(lowercase_      ) / F'''log.{prefix}.stderr.txt'''		,"w"      ) as f:
            f.write(result.stderr      )

      if result.returncode != 0:
            if verbose:
                  print("failed"      )
            return {target_metric_key: nan}

      with io.open(F'''{output_dir}/all_results.json'''		,"r"		,encoding="utf-8"      ) as f:
            _UpperCamelCase   :						str    =							json.load(lowercase_      )

      # filter out just the keys we want
      return {k: v for k, v in metrics.items() if k in metric_keys}




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_		,)       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						str    =							[]
      _UpperCamelCase   :						List[str]    =							[]
      _UpperCamelCase   :						List[Any]    =							F'''{id}: {variation:<{longest_variation_len}}'''
      _UpperCamelCase   :						Optional[int]    =							F'''{preamble}: '''
      _UpperCamelCase   :						int    =							set(report_metric_keys + [target_metric_key]      )
      for i in tqdm(range(lowercase_      )		,desc=lowercase_		,leave=lowercase_      ):
            _UpperCamelCase   :						Optional[Any]    =							process_run_single(
                lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )
            _UpperCamelCase   :						int    =							single_run_metrics[target_metric_key]
            if not math.isnan(lowercase_      ):
                  metrics.append(lowercase_      )
                  results.append(lowercase_      )
                  outcome += "✓"
            else:
                  outcome += "✘"
      _UpperCamelCase   :						Any    =							F'''\33[2K\r{outcome}'''
      if len(lowercase_      ) > 0:
            _UpperCamelCase   :						Dict    =							{k: fmean([x[k] for x in metrics]      ) for k in metrics[0].keys()}
            _UpperCamelCase   :						Union[str, Any]    =							round(mean_metrics[target_metric_key]		,2      )
            _UpperCamelCase   :						List[str]    =							F'''{outcome} {mean_target}'''
            if len(lowercase_      ) > 1:
                  results_str += F''' {tuple(round(lowercase_		,2      ) for x in results      )}'''
            print(lowercase_      )
            _UpperCamelCase   :						List[str]    =							variation
            return mean_metrics
      else:
            print(lowercase_      )
            return {variation_key: variation, target_metric_key: nan}




def        lowercase__     (       )       ->		Dict:




      """simple docstring"""

      _UpperCamelCase   :						Tuple    =							torch.cuda.get_device_properties(torch.device("cuda"      )      )
      return F'''
Datetime    : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'      )}

Software:
transformers: {transformers.__version__}
torch       : {torch.__version__}
cuda        : {torch.version.cuda}
python      : {platform.python_version()}

Hardware:
{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB
'''




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		Optional[int]:




      """simple docstring"""

      _UpperCamelCase   :						Tuple    =							pd.DataFrame(lowercase_      )
      _UpperCamelCase   :						Optional[int]    =							"variation"
      _UpperCamelCase   :						Union[str, Any]    =							"diff_%"

      _UpperCamelCase   :						str    =							nan
      if base_variation is not None and len(df[df[variation_key] == base_variation]      ):
            # this may still return nan
            _UpperCamelCase   :						Dict    =							df.loc[df[variation_key] == base_variation][target_metric_key].item()
      if math.isnan(lowercase_      ):
            # as a fallback, use the minimal value as the sentinel
            _UpperCamelCase   :						Optional[Any]    =							df.loc[df[target_metric_key] != nan][target_metric_key].min()

      # create diff column if possible
      if not math.isnan(lowercase_      ):
            _UpperCamelCase   :						List[Any]    =							df.apply(
                lambda lowercase_      : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value      )
                if not math.isnan(r[target_metric_key]      )
                else 0		,axis="columns"		,)

      # re-order columns
      _UpperCamelCase   :						Any    =							[variation_key, target_metric_key, diff_key, *report_metric_keys]
      _UpperCamelCase   :						Tuple    =							df.reindex(lowercase_		,axis="columns"      )  # reorder cols

      # capitalize
      _UpperCamelCase   :						int    =							df.rename(str.capitalize		,axis="columns"      )

      # make the cols as narrow as possible
      _UpperCamelCase   :						Dict    =							df.rename(lambda lowercase_      : c.replace("_"		,"<br>"      )		,axis="columns"      )
      _UpperCamelCase   :						Tuple    =							df.rename(lambda lowercase_      : c.replace("_"		,"\n"      )		,axis="columns"      )

      _UpperCamelCase   :						Optional[Any]    =							["", "Copy between the cut-here-lines and paste as is to github or a forum"]
      report += ["----------8<-----------------8<--------"]
      report += ["*** Results:", df_github.to_markdown(index=lowercase_		,floatfmt=".2f"      )]
      report += ["```"]
      report += ["*** Setup:", get_versions()]
      report += ["*** The benchmark command line was:", get_original_command()]
      report += ["```"]
      report += ["----------8<-----------------8<--------"]
      report += ["*** Results (console):", df_console.to_markdown(index=lowercase_		,floatfmt=".2f"      )]

      print("\n\n".join(lowercase_      )      )




def        lowercase__     (       )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						str    =							argparse.ArgumentParser()
      parser.add_argument(
          "--base-cmd"		,default=lowercase_		,type=lowercase_		,required=lowercase_		,help="Base cmd"		,)
      parser.add_argument(
          "--variations"		,default=lowercase_		,type=lowercase_		,nargs="+"		,required=lowercase_		,help="Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'"		,)
      parser.add_argument(
          "--base-variation"		,default=lowercase_		,type=lowercase_		,help="Baseline variation to compare to. if None the minimal target value will be used to compare against"		,)
      parser.add_argument(
          "--target-metric-key"		,default=lowercase_		,type=lowercase_		,required=lowercase_		,help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second"		,)
      parser.add_argument(
          "--report-metric-keys"		,default=""		,type=lowercase_		,help="Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples"		,)
      parser.add_argument(
          "--repeat-times"		,default=1		,type=lowercase_		,help="How many times to re-run each variation - an average will be reported"		,)
      parser.add_argument(
          "--output_dir"		,default="output_benchmark"		,type=lowercase_		,help="The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked"		,)
      parser.add_argument(
          "--verbose"		,default=lowercase_		,action="store_true"		,help="Whether to show the outputs of each run or just the benchmark progress"		,)
      _UpperCamelCase   :						Tuple    =							parser.parse_args()

      _UpperCamelCase   :						str    =							args.output_dir
      Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
      _UpperCamelCase   :						Tuple    =							get_base_command(lowercase_		,lowercase_      )

      # split each dimension into its --foo variations
      _UpperCamelCase   :						Dict    =							[list(map(str.strip		,re.split(r"\|"		,lowercase_      )      )      ) for x in args.variations]
      # build a cartesian product of dimensions and convert those back into cmd-line arg strings,
      # while stripping white space for inputs that were empty
      _UpperCamelCase   :						Optional[Any]    =							list(map(str.strip		,map(" ".join		,itertools.product(*lowercase_      )      )      )      )
      _UpperCamelCase   :						Optional[int]    =							max(len(lowercase_      ) for x in variations      )

      # split wanted keys
      _UpperCamelCase   :						Tuple    =							args.report_metric_keys.split()

      # capture prints into a log file for convenience
      _UpperCamelCase   :						Optional[Any]    =							F'''benchmark-report-{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S'      )}.txt'''
      print(F'''\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt'''      )
      print(F'''and this script\'s output is also piped into {report_fn}'''      )

      _UpperCamelCase   :						List[Any]    =							Tee(lowercase_      )

      print(F'''\n*** Running {len(lowercase_      )} benchmarks:'''      )
      print(F'''Base command: {' '.join(lowercase_      )}'''      )

      _UpperCamelCase   :						List[Any]    =							"variation"
      _UpperCamelCase   :						List[Any]    =							[]
      for id, variation in enumerate(tqdm(lowercase_		,desc="Total completion: "		,leave=lowercase_      )      ):
            _UpperCamelCase   :						Any    =							base_cmd + variation.split()
            results.append(
                process_run(
                    id + 1		,lowercase_		,lowercase_		,lowercase_		,lowercase_		,args.target_metric_key		,lowercase_		,args.repeat_times		,lowercase_		,args.verbose		,)      )

      process_results(lowercase_		,args.target_metric_key		,lowercase_		,args.base_variation		,lowercase_      )


if __name__ == "__main__":
  main()







"""simple docstring"""


import argparse
import json
from pathlib import Path

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

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


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




def        lowercase__     (       lowercase_      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							DPTConfig(embedding_type="hybrid"      )

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

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

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

      return config, expected_shape




def        lowercase__     (       lowercase_      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							["pretrained.model.head.weight", "pretrained.model.head.bias"]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		int:




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

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

      if ".." in name:
            _UpperCamelCase   :						List[str]    =							name.replace(".."		,"."      )

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




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Dict:




      """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)
            _UpperCamelCase   :						Any    =							state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''      )
            _UpperCamelCase   :						Tuple    =							state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''      )
            # next, add query, keys and values (in that order) to the state dict
            _UpperCamelCase   :						Tuple    =							in_proj_weight[: config.hidden_size, :]
            _UpperCamelCase   :						Dict    =							in_proj_bias[: config.hidden_size]
            _UpperCamelCase   :						Optional[int]    =							in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _UpperCamelCase   :						Union[str, Any]    =							in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _UpperCamelCase   :						Dict    =							in_proj_weight[
                -config.hidden_size :, :
            ]
            _UpperCamelCase   :						List[Any]    =							in_proj_bias[-config.hidden_size :]




def        lowercase__     (       )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							"http://images.cocodataset.org/val2017/000000039769.jpg"
      _UpperCamelCase   :						Dict    =							Image.open(requests.get(lowercase_		,stream=lowercase_      ).raw      )
      return im




@torch.no_grad()
def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		Union[str, Any]:




      """simple docstring"""

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

      # load HuggingFace model
      _UpperCamelCase   :						Dict    =							DPTForSemanticSegmentation(lowercase_      ) if "ade" in checkpoint_url else DPTForDepthEstimation(lowercase_      )
      model.load_state_dict(lowercase_      )
      model.eval()

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

      _UpperCamelCase   :						int    =							prepare_img()
      _UpperCamelCase   :						List[str]    =							image_processor(lowercase_		,return_tensors="pt"      )

      # forward pass
      _UpperCamelCase   :						Any    =							model(**lowercase_      ).logits if "ade" in checkpoint_url else model(**lowercase_      ).predicted_depth

      if show_prediction:
            _UpperCamelCase   :						Optional[int]    =							(
                torch.nn.functional.interpolate(
                    outputs.unsqueeze(1      )		,size=(image.size[1], image.size[0])		,mode="bicubic"		,align_corners=lowercase_		,)
                .squeeze()
                .cpu()
                .numpy()
            )

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

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

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