





'''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


_SCREAMING_SNAKE_CASE							     =			float("nan")
class 			_lowerCAmelCase		:






     """simple docstring"""






     def __init__(   self							:			str   ,       __snake_case							:			Union[str, Any]						)->	Optional[int]:
        snake_case 						=   sys.stdout
        snake_case 						=   open(__snake_case   ,       """a"""						)



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



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			Tuple						)->	Union[str, Any]:
        self.stdout.write(__snake_case						)
        # strip tqdm codes
        self.file.write(re.sub(r"""^.*\r"""   ,       """"""   ,       __snake_case   ,       0   ,       re.M						)						)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str=80      ,    __lowerCAmelCase       :							int=False		)		->  Optional[Any]:
   snake_case 						=   []

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

    # python executable (not always needed if the script is executable)
   snake_case 						=   sys.executable if full_python_path else sys.executable.split("""/"""		)[-1]
   cmd.append(__lowerCAmelCase		)

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

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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Optional[Any]		)		->  Optional[Any]:
   # unwrap multi-line input
   snake_case 						=   re.sub(r"""[\\\n]+"""      ,    """ """      ,    args.base_cmd		)

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

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

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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Optional[int]		)		->  int:
   # Enable to debug everything but the run itself, to do it fast and see the progress.
   # This is useful for debugging the output formatting quickly - we can remove it later once
   # everybody is happy with the output
   if 0:
      import random
      from time import sleep

      sleep(0		)
      return dict(
          {k: random.uniform(0      ,    1_00		) for k in metric_keys}      ,    **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.2222_2222]		)}      ,    )

   snake_case 						=   subprocess.run(__lowerCAmelCase      ,    capture_output=__lowerCAmelCase      ,    text=__lowerCAmelCase		)

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

   # save the streams
   snake_case 						=   variation.replace(""" """      ,    """-"""		)
   with open(Path(__lowerCAmelCase		) / F'''log.{prefix}.stdout.txt'''      ,    """w"""		) as f:
      f.write(result.stdout		)
   with open(Path(__lowerCAmelCase		) / 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:
      snake_case 						=   json.load(__lowerCAmelCase		)

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





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





def 	__lowerCamelCase			(      )		->  Optional[Any]:
   snake_case 						=   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 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							str		)		->  Dict:
   snake_case 						=   pd.DataFrame(__lowerCAmelCase		)
   snake_case 						=   """variation"""
   snake_case 						=   """diff_%"""

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

   # create diff column if possible
   if not math.isnan(__lowerCAmelCase		):
      snake_case 						=   df.apply(
          lambda __lowerCAmelCase		: round(1_00 * (r[target_metric_key] - sentinel_value) / sentinel_value		)
          if not math.isnan(r[target_metric_key]		)
          else 0      ,    axis="""columns"""      ,    )

   # re-order columns
   snake_case 						=   [variation_key, target_metric_key, diff_key, *report_metric_keys]
   snake_case 						=   df.reindex(__lowerCAmelCase      ,    axis="""columns"""		)  # reorder cols

   # capitalize
   snake_case 						=   df.rename(str.capitalize      ,    axis="""columns"""		)

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

   snake_case 						=   ["""""", """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=__lowerCAmelCase      ,    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=__lowerCAmelCase      ,    floatfmt=""".2f"""		)]

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





def 	__lowerCamelCase			(      )		->  List[Any]:
   snake_case 						=   argparse.ArgumentParser()
   parser.add_argument(
       """--base-cmd"""      ,    default=__lowerCAmelCase      ,    type=__lowerCAmelCase      ,    required=__lowerCAmelCase      ,    help="""Base cmd"""      ,    )
   parser.add_argument(
       """--variations"""      ,    default=__lowerCAmelCase      ,    type=__lowerCAmelCase      ,    nargs="""+"""      ,    required=__lowerCAmelCase      ,    help="""Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'"""      ,    )
   parser.add_argument(
       """--base-variation"""      ,    default=__lowerCAmelCase      ,    type=__lowerCAmelCase      ,    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=__lowerCAmelCase      ,    type=__lowerCAmelCase      ,    required=__lowerCAmelCase      ,    help="""Target metric key in output_dir/all_results.json, e.g., train_samples_per_second"""      ,    )
   parser.add_argument(
       """--report-metric-keys"""      ,    default=""""""      ,    type=__lowerCAmelCase      ,    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=__lowerCAmelCase      ,    help="""How many times to re-run each variation - an average will be reported"""      ,    )
   parser.add_argument(
       """--output_dir"""      ,    default="""output_benchmark"""      ,    type=__lowerCAmelCase      ,    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=__lowerCAmelCase      ,    action="""store_true"""      ,    help="""Whether to show the outputs of each run or just the benchmark progress"""      ,    )
   snake_case 						=   parser.parse_args()

   snake_case 						=   args.output_dir
   Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
   snake_case 						=   get_base_command(__lowerCAmelCase      ,    __lowerCAmelCase		)

   # split each dimension into its --foo variations
   snake_case 						=   [list(map(str.strip      ,    re.split(r"""\|"""      ,    __lowerCAmelCase		)		)		) 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
   snake_case 						=   list(map(str.strip      ,    map(""" """.join      ,    itertools.product(*__lowerCAmelCase		)		)		)		)
   snake_case 						=   max(len(__lowerCAmelCase		) for x in variations		)

   # split wanted keys
   snake_case 						=   args.report_metric_keys.split()

   # capture prints into a log file for convenience
   snake_case 						=   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}'''		)

   snake_case 						=   Tee(__lowerCAmelCase		)

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

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

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


if __name__ == "__main__":
 main()








'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Tuple=False      ,    __lowerCAmelCase       :							Any=False		)		->  Optional[Any]:
   snake_case 						=   """backbone.""" if is_semantic else """"""

   snake_case 						=   []
   for i in range(config.num_hidden_layers		):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')		)
      rename_keys.append(
          (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')		)
      rename_keys.append(
          (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')		)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           (F'''{prefix}cls_token''', """beit.embeddings.cls_token"""),
           (F'''{prefix}patch_embed.proj.weight''', """beit.embeddings.patch_embeddings.projection.weight"""),
           (F'''{prefix}patch_embed.proj.bias''', """beit.embeddings.patch_embeddings.projection.bias"""),
           (F'''{prefix}pos_embed''', """beit.embeddings.position_embeddings"""),
       ]		)

   if has_lm_head:
      # mask token + layernorm
      rename_keys.extend(
          [
              ("""mask_token""", """beit.embeddings.mask_token"""),
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.bias"""),
          ]		)
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
              ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
              ("""head.weight""", """classifier.weight"""),
              ("""head.bias""", """classifier.bias"""),
          ]		)

   return rename_keys





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							str=False      ,    __lowerCAmelCase       :							List[Any]=False		)		->  Optional[int]:
   for i in range(config.num_hidden_layers		):
      snake_case 						=   """backbone.""" if is_semantic else """"""
      # queries, keys and values
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

      snake_case 						=   in_proj_weight[
          : config.hidden_size, :
      ]
      snake_case 						=   q_bias
      snake_case 						=   in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      snake_case 						=   in_proj_weight[
          -config.hidden_size :, :
      ]
      snake_case 						=   v_bias

      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

      snake_case 						=   gamma_a
      snake_case 						=   gamma_a





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Tuple		)		->  Any:
   snake_case 						=   dct.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      )		->  Dict:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)
   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Any=False		)		->  str:
   snake_case 						=   False if """rvlcdip""" in checkpoint_url else True
   snake_case 						=   BeitConfig(use_absolute_position_embeddings=__lowerCAmelCase      ,    use_mask_token=__lowerCAmelCase		)

   # size of the architecture
   if "large" in checkpoint_url or "dit-l" in checkpoint_url:
      snake_case 						=   10_24
      snake_case 						=   40_96
      snake_case 						=   24
      snake_case 						=   16

   # labels
   if "rvlcdip" in checkpoint_url:
      snake_case 						=   16
      snake_case 						=   """huggingface/label-files"""
      snake_case 						=   """rvlcdip-id2label.json"""
      snake_case 						=   json.load(open(hf_hub_download(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)      ,    """r"""		)		)
      snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case 						=   idalabel
      snake_case 						=   {v: k for k, v in idalabel.items()}

   # load state_dict of original model, remove and rename some keys
   snake_case 						=   torch.hub.load_state_dict_from_url(__lowerCAmelCase      ,    map_location="""cpu"""		)["""model"""]

   snake_case 						=   create_rename_keys(__lowerCAmelCase      ,    has_lm_head=__lowerCAmelCase		)
   for src, dest in rename_keys:
      rename_key(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   read_in_q_k_v(__lowerCAmelCase      ,    __lowerCAmelCase      ,    has_lm_head=__lowerCAmelCase		)

   # load HuggingFace model
   snake_case 						=   BeitForMaskedImageModeling(__lowerCAmelCase		) if has_lm_head else BeitForImageClassification(__lowerCAmelCase		)
   model.eval()
   model.load_state_dict(__lowerCAmelCase		)

   # Check outputs on an image
   snake_case 						=   BeitImageProcessor(
       size=config.image_size      ,    resample=PILImageResampling.BILINEAR      ,    do_center_crop=__lowerCAmelCase		)
   snake_case 						=   prepare_img()

   snake_case 						=   image_processor(images=__lowerCAmelCase      ,    return_tensors="""pt"""		)
   snake_case 						=   encoding["""pixel_values"""]

   snake_case 						=   model(__lowerCAmelCase		)
   snake_case 						=   outputs.logits

   # verify logits
   snake_case 						=   [1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
   assert logits.shape == torch.Size(__lowerCAmelCase		), "Shape of logits not as expected"

   Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
   print(F'''Saving model to {pytorch_dump_folder_path}'''		)
   model.save_pretrained(__lowerCAmelCase		)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
   image_processor.save_pretrained(__lowerCAmelCase		)

   if push_to_hub:
      if has_lm_head:
         snake_case 						=   """dit-base""" if """base""" in checkpoint_url else """dit-large"""
      else:
         snake_case 						=   """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
      image_processor.push_to_hub(
          repo_path_or_name=Path(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    organization="""nielsr"""      ,    commit_message="""Add image processor"""      ,    use_temp_dir=__lowerCAmelCase      ,    )
      model.push_to_hub(
          repo_path_or_name=Path(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    organization="""nielsr"""      ,    commit_message="""Add model"""      ,    use_temp_dir=__lowerCAmelCase      ,    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_url",
     default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
     type=str,
     help="URL to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 parser.add_argument(
     "--push_to_hub",
     action="store_true",
 )
 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

