


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


__UpperCamelCase       :       str						       =				float("nan")

class       __magic_name__	:


     def __init__(      self							:     str							,	lowerCamelCase__							:     Optional[int]			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   sys.stdout
      UpperCamelCase__					:    List[str]          =   open(UpperCAmelCase__							,	'''a'''			)


     def __getattr__(      self							:     List[Any]							,	lowerCamelCase__							:     Tuple			)  ->      Tuple:





      '''simple docstring'''






      return getattr(self.stdout							,	UpperCAmelCase__			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     List[Any]			)  ->      Union[str, Any]:





      '''simple docstring'''






      self.stdout.write(UpperCAmelCase__			)
      # strip tqdm codes
      self.file.write(re.sub(r'''^.*\r'''							,	''''''							,	UpperCAmelCase__							,	0							,	re.M			)			)



def 			_a  (				SCREAMING_SNAKE_CASE	:				List[str]=80    , SCREAMING_SNAKE_CASE	:				Dict=False     ):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   []

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

    # python executable (not always needed if the script is executable)
 UpperCamelCase__					:    Tuple          =   sys.executable if full_python_path else sys.executable.split('''/'''     )[-1]
 cmd.append(_UpperCamelCase     )

 # 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__					:    Optional[int]          =   []
 UpperCamelCase__					:    Any          =   ''''''
 while len(_UpperCamelCase     ) > 0:
  current_line += F"{cmd.pop(0     )} "
  if len(_UpperCamelCase     ) == 0 or len(_UpperCamelCase     ) + len(cmd[0]     ) + 1 > max_width - 1:
   lines.append(_UpperCamelCase     )
   UpperCamelCase__					:    Union[str, Any]          =   ''''''
 return "\\\n".join(_UpperCamelCase     )

def 			_a  (				SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



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

 # remove --output_dir if any and set our own
 UpperCamelCase__					:    Optional[int]          =   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 			_a  (				SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				int     ):







 """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__					:    Union[str, Any]          =   subprocess.run(_UpperCamelCase    , capture_output=_UpperCamelCase    , text=_UpperCamelCase     )

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

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

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

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				Dict    , ):







 """simple docstring"""



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

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   torch.cuda.get_device_properties(torch.device('''cuda'''     )     )
 return F"\nDatetime    : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'     )}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch       : {torch.__version__}\ncuda        : {torch.version.cuda}\npython      : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n"

def 			_a  (				SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				Dict     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   pd.DataFrame(_UpperCamelCase     )
 UpperCamelCase__					:    int          =   '''variation'''
 UpperCamelCase__					:    str          =   '''diff_%'''

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

 # create diff column if possible
 if not math.isnan(_UpperCamelCase     ):
  UpperCamelCase__					:    Union[str, Any]          =   df.apply(
      lambda SCREAMING_SNAKE_CASE     : 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__					:    Union[str, Any]          =   [variation_key, target_metric_key, diff_key, *report_metric_keys]
 UpperCamelCase__					:    Union[str, Any]          =   df.reindex(_UpperCamelCase    , axis='''columns'''     )  # reorder cols

 # capitalize
 UpperCamelCase__					:    Tuple          =   df.rename(str.capitalize    , axis='''columns'''     )

 # make the cols as narrow as possible
 UpperCamelCase__					:    str          =   df.rename(lambda SCREAMING_SNAKE_CASE     : c.replace('''_'''    , '''<br>'''     )    , axis='''columns'''     )
 UpperCamelCase__					:    str          =   df.rename(lambda SCREAMING_SNAKE_CASE     : c.replace('''_'''    , '''\n'''     )    , axis='''columns'''     )

 UpperCamelCase__					:    Dict          =   ['''''', '''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=_UpperCamelCase    , 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=_UpperCamelCase    , floatfmt='''.2f'''     )]

 print('''\n\n'''.join(_UpperCamelCase     )     )

def 			_a  (				):







 """simple docstring"""



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

 UpperCamelCase__					:    Any          =   args.output_dir
 Path(_UpperCamelCase     ).mkdir(exist_ok=_UpperCamelCase     )
 UpperCamelCase__					:    str          =   get_base_command(_UpperCamelCase    , _UpperCamelCase     )

 # split each dimension into its --foo variations
 UpperCamelCase__					:    Optional[Any]          =   [list(map(str.strip    , re.split(r'''\|'''    , _UpperCamelCase     )     )     ) 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[int]          =   list(map(str.strip    , map(''' '''.join    , itertools.product(*_UpperCamelCase     )     )     )     )
 UpperCamelCase__					:    Optional[int]          =   max(len(_UpperCamelCase     ) for x in variations     )

 # split wanted keys
 UpperCamelCase__					:    str          =   args.report_metric_keys.split()

 # capture prints into a log file for convenience
 UpperCamelCase__					:    List[str]          =   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__					:    Dict          =   Tee(_UpperCamelCase     )

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

 UpperCamelCase__					:    Dict          =   '''variation'''
 UpperCamelCase__					:    Dict          =   []
 for id, variation in enumerate(tqdm(_UpperCamelCase    , desc='''Total completion: '''    , leave=_UpperCamelCase     )     ):
  UpperCamelCase__					:    Tuple          =   base_cmd + variation.split()
  results.append(
      process_run(
          id + 1    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , args.target_metric_key    , _UpperCamelCase    , args.repeat_times    , _UpperCamelCase    , args.verbose    , )     )

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


if __name__ == "__main__":
       main()










from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 			_a  (				SCREAMING_SNAKE_CASE	:				Namespace     ):







 """simple docstring"""



 return ConvertCommand(
     args.model_type    , args.tf_checkpoint    , args.pytorch_dump_output    , args.config    , args.finetuning_task_name     )


__UpperCamelCase       :       List[Any]						       =				"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"






class       __magic_name__	(     __lowerCAmelCase):


     @staticmethod
     def      UpperCAmelCase__	(      lowerCamelCase__							:     ArgumentParser			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   parser.add_parser(
          '''convert'''							,	help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''							,	)
      train_parser.add_argument('''--model_type'''							,	type=lowerCamelCase__							,	required=lowerCamelCase__							,	help='''Model\'s type.'''			)
      train_parser.add_argument(
          '''--tf_checkpoint'''							,	type=lowerCamelCase__							,	required=lowerCamelCase__							,	help='''TensorFlow checkpoint path or folder.'''			)
      train_parser.add_argument(
          '''--pytorch_dump_output'''							,	type=lowerCamelCase__							,	required=lowerCamelCase__							,	help='''Path to the PyTorch saved model output.'''			)
      train_parser.add_argument('''--config'''							,	type=lowerCamelCase__							,	default=''''''							,	help='''Configuration file path or folder.'''			)
      train_parser.add_argument(
          '''--finetuning_task_name'''							,	type=lowerCamelCase__							,	default=lowerCamelCase__							,	help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							,	)
      train_parser.set_defaults(func=lowerCamelCase__			)


     def __init__(      self							:     Optional[Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	*lowerCamelCase__							:     Optional[int]							,	)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   logging.get_logger('''transformers-cli/converting'''			)

      self._logger.info(F"Loading model {model_type}"			)
      UpperCamelCase__					:    List[str]          =   model_type
      UpperCamelCase__					:    Optional[int]          =   tf_checkpoint
      UpperCamelCase__					:    List[Any]          =   pytorch_dump_output
      UpperCamelCase__					:    List[Any]          =   config
      UpperCamelCase__					:    Any          =   finetuning_task_name


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[Any]:





      '''simple docstring'''






      if self._model_type == "albert":
       try:
        from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
            convert_tf_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "bert":
       try:
        from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
            convert_tf_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "funnel":
       try:
        from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
            convert_tf_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "t5":
       try:
        from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "gpt":
       from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
           convert_openai_checkpoint_to_pytorch,
       )

       convert_openai_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "transfo_xl":
       try:
        from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
            convert_transfo_xl_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       if "ckpt" in self._tf_checkpoint.lower():
        UpperCamelCase__					:    str          =   self._tf_checkpoint
        UpperCamelCase__					:    List[Any]          =   ''''''
       else:
        UpperCamelCase__					:    Any          =   self._tf_checkpoint
        UpperCamelCase__					:    List[Any]          =   ''''''
       convert_transfo_xl_checkpoint_to_pytorch(
           lowerCamelCase__							,	self._config							,	self._pytorch_dump_output							,	lowerCamelCase__			)
      elif self._model_type == "gpt2":
       try:
        from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
            convert_gpta_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      elif self._model_type == "xlnet":
       try:
        from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
            convert_xlnet_checkpoint_to_pytorch,
        )
       except ImportError:
        raise ImportError(lowerCamelCase__			)

       convert_xlnet_checkpoint_to_pytorch(
           self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output							,	self._finetuning_task_name			)
      elif self._model_type == "xlm":
       from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
           convert_xlm_checkpoint_to_pytorch,
       )

       convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint							,	self._pytorch_dump_output			)
      elif self._model_type == "lxmert":
       from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
           convert_lxmert_checkpoint_to_pytorch,
       )

       convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint							,	self._pytorch_dump_output			)
      elif self._model_type == "rembert":
       from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
           convert_rembert_tf_checkpoint_to_pytorch,
       )

       convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,	self._config							,	self._pytorch_dump_output			)
      else:
       raise ValueError(
           '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''			)


