

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


__A :  Optional[Any]			  =      float('''nan''')






class  __A					:






    def __init__(						self      :     Tuple     ,					UpperCAmelCase_      :     Any     ):
        lowerCAmelCase					:      int           =   sys.stdout
        lowerCAmelCase					:      Optional[Any]           =   open(__a     ,					'a'     )






    def __getattr__(						self      :     str     ,					UpperCAmelCase_      :     List[str]     ):
        return getattr(self.stdout     ,					__a     )






    def 						lowercase__			(						self      :     Dict     ,					UpperCAmelCase_      :     Optional[int]     ):
        self.stdout.write(__a     )
        # strip tqdm codes
        self.file.write(re.sub(R'^.*\r'     ,					''     ,					__a     ,					0     ,					re.M     )     )



def 		SCREAMING_SNAKE_CASE__					(				_UpperCAmelCase=80,   _UpperCAmelCase=False					)       ->			Any:



    '''simple docstring'''






    lowerCAmelCase					:      Union[str, Any]           =   []

    # deal with critical env vars
    lowerCAmelCase					:      List[str]           =   ["CUDA_VISIBLE_DEVICES"]
    for key in env_keys:
        lowerCAmelCase					:      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)
    lowerCAmelCase					:      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
    lowerCAmelCase					:      Tuple           =   []
    lowerCAmelCase					:      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_					)
            lowerCAmelCase					:      List[Any]           =   ""
    return "\\\n".join(lowercase_					)
def 		SCREAMING_SNAKE_CASE__					(				_UpperCAmelCase,   _UpperCAmelCase					)       ->			Any:



    '''simple docstring'''






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

    # remove --output_dir if any and set our own
    lowerCAmelCase					:      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
    lowerCAmelCase					:      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 		SCREAMING_SNAKE_CASE__					(				_UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase					)       ->			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, 1_0.3_1, 1_0_0.2, 5_5.6_6_6_6, 2_2_2.2_2_2_2_2_2_2_2]					)},   )

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

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

    # save the streams
    lowerCAmelCase					:      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:
        lowerCAmelCase					:      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 		SCREAMING_SNAKE_CASE__					(				_UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   )       ->			str:



    '''simple docstring'''






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



    '''simple docstring'''






    lowerCAmelCase					:      Tuple           =   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 		SCREAMING_SNAKE_CASE__					(				_UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase					)       ->			Optional[int]:



    '''simple docstring'''






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

    lowerCAmelCase					:      str           =   nan
    if base_variation is not None and len(df[df[variation_key] == base_variation]					):
        # this may still return nan
        lowerCAmelCase					:      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
        lowerCAmelCase					:      Optional[Any]           =   df.loc[df[target_metric_key] != nan][target_metric_key].min()

    # create diff column if possible
    if not math.isnan(lowercase_					):
        lowerCAmelCase					:      List[Any]           =   df.apply(
            lambda _UpperCAmelCase					: 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
    lowerCAmelCase					:      Any           =   [variation_key, target_metric_key, diff_key, *report_metric_keys]
    lowerCAmelCase					:      Tuple           =   df.reindex(lowercase_,   axis='columns'					)  # reorder cols

    # capitalize
    lowerCAmelCase					:      int           =   df.rename(str.capitalize,   axis='columns'					)

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

    lowerCAmelCase					:      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 		SCREAMING_SNAKE_CASE__					(				)       ->			str:



    '''simple docstring'''






    lowerCAmelCase					:      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',   )
    lowerCAmelCase					:      Tuple           =   parser.parse_args()

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

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

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

    # capture prints into a log file for convenience
    lowerCAmelCase					:      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}"					)

    lowerCAmelCase					:      List[Any]           =   Tee(lowercase_					)

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

    lowerCAmelCase					:      List[Any]           =   "variation"
    lowerCAmelCase					:      List[Any]           =   []
    for id, variation in enumerate(tqdm(lowercase_,   desc='Total completion: ',   leave=lowercase_					)					):
        lowerCAmelCase					:      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 numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_ = 1e-12		,lowercase_ = 100		,)       ->		tuple[float, np.ndarray]:




      """simple docstring"""

      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[1]
      # Ensure proper dimensionality.
      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[0]
      # Ensure inputs are either both complex or both real
      assert np.iscomplexobj(lowercase_      ) == np.iscomplexobj(lowercase_      )
      _UpperCamelCase   :						Dict    =							np.iscomplexobj(lowercase_      )
      if is_complex:
            # Ensure complex input_matrix is Hermitian
            assert np.array_equal(lowercase_		,input_matrix.conj().T      )

      # Set convergence to False. Will define convergence when we exceed max_iterations
      # or when we have small changes from one iteration to next.

      _UpperCamelCase   :						List[Any]    =							False
      _UpperCamelCase   :						Optional[int]    =							0
      _UpperCamelCase   :						int    =							0
      _UpperCamelCase   :						Optional[int]    =							1e12

      while not convergence:
            # Multiple matrix by the vector.
            _UpperCamelCase   :						Optional[int]    =							np.dot(lowercase_		,lowercase_      )
            # Normalize the resulting output vector.
            _UpperCamelCase   :						List[Any]    =							w / np.linalg.norm(lowercase_      )
            # Find rayleigh quotient
            # (faster than usual b/c we know vector is normalized already)
            _UpperCamelCase   :						Optional[Any]    =							vector.conj().T if is_complex else vector.T
            _UpperCamelCase   :						Dict    =							np.dot(lowercase_		,np.dot(lowercase_		,lowercase_      )      )

            # Check convergence.
            _UpperCamelCase   :						Optional[int]    =							np.abs(lambda_ - lambda_previous      ) / lambda_
            iterations += 1

            if error <= error_tol or iterations >= max_iterations:
                  _UpperCamelCase   :						List[Any]    =							True

            _UpperCamelCase   :						Tuple    =							lambda_

      if is_complex:
            _UpperCamelCase   :						Tuple    =							np.real(lambda_      )

      return lambda_, vector




def        lowercase__     (       )       ->		None:




      """simple docstring"""

      _UpperCamelCase   :						Dict    =							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
      _UpperCamelCase   :						Optional[int]    =							np.array([41, 4, 20]      )
      _UpperCamelCase   :						Union[str, Any]    =							real_input_matrix.astype(np.complexaaa      )
      _UpperCamelCase   :						int    =							np.triu(1J * complex_input_matrix		,1      )
      complex_input_matrix += imag_matrix
      complex_input_matrix += -1 * imag_matrix.T
      _UpperCamelCase   :						Union[str, Any]    =							np.array([41, 4, 20]      ).astype(np.complexaaa      )

      for problem_type in ["real", "complex"]:
            if problem_type == "real":
                  _UpperCamelCase   :						Dict    =							real_input_matrix
                  _UpperCamelCase   :						Any    =							real_vector
            elif problem_type == "complex":
                  _UpperCamelCase   :						int    =							complex_input_matrix
                  _UpperCamelCase   :						Union[str, Any]    =							complex_vector

            # Our implementation.
            _UpperCamelCase,	_UpperCamelCase   :						int    =							power_iteration(lowercase_		,lowercase_      )

            # Numpy implementation.

            # Get eigenvalues and eigenvectors using built-in numpy
            # eigh (eigh used for symmetric or hermetian matrices).
            _UpperCamelCase,	_UpperCamelCase   :						str    =							np.linalg.eigh(lowercase_      )
            # Last eigenvalue is the maximum one.
            _UpperCamelCase   :						Any    =							eigen_values[-1]
            # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
            _UpperCamelCase   :						Tuple    =							eigen_vectors[:, -1]

            # Check our implementation and numpy gives close answers.
            assert np.abs(eigen_value - eigen_value_max      ) <= 1e-6
            # Take absolute values element wise of each eigenvector.
            # as they are only unique to a minus sign.
            assert np.linalg.norm(np.abs(lowercase_      ) - np.abs(lowercase_      )      ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()
