'''simple docstring'''

import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


A_       						=							datasets.logging.get_logger(__name__)


A_       						=							"\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n    author = \"Moosavi, Nafise Sadat  and\n      Strube, Michael\",\n    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = aug,\n    year = \"2016\",\n    address = \"Berlin, Germany\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P16-1060\",\n    doi = \"10.18653/v1/P16-1060\",\n    pages = \"632--642\",\n}\n\n"

A_       						=							"\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n"

A_       						=							"\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting 'keep_singletons=False', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    'mentions': mentions\n    'muc': MUC metric [Vilain et al, 1995]\n    'bcub': B-cubed [Bagga and Baldwin, 1998]\n    'ceafe': CEAFe [Luo et al., 2005]\n    'lea': LEA [Moosavi and Strube, 2016]\n    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric('coval')\n    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',\n    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',\n    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',\n    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',\n    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',\n    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}\n"





def    A_  ( snake_case      ,       snake_case      ,       snake_case=False      ,       snake_case=False      ,       snake_case=True      ,       snake_case=False      ,       snake_case="dummy_doc" ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					{doc: key_lines}
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					{doc: sys_lines}

  SCREAMING_SNAKE_CASE:Dict						      =					{}

  SCREAMING_SNAKE_CASE:Optional[int]						      =					0
  SCREAMING_SNAKE_CASE:Dict						      =					0
  SCREAMING_SNAKE_CASE:int						      =					0
  SCREAMING_SNAKE_CASE:int						      =					0
  SCREAMING_SNAKE_CASE:Dict						      =					0
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					0

  SCREAMING_SNAKE_CASE:List[str]						      =					reader.get_doc_mentions(snake_case      ,       key_doc_lines[doc]      ,       snake_case )
  key_singletons_num += singletons_num

  if NP_only or min_span:
    SCREAMING_SNAKE_CASE:Dict						      =					reader.set_annotated_parse_trees(snake_case      ,       key_doc_lines[doc]      ,       snake_case      ,       snake_case )

  SCREAMING_SNAKE_CASE:Any						      =					reader.get_doc_mentions(snake_case      ,       sys_doc_lines[doc]      ,       snake_case )
  sys_singletons_num += singletons_num

  if NP_only or min_span:
    SCREAMING_SNAKE_CASE:str						      =					reader.set_annotated_parse_trees(snake_case      ,       key_doc_lines[doc]      ,       snake_case      ,       snake_case )

  if remove_nested:
    SCREAMING_SNAKE_CASE:Any						      =					reader.remove_nested_coref_mentions(snake_case      ,       snake_case )
    key_nested_coref_num += nested_mentions
    key_removed_nested_clusters += removed_clusters

    SCREAMING_SNAKE_CASE:List[str]						      =					reader.remove_nested_coref_mentions(snake_case      ,       snake_case )
    sys_nested_coref_num += nested_mentions
    sys_removed_nested_clusters += removed_clusters

  SCREAMING_SNAKE_CASE:Optional[Any]						      =					reader.get_mention_assignments(snake_case      ,       snake_case )
  SCREAMING_SNAKE_CASE:Dict						      =					reader.get_mention_assignments(snake_case      ,       snake_case )

  SCREAMING_SNAKE_CASE:List[Any]						      =					(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

  if remove_nested:
    logger.info(
        "Number of removed nested coreferring mentions in the key "
        F'''annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}''' )
    logger.info(
        "Number of resulting singleton clusters in the key "
        F'''annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}''' )

  if not keep_singletons:
    logger.info(
        F'''{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '''
        "files, respectively" )

  return doc_coref_infos





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Tuple						      =					get_coref_infos(snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case )

  SCREAMING_SNAKE_CASE:Optional[int]						      =					{}
  SCREAMING_SNAKE_CASE:int						      =					0
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					0

  for name, metric in metrics:
    SCREAMING_SNAKE_CASE:Any						      =					evaluator.evaluate_documents(snake_case      ,       snake_case      ,       beta=1 )
    if name in ["muc", "bcub", "ceafe"]:
      conll += fa
      conll_subparts_num += 1
    output_scores.update({F'''{name}/recall''': recall, F'''{name}/precision''': precision, F'''{name}/f1''': fa} )

    logger.info(
        name.ljust(10 )      ,       F'''Recall: {recall * 100:.2f}'''      ,       F''' Precision: {precision * 100:.2f}'''      ,       F''' F1: {fa * 100:.2f}'''      ,       )

  if conll_subparts_num == 3:
    SCREAMING_SNAKE_CASE:Any						      =					(conll / 3) * 100
    logger.info(F'''CoNLL score: {conll:.2f}''' )
    output_scores.update({"conll_score": conll} )

  return output_scores





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					False
  for line in key_lines:
    if not line.startswith("#" ):
      if len(line.split() ) > 6:
        SCREAMING_SNAKE_CASE:Tuple						      =					line.split()[5]
        if not parse_col == "-":
          SCREAMING_SNAKE_CASE:List[str]						      =					True
          break
        else:
          break
  return has_gold_parse





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION						)
class 							_snake_case (       datasets.Metric						):
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     return datasets.MetricInfo(
         description=_DESCRIPTION     ,citation=_CITATION     ,inputs_description=_KWARGS_DESCRIPTION     ,features=datasets.Features(
             {
                 "predictions": datasets.Sequence(datasets.Value("string"							)							),
                 "references": datasets.Sequence(datasets.Value("string"							)							),
             }							)     ,codebase_urls=["https://github.com/ns-moosavi/coval"]     ,reference_urls=[
             "https://github.com/ns-moosavi/coval",
             "https://www.aclweb.org/anthology/P16-1060",
             "http://www.conll.cemantix.org/2012/data.html",
         ]     ,)







   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=True     ,SCREAMING_SNAKE_CASE__			:      int=False     ,SCREAMING_SNAKE_CASE__			:      List[str]=False     ,SCREAMING_SNAKE_CASE__			:      str=False							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					[
         ("mentions", evaluator.mentions),
         ("muc", evaluator.muc),
         ("bcub", evaluator.b_cubed),
         ("ceafe", evaluator.ceafe),
         ("lea", evaluator.lea),
     ]

     if min_span:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					util.check_gold_parse_annotation(SCREAMING_SNAKE_CASE__							)
       if not has_gold_parse:
         raise NotImplementedError("References should have gold parse annotation to use 'min_span'."							)
         # util.parse_key_file(key_file)
         # key_file = key_file + ".parsed"

     SCREAMING_SNAKE_CASE:List[Any]						      =					evaluate(
         key_lines=SCREAMING_SNAKE_CASE__     ,sys_lines=SCREAMING_SNAKE_CASE__     ,metrics=SCREAMING_SNAKE_CASE__     ,NP_only=SCREAMING_SNAKE_CASE__     ,remove_nested=SCREAMING_SNAKE_CASE__     ,keep_singletons=SCREAMING_SNAKE_CASE__     ,min_span=SCREAMING_SNAKE_CASE__     ,)

     return score









'''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


A_       						=							float("nan")





class 							_snake_case :
   def __init__( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					sys.stdout
     SCREAMING_SNAKE_CASE:List[Any]						      =					open(SCREAMING_SNAKE_CASE__     ,"a"							)
   def __getattr__( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     return getattr(self.stdout     ,SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      str							):
     self.stdout.write(SCREAMING_SNAKE_CASE__							)
     # strip tqdm codes
     self.file.write(re.sub(R"^.*\r"     ,""     ,SCREAMING_SNAKE_CASE__     ,0     ,re.M							)							)





def    A_  ( snake_case=80      ,       snake_case=False ):
  SCREAMING_SNAKE_CASE:Tuple						      =					[]

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

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

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

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





def    A_  ( snake_case      ,       snake_case ):
  # unwrap multi-line input
  SCREAMING_SNAKE_CASE:List[Any]						      =					re.sub(r"[\\\n]+"      ,       " "      ,       args.base_cmd )

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

  # ensure we have --overwrite_output_dir
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
  # 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      ,       100 ) for k in metric_keys}      ,       **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.2222_2222] )}      ,       )

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					subprocess.run(snake_case      ,       capture_output=snake_case      ,       text=snake_case )

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

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

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





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





def    A_  ( ):
  SCREAMING_SNAKE_CASE:int						      =					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    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					pd.DataFrame(snake_case )
  SCREAMING_SNAKE_CASE:str						      =					"variation"
  SCREAMING_SNAKE_CASE:str						      =					"diff_%"

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

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

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

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

  SCREAMING_SNAKE_CASE:List[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=snake_case      ,       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=snake_case      ,       floatfmt=".2f" )]

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





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

  SCREAMING_SNAKE_CASE:int						      =					args.output_dir
  Path(snake_case ).mkdir(exist_ok=snake_case )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					get_base_command(snake_case      ,       snake_case )

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

  # split wanted keys
  SCREAMING_SNAKE_CASE:Any						      =					args.report_metric_keys.split()

  # capture prints into a log file for convenience
  SCREAMING_SNAKE_CASE: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}''' )

  SCREAMING_SNAKE_CASE:Any						      =					Tee(snake_case )

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

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

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


if __name__ == "__main__":
    main()






