





import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def _A       (   __magic_name__  , __magic_name__=None	):
   lowercase__			       =  None
   if token is not None:
      lowercase__			       =  {"Accept": "application/vnd.github+json", "Authorization": f'''Bearer {token}'''}

   lowercase__			       =  f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
   lowercase__			       =  requests.get(__magic_name__  , headers=__magic_name__	).json()
   lowercase__			       =  {}

   try:
      job_links.update({job["name"]: job["html_url"] for job in result["jobs"]}	)
      lowercase__			       =  math.ceil((result["total_count"] - 100) / 100	)

      for i in range(__magic_name__	):
         lowercase__			       =  requests.get(url + f'''&page={i + 2}'''  , headers=__magic_name__	).json()
         job_links.update({job["name"]: job["html_url"] for job in result["jobs"]}	)

      return job_links
   except Exception:
      print(f'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''	)

   return {}


def _A       (   __magic_name__  , __magic_name__=None	):
   lowercase__			       =  None
   if token is not None:
      lowercase__			       =  {"Accept": "application/vnd.github+json", "Authorization": f'''Bearer {token}'''}

   lowercase__			       =  f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
   lowercase__			       =  requests.get(__magic_name__  , headers=__magic_name__	).json()
   lowercase__			       =  {}

   try:
      artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}	)
      lowercase__			       =  math.ceil((result["total_count"] - 100) / 100	)

      for i in range(__magic_name__	):
         lowercase__			       =  requests.get(url + f'''&page={i + 2}'''  , headers=__magic_name__	).json()
         artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}	)

      return artifacts
   except Exception:
      print(f'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''	)

   return {}


def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  None
   if token is not None:
      lowercase__			       =  {"Accept": "application/vnd.github+json", "Authorization": f'''Bearer {token}'''}

   lowercase__			       =  requests.get(__magic_name__  , headers=__magic_name__  , allow_redirects=__magic_name__	)
   lowercase__			       =  result.headers["Location"]
   lowercase__			       =  requests.get(__magic_name__  , allow_redirects=__magic_name__	)
   lowercase__			       =  os.path.join(__magic_name__  , f'''{artifact_name}.zip'''	)
   with open(__magic_name__  , "wb"	) as fp:
      fp.write(response.content	)


def _A       (   __magic_name__  , __magic_name__=None	):
   lowercase__			       =  []
   lowercase__			       =  []
   lowercase__			       =  None

   with zipfile.ZipFile(__magic_name__	) as z:
      for filename in z.namelist():
         if not os.path.isdir(__magic_name__	):
            # read the file
            if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
               with z.open(__magic_name__	) as f:
                  for line in f:
                     lowercase__			       =  line.decode("UTF-8"	).strip()
                     if filename == "failures_line.txt":
                        try:
                           # `error_line` is the place where `error` occurs
                           lowercase__			       =  line[: line.index(": "	)]
                           lowercase__			       =  line[line.index(": "	) + len(": "	) :]
                           errors.append([error_line, error]	)
                        except Exception:
                           # skip un-related lines
                           pass
                     elif filename == "summary_short.txt" and line.startswith("FAILED "	):
                        # `test` is the test method that failed
                        lowercase__			       =  line[len("FAILED "	) :]
                        failed_tests.append(__magic_name__	)
                     elif filename == "job_name.txt":
                        lowercase__			       =  line

   if len(__magic_name__	) != len(__magic_name__	):
      raise ValueError(
          f'''`errors` and `failed_tests` should have the same number of elements. Got {len(__magic_name__	)} for `errors` '''
          f'''and {len(__magic_name__	)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'''
          " problem."	)

   lowercase__			       =  None
   if job_name and job_links:
      lowercase__			       =  job_links.get(__magic_name__  , __magic_name__	)

   # A list with elements of the form (line of error, error, failed test)
   lowercase__			       =  [x + [y] + [job_link] for x, y in zip(__magic_name__  , __magic_name__	)]

   return result


def _A       (   __magic_name__  , __magic_name__=None	):
   lowercase__			       =  []

   lowercase__			       =  [os.path.join(__magic_name__  , __magic_name__	) for p in os.listdir(__magic_name__	) if p.endswith(".zip"	)]
   for p in paths:
      errors.extend(get_errors_from_single_artifact(__magic_name__  , job_links=__magic_name__	)	)

   return errors


def _A       (   __magic_name__  , __magic_name__=None	):
   lowercase__			       =  Counter()
   counter.update([x[1] for x in logs]	)
   lowercase__			       =  counter.most_common()
   lowercase__			       =  {}
   for error, count in counts:
      if error_filter is None or error not in error_filter:
         lowercase__			       =  {"count": count, "failed_tests": [(x[2], x[0]) for x in logs if x[1] == error]}

   lowercase__			       =  dict(sorted(r.items()  , key=lambda __magic_name__	: item[1]["count"]  , reverse=__magic_name__	)	)
   return r


def _A       (   __magic_name__	):
   lowercase__			       =  test.split("::"	)[0]
   if test.startswith("tests/models/"	):
      lowercase__			       =  test.split("/"	)[2]
   else:
      lowercase__			       =  None

   return test


def _A       (   __magic_name__  , __magic_name__=None	):
   lowercase__			       =  [(x[0], x[1], get_model(x[2]	)) for x in logs]
   lowercase__			       =  [x for x in logs if x[2] is not None]
   lowercase__			       =  {x[2] for x in logs}

   lowercase__			       =  {}
   for test in tests:
      lowercase__			       =  Counter()
      # count by errors in `test`
      counter.update([x[1] for x in logs if x[2] == test]	)
      lowercase__			       =  counter.most_common()
      lowercase__			       =  {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
      lowercase__			       =  sum(error_counts.values()	)
      if n_errors > 0:
         lowercase__			       =  {"count": n_errors, "errors": error_counts}

   lowercase__			       =  dict(sorted(r.items()  , key=lambda __magic_name__	: item[1]["count"]  , reverse=__magic_name__	)	)
   return r


def _A       (   __magic_name__	):
   lowercase__			       =  "| no. | error | status |"
   lowercase__			       =  "|-:|:-|:-|"
   lowercase__			       =  [header, sep]
   for error in reduced_by_error:
      lowercase__			       =  reduced_by_error[error]["count"]
      lowercase__			       =  f'''| {count} | {error[:100]} |  |'''
      lines.append(__magic_name__	)

   return "\n".join(__magic_name__	)


def _A       (   __magic_name__	):
   lowercase__			       =  "| model | no. of errors | major error | count |"
   lowercase__			       =  "|-:|-:|-:|-:|"
   lowercase__			       =  [header, sep]
   for model in reduced_by_model:
      lowercase__			       =  reduced_by_model[model]["count"]
      lowercase__       ,							lowercase__			       =  list(reduced_by_model[model]["errors"].items()	)[0]
      lowercase__			       =  f'''| {model} | {count} | {error[:60]} | {_count} |'''
      lines.append(__magic_name__	)

   return "\n".join(__magic_name__	)


if __name__ == "__main__":
  _snake_case           =			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
  parser.add_argument(
      """--output_dir""",
      type=str,
      required=True,
      help="""Where to store the downloaded artifacts and other result files.""",
  )
  parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
  _snake_case           =			parser.parse_args()

  os.makedirs(args.output_dir, exist_ok=True)

  _snake_case           =			get_job_links(args.workflow_run_id, token=args.token)
  _snake_case           =			{}
  # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
  # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
  if _job_links:
    for k, v in _job_links.items():
      # This is how GitHub actions combine job names.
      if " / " in k:
        _snake_case           =			k.find(""" / """)
        _snake_case           =			k[index + len(""" / """) :]
      _snake_case           =			v
  with open(os.path.join(args.output_dir, """job_links.json"""), """w""", encoding="""UTF-8""") as fp:
    json.dump(job_links, fp, ensure_ascii=False, indent=4)

  _snake_case           =			get_artifacts_links(args.workflow_run_id, token=args.token)
  with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
    json.dump(artifacts, fp, ensure_ascii=False, indent=4)

  for idx, (name, url) in enumerate(artifacts.items()):
    download_artifact(name, url, args.output_dir, args.token)
    # Be gentle to GitHub
    time.sleep(1)

  _snake_case           =			get_all_errors(args.output_dir, job_links=job_links)

  # `e[1]` is the error
  _snake_case           =			Counter()
  counter.update([e[1] for e in errors])

  # print the top 30 most common test errors
  _snake_case           =			counter.most_common(30)
  for item in most_common:
    print(item)

  with open(os.path.join(args.output_dir, """errors.json"""), """w""", encoding="""UTF-8""") as fp:
    json.dump(errors, fp, ensure_ascii=False, indent=4)

  _snake_case           =			reduce_by_error(errors)
  _snake_case           =			reduce_by_model(errors)

  _snake_case           =			make_github_table(reduced_by_error)
  _snake_case           =			make_github_table_per_model(reduced_by_model)

  with open(os.path.join(args.output_dir, """reduced_by_error.txt"""), """w""", encoding="""UTF-8""") as fp:
    fp.write(sa)
  with open(os.path.join(args.output_dir, """reduced_by_model.txt"""), """w""", encoding="""UTF-8""") as fp:
    fp.write(sa)













import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_snake_case           =			logging.getLogger(__name__)





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
  __lowerCamelCase		=      field(
      default=lowercase_							,							metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
  __lowerCamelCase		=      field(
      default=lowercase_							,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
  __lowerCamelCase		=      field(
      default=lowercase_							,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}							,							)
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Whether tp freeze the encoder.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Whether  to freeze the embeddings.'} )





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      field(
      metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
  __lowerCamelCase		=      field(
      default='summarization'							,							metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}							,							)
  __lowerCamelCase		=      field(
      default=1_024							,							metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }							,							)
  __lowerCamelCase		=      field(
      default=128							,							metadata={
          'help': (
              'The maximum total sequence length for target text after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }							,							)
  __lowerCamelCase		=      field(
      default=142							,							metadata={
          'help': (
              'The maximum total sequence length for validation target text after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded. '
              'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
              'during ``evaluate`` and ``predict``.'
          )
      }							,							)
  __lowerCamelCase		=      field(
      default=142							,							metadata={
          'help': (
              'The maximum total sequence length for test target text after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }							,							)
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# training examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# validation examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# test examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Source language id for translation.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Target language id for translation.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': '# num_beams to use for evaluation.'} )
  __lowerCamelCase		=      field(
      default=lowercase_							,							metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}							,							)


def _A       (   __magic_name__  , __magic_name__  , __magic_name__	):
   logger.info(f'''***** {split} metrics *****'''	)
   for key in sorted(metrics.keys()	):
      logger.info(f'''  {key} = {metrics[key]}'''	)
   save_json(__magic_name__  , os.path.join(__magic_name__  , f'''{split}_results.json'''	)	)


def _A       (   ):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   lowercase__			       =  HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)	)

   if len(sys.argv	) == 2 and sys.argv[1].endswith(".json"	):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      lowercase__       ,							lowercase__       ,							lowercase__			       =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
   else:
      lowercase__       ,							lowercase__       ,							lowercase__			       =  parser.parse_args_into_dataclasses()

   check_output_dir(__magic_name__	)

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"  , datefmt="%m/%d/%Y %H:%M:%S"  , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  , )
   logger.warning(
       "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"  , training_args.local_rank  , training_args.device  , training_args.n_gpu  , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED	)  , training_args.fpaa  , )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank	):
      transformers.utils.logging.set_verbosity_info()
   logger.info("Training/evaluation parameters %s"  , __magic_name__	)

   # Set seed
   set_seed(training_args.seed	)

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   lowercase__			       =  AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path  , cache_dir=model_args.cache_dir  , )

   lowercase__			       =  ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
   for p in extra_model_params:
      if getattr(__magic_name__  , __magic_name__  , __magic_name__	):
         assert hasattr(__magic_name__  , __magic_name__	), f'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
         setattr(__magic_name__  , __magic_name__  , getattr(__magic_name__  , __magic_name__	)	)

   lowercase__			       =  AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  , cache_dir=model_args.cache_dir  , )
   lowercase__			       =  AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path  , from_tf=".ckpt" in model_args.model_name_or_path  , config=__magic_name__  , cache_dir=model_args.cache_dir  , )

   # use task specific params
   use_task_specific_params(__magic_name__  , data_args.task	)

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      lowercase__			       =  model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(__magic_name__  , (MBartTokenizer, MBartTokenizerFast)	):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(__magic_name__  , __magic_name__	):
         lowercase__			       =  tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         lowercase__			       =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang	)

   if model_args.freeze_embeds:
      freeze_embeds(__magic_name__	)
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()	)
      assert_all_frozen(model.get_encoder()	)

   lowercase__			       =  SeqaSeqDataset

   # Get datasets
   lowercase__			       =  (
       dataset_class(
           __magic_name__  , type_path="train"  , data_dir=data_args.data_dir  , n_obs=data_args.n_train  , max_target_length=data_args.max_target_length  , max_source_length=data_args.max_source_length  , prefix=model.config.prefix or ""  , )
       if training_args.do_train
       else None
   )
   lowercase__			       =  (
       dataset_class(
           __magic_name__  , type_path="val"  , data_dir=data_args.data_dir  , n_obs=data_args.n_val  , max_target_length=data_args.val_max_target_length  , max_source_length=data_args.max_source_length  , prefix=model.config.prefix or ""  , )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   lowercase__			       =  (
       dataset_class(
           __magic_name__  , type_path="test"  , data_dir=data_args.data_dir  , n_obs=data_args.n_test  , max_target_length=data_args.test_max_target_length  , max_source_length=data_args.max_source_length  , prefix=model.config.prefix or ""  , )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   lowercase__			       =  (
       build_compute_metrics_fn(data_args.task  , __magic_name__	) if training_args.predict_with_generate else None
   )
   lowercase__			       =  SeqaSeqTrainer(
       model=__magic_name__  , args=__magic_name__  , data_args=__magic_name__  , train_dataset=__magic_name__  , eval_dataset=__magic_name__  , data_collator=SeqaSeqDataCollator(
           __magic_name__  , __magic_name__  , model.config.decoder_start_token_id  , training_args.tpu_num_cores	)  , compute_metrics=__magic_name__  , tokenizer=__magic_name__  , )

   lowercase__			       =  {}
   # Training
   if training_args.do_train:
      logger.info("*** Train ***"	)

      lowercase__			       =  trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
      lowercase__			       =  train_result.metrics
      lowercase__			       =  data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("train"  , __magic_name__  , training_args.output_dir	)
         all_metrics.update(__magic_name__	)

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir  , "trainer_state.json"	)	)

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
   if training_args.do_eval:
      logger.info("*** Evaluate ***"	)

      lowercase__			       =  trainer.evaluate(metric_key_prefix="val"	)
      lowercase__			       =  data_args.n_val
      lowercase__			       =  round(metrics["val_loss"]  , 4	)

      if trainer.is_world_process_zero():
         handle_metrics("val"  , __magic_name__  , training_args.output_dir	)
         all_metrics.update(__magic_name__	)

   if training_args.do_predict:
      logger.info("*** Predict ***"	)

      lowercase__			       =  trainer.predict(test_dataset=__magic_name__  , metric_key_prefix="test"	)
      lowercase__			       =  test_output.metrics
      lowercase__			       =  data_args.n_test

      if trainer.is_world_process_zero():
         lowercase__			       =  round(metrics["test_loss"]  , 4	)
         handle_metrics("test"  , __magic_name__  , training_args.output_dir	)
         all_metrics.update(__magic_name__	)

         if training_args.predict_with_generate:
            lowercase__			       =  tokenizer.batch_decode(
                test_output.predictions  , skip_special_tokens=__magic_name__  , clean_up_tokenization_spaces=__magic_name__	)
            lowercase__			       =  lmap(str.strip  , __magic_name__	)
            write_txt_file(__magic_name__  , os.path.join(training_args.output_dir  , "test_generations.txt"	)	)

   if trainer.is_world_process_zero():
      save_json(__magic_name__  , os.path.join(training_args.output_dir  , "all_results.json"	)	)

   return all_metrics


def _A       (   __magic_name__	):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()






