





"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCamelCase_       =		getLogger(__name__)



def 					__lowerCamelCase       (							a_					:		str			,       a_					:		str			,       a_					:		str			,       a_					:		int = 8			,       a_					:		int = 10_24			,       a_					:		Optional[int]="val"			,       a_					:		Optional[Any]=None			,       a_					:		Tuple=False			,       a_					:		Optional[Any]="summarization"			,       a_					:		Tuple=None			,       a_					:		Tuple=1			,       a_					:		Dict = None			,       a_					:		Union[str, Any]=""			,       **a_					:		Optional[int]			,       )      ->				Dict:
      __SCREAMING_SNAKE_CASE       :int		    =  str(a_							)
      assert local_rank is not None
      torch.distributed.init_process_group(backend='''nccl'''			,       rank=a_							)

      __SCREAMING_SNAKE_CASE       :int		    =  Path(a_							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  save_dir.joinpath(f'''rank_{local_rank}_output.json'''							)
      torch.cuda.set_device(a_							)
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  AutoModelForSeqaSeqLM.from_pretrained(a_							).cuda()
      if fpaa:
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  model.half()
      # determine if we need to increase num_beams
      use_task_specific_params(a_			,       a_							)  # update config with task specific params
      __SCREAMING_SNAKE_CASE       :int		    =  generate_kwargs.pop('''num_beams'''			,       model.config.num_beams							)  # AttributeError risk?
      if num_return_sequences > num_beams:
            __SCREAMING_SNAKE_CASE       :int		    =  num_return_sequences

      __SCREAMING_SNAKE_CASE       :int		    =  AutoTokenizer.from_pretrained(a_							)
      logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''							)  # if this is wrong, check config.model_type.

      if max_source_length is None:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  tokenizer.model_max_length
      if prefix is None:
            __SCREAMING_SNAKE_CASE       :int		    =  prefix or getattr(model.config			,       '''prefix'''			,       ''''''							) or ''''''
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  SeqaSeqDataset(
          a_			,       a_			,       a_			,       max_target_length=10_24			,       type_path=a_			,       n_obs=a_			,       prefix=a_			,       **a_			,       )
      # I set shuffle=True for a more accurate progress bar.
      # If all the longest samples are first, the prog bar estimate is too high at the beginning.
      __SCREAMING_SNAKE_CASE       :str		    =  ds.make_sortish_sampler(a_			,       distributed=a_			,       add_extra_examples=a_			,       shuffle=a_							)
      __SCREAMING_SNAKE_CASE       :Any		    =  DataLoader(a_			,       sampler=a_			,       batch_size=a_			,       collate_fn=ds.collate_fn							)
      __SCREAMING_SNAKE_CASE       :int		    =  []
      for batch in tqdm(a_							):
            __SCREAMING_SNAKE_CASE       :Tuple		    =  model.generate(
                input_ids=batch['''input_ids'''].to(model.device							)			,       attention_mask=batch['''attention_mask'''].to(model.device							)			,       num_return_sequences=a_			,       num_beams=a_			,       **a_			,       )
            __SCREAMING_SNAKE_CASE       :Any		    =  tokenizer.batch_decode(a_			,       skip_special_tokens=a_			,       clean_up_tokenization_spaces=a_							)
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  batch['''ids''']
            if num_return_sequences > 1:
                  __SCREAMING_SNAKE_CASE       :Any		    =  chunks(a_			,       a_							)  # batch size chunks, each of size num_return_seq
            for i, pred in enumerate(a_							):
                  results.append({'''pred''': pred, '''id''': ids[i].item()}							)
      save_json(a_			,       a_							)
      return results, sampler.num_replicas



def 					__lowerCamelCase       (							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  argparse.ArgumentParser(
          epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''							)
      parser.add_argument('''--data_dir'''			,       type=a_			,       help='''like cnn_dm/test.source'''							)
      parser.add_argument(
          '''--model_name'''			,       type=a_			,       help='''like facebook/bart-large-cnn,t5-base, etc.'''			,       default='''sshleifer/distilbart-xsum-12-3'''			,       )
      parser.add_argument('''--save_dir'''			,       type=a_			,       help='''where to save'''			,       default='''tmp_gen'''							)
      parser.add_argument('''--max_source_length'''			,       type=a_			,       default=a_							)
      parser.add_argument(
          '''--type_path'''			,       type=a_			,       default='''test'''			,       help='''which subset to evaluate typically train/val/test'''							)
      parser.add_argument('''--task'''			,       type=a_			,       default='''summarization'''			,       help='''used for task_specific_params + metrics'''							)
      parser.add_argument('''--bs'''			,       type=a_			,       default=8			,       required=a_			,       help='''batch size'''							)
      parser.add_argument(
          '''--local_rank'''			,       type=a_			,       default=-1			,       required=a_			,       help='''should be passed by distributed.launch'''							)

      parser.add_argument(
          '''--n_obs'''			,       type=a_			,       default=a_			,       required=a_			,       help='''How many observations. Defaults to all.'''							)
      parser.add_argument(
          '''--num_return_sequences'''			,       type=a_			,       default=1			,       required=a_			,       help='''How many sequences to return'''							)
      parser.add_argument(
          '''--sync_timeout'''			,       type=a_			,       default=6_00			,       required=a_			,       help='''How long should master process wait for other processes to finish.'''			,       )
      parser.add_argument('''--src_lang'''			,       type=a_			,       default=a_			,       required=a_							)
      parser.add_argument('''--tgt_lang'''			,       type=a_			,       default=a_			,       required=a_							)
      parser.add_argument(
          '''--prefix'''			,       type=a_			,       required=a_			,       default=a_			,       help='''will be added to the begininng of src examples'''							)
      parser.add_argument('''--fp16'''			,       action='''store_true'''							)
      parser.add_argument('''--debug'''			,       action='''store_true'''							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  time.time()
      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Optional[int]		    =  parser.parse_known_args()
      __SCREAMING_SNAKE_CASE       :List[str]		    =  parse_numeric_n_bool_cl_kwargs(a_							)
      if generate_kwargs and args.local_rank <= 0:
            print(f'''parsed the following generate kwargs: {generate_kwargs}'''							)
      __SCREAMING_SNAKE_CASE       :int		    =  Path(args.save_dir + '''_tmp'''							)
      Path(a_							).mkdir(exist_ok=a_							)  # this handles locking.
      __SCREAMING_SNAKE_CASE       :str		    =  list(json_save_dir.glob('''rank_*.json'''							)							)
      if intermediate_files:
            raise ValueError(f'''Found files at {json_save_dir} please move or remove them.'''							)
            # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  {}
      if args.src_lang is not None:
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  args.src_lang
      if args.tgt_lang is not None:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  args.tgt_lang

      Path(args.save_dir							).mkdir(exist_ok=a_							)
      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Any		    =  eval_data_dir(
          args.data_dir			,       a_			,       args.model_name			,       type_path=args.type_path			,       bs=args.bs			,       fpaa=args.fpaa			,       task=args.task			,       local_rank=args.local_rank			,       n_obs=args.n_obs			,       max_source_length=args.max_source_length			,       num_return_sequences=args.num_return_sequences			,       prefix=args.prefix			,       dataset_kwargs=a_			,       **a_			,       )

      if args.local_rank <= 0:
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  Path(args.save_dir							)
            save_dir.mkdir(exist_ok=a_							)
            __SCREAMING_SNAKE_CASE       :Tuple		    =  gather_results_from_each_node(a_			,       a_			,       args.sync_timeout							)
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  combine_partial_results(a_							)
            if args.num_return_sequences > 1:
                  __SCREAMING_SNAKE_CASE       :Any		    =  save_dir.joinpath('''pseudolabel_results.json'''							)
                  print(f'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''							)
                  save_json(a_			,       a_							)
                  return
            __SCREAMING_SNAKE_CASE       :str		    =  Path(args.data_dir							).joinpath(args.type_path + '''.target'''							)
            with open(a_							) as f:
                  __SCREAMING_SNAKE_CASE       :Any		    =  [x.rstrip() for x in f.readlines()][: len(a_							)]

            # Calculate metrics, save metrics,  and save _generations.txt
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  '''translation''' in args.task
            __SCREAMING_SNAKE_CASE       :List[str]		    =  calculate_bleu if calc_bleu else calculate_rouge
            __SCREAMING_SNAKE_CASE       :Any		    =  '''bleu''' if calc_bleu else '''rouge'''
            __SCREAMING_SNAKE_CASE       :Dict		    =  score_fn(a_			,       a_							)
            __SCREAMING_SNAKE_CASE       :str		    =  len(a_							)
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  time.time() - start_time
            __SCREAMING_SNAKE_CASE       :List[str]		    =  round(runtime / metrics['''n_obs''']			,       4							)
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_replicas
            # TODO(@stas00): add whatever metadata to metrics
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  save_dir.joinpath(f'''{args.type_path}_{metric_name}.json'''							)
            save_json(a_			,       a_			,       indent=a_							)
            print(a_							)
            write_txt_file(a_			,       save_dir.joinpath(f'''{args.type_path}_generations.txt'''							)							)
            if args.debug:
                  write_txt_file(a_			,       save_dir.joinpath(f'''{args.type_path}.target'''							)							)
            else:
                  shutil.rmtree(a_							)



def 					__lowerCamelCase       (							a_					:		List[str]							)      ->				List:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  []
      for partial_result in partial_results:
            records.extend(a_							)
      __SCREAMING_SNAKE_CASE       :str		    =  sorted(a_			,       key=lambda a_							: x["id"]							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  [x['''pred'''] for x in records]
      return preds



def 					__lowerCamelCase       (							a_					:		Optional[Any]			,       a_					:		Any			,       a_					:		List[Any]							)      ->				List[Dict[str, List]]:
      # WAIT FOR lots of .json files
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  time.time()
      logger.info('''waiting for all nodes to finish'''							)
      __SCREAMING_SNAKE_CASE       :int		    =  None
      while (time.time() - start_wait) < timeout:
            __SCREAMING_SNAKE_CASE       :List[str]		    =  list(save_dir.glob('''rank_*.json'''							)							)
            if len(a_							) < num_replicas:
                  continue
            try:
                  # make sure all json files are fully saved
                  __SCREAMING_SNAKE_CASE       :str		    =  lmap(a_			,       a_							)
                  return json_data
            except JSONDecodeError:
                  continue
      else:
            raise TimeoutError('''Rank 0 gave up on waiting for other processes'''							)
      # Unreachable


if __name__ == "__main__":
  # Usage for MT:
  run_generate()






"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


lowerCamelCase_       =		TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


lowerCamelCase_       =		[]
lowerCamelCase_       =		[]

lowerCamelCase_       =		{"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

lowerCamelCase_       =		[
    {
        "type": "header",
        "text": {
            "type": "plain_text",
            "text": f'🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results',
            "emoji": True,
        },
    }
]

lowerCamelCase_       =		0
for log in Path().glob("*.log"):
  lowerCamelCase_       =		0
  with open(log, "r") as f:
    for line in f:
      lowerCamelCase_       =		json.loads(line)
      if line.get("nodeid", "") != "":
        lowerCamelCase_       =		line["nodeid"]
        if line.get("duration", None) is not None:
          lowerCamelCase_       =		f'{line["duration"]:.4f}'
          if line.get("outcome", "") == "failed":
            section_num_failed += 1
            failed.append([test, duration, log.name.split("_")[0]])
            total_num_failed += 1
  group_info.append([str(log), section_num_failed, failed])
  lowerCamelCase_       =		[]
  log.unlink()

lowerCamelCase_       =		""
lowerCamelCase_       =		[]
if total_num_failed > 0:
  for name, num_failed, failed_tests in group_info:
    if num_failed > 0:
      if num_failed == 1:
        message += f"*{name[1:]}: {num_failed} failed test*\n"
      else:
        message += f"*{name[1:]}: {num_failed} failed tests*\n"
      lowerCamelCase_       =		[]
      lowerCamelCase_       =		{}
      for test in failed_tests:
        lowerCamelCase_       =		test[0].split("::")
        lowerCamelCase_       =		data[0].split("/")[-1]
        if data[0] not in filesafailed:
          lowerCamelCase_       =		[data[1:]]
        else:
          filesafailed[data[0]] += [data[1:]]
        failed_table.append(data)

      lowerCamelCase_       =		[test[0] for test in failed_table]
      lowerCamelCase_       =		list(set(files))
      # Count number of instances in failed_tests
      lowerCamelCase_       =		[]
      for file in individual_files:
        table.append([file, len(filesafailed[file])])

      lowerCamelCase_       =		tabulate(
          table,
          headers=["Test Location", "Num Failed"],
          tablefmt=hf_table_format,
          stralign="right",
      )
      message += f"\n```\n{failed_table}\n```"
      all_filesafailed.append(filesafailed)
  if len(message) > 3_0_0_0:
    lowerCamelCase_       =		"Too many failed tests, please see the full report in the Action results."
    lowerCamelCase_       =		len(err) + 1_0
    lowerCamelCase_       =		message[: 3_0_0_0 - offset] + f'\n...\n```\n{err}'
  print(f'### {message}')
else:
  lowerCamelCase_       =		"No failed tests! 🤗"
  print(f'## {message}')
  payload.append(no_error_payload)

if os.environ.get("TEST_TYPE", "") != "":
  from slack_sdk import WebClient

  lowerCamelCase_       =		WebClient(token=os.environ["SLACK_API_TOKEN"])
  if message != "No failed tests! 🤗":
    lowerCamelCase_       =		{
        "type": "section",
        "text": {
            "type": "mrkdwn",
            "text": message,
        },
    }
    payload.append(md_report)
    lowerCamelCase_       =		{
        "type": "section",
        "text": {
            "type": "mrkdwn",
            "text": "*For more details:*",
        },
        "accessory": {
            "type": "button",
            "text": {
                "type": "plain_text",
                "text": "Check Action results",
                "emoji": True,
            },
            "url": f'https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
        },
    }
    payload.append(action_button)
    lowerCamelCase_       =		{
        "type": "context",
        "elements": [
            {
                "type": "plain_text",
                "text": f'Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}',
            }
        ],
    }
    payload.append(date_report)
  lowerCamelCase_       =		client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
  lowerCamelCase_       =		response.data["ts"]
  for failed_file in all_filesafailed:
    for test_location, test_failures in failed_file.items():
      # Keep only the first instance of the test name
      lowerCamelCase_       =		""
      for i, row in enumerate(test_failures):
        if row[0] != test_class:
          lowerCamelCase_       =		row[0]
        else:
          lowerCamelCase_       =		""

      lowerCamelCase_       =		{
          "type": "section",
          "text": {
              "type": "mrkdwn",
              "text": f'Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```',
          },
      }

      client.chat_postMessage(
          channel="#accelerate-ci-daily",
          thread_ts=ts,
          blocks=[payload],
      )