


'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''


 def __init__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Tuple       =			data







 def __iter__(      self						):

  """simple docstring"""







  for element in self.data:
   yield element


def 			_lowercase       (				UpperCamelCase__       :					int=True			):
 __A  :		Optional[int]       =			Accelerator(even_batches=UpperCamelCase__			)
 assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
 return accelerator


def 			_lowercase       (				UpperCamelCase__       :					Accelerator,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					bool = False			):
 if iterable:
  __A  :		int       =			DummyIterableDataset(torch.as_tensor(range(UpperCamelCase__			)			)			)
 else:
  __A  :		Optional[int]       =			TensorDataset(torch.as_tensor(range(UpperCamelCase__			)			)			)

 __A  :		Optional[Any]       =			DataLoader(UpperCamelCase__,				batch_size=UpperCamelCase__			)
 __A  :		List[Any]       =			accelerator.prepare(UpperCamelCase__			)

 return dl


def 			_lowercase       (				UpperCamelCase__       :					Accelerator,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					List[int],				UpperCamelCase__       :					List[int],				):
 __A  :		Tuple       =			create_dataloader(accelerator=UpperCamelCase__,				dataset_size=UpperCamelCase__,				batch_size=UpperCamelCase__			)

 __A  :		List[Any]       =			[len(batch[0]			) for batch in dl]

 if accelerator.process_index == 0:
  assert batch_sizes == process_0_expected_batch_sizes
 elif accelerator.process_index == 1:
  assert batch_sizes == process_1_expected_batch_sizes


def 			_lowercase       (				):
 __A  :		List[str]       =			create_accelerator()

 # without padding, we would expect a different number of batches
 verify_dataloader_batch_sizes(
     UpperCamelCase__,				dataset_size=3,				batch_size=1,				process_0_expected_batch_sizes=[1, 1],				process_1_expected_batch_sizes=[1, 1],				)

 # without padding, we would expect the same number of batches, but different sizes
 verify_dataloader_batch_sizes(
     UpperCamelCase__,				dataset_size=7,				batch_size=2,				process_0_expected_batch_sizes=[2, 2],				process_1_expected_batch_sizes=[2, 2],				)


def 			_lowercase       (				):
 __A  :		List[str]       =			create_accelerator(even_batches=UpperCamelCase__			)

 verify_dataloader_batch_sizes(
     UpperCamelCase__,				dataset_size=3,				batch_size=1,				process_0_expected_batch_sizes=[1, 1],				process_1_expected_batch_sizes=[1],				)

 verify_dataloader_batch_sizes(
     UpperCamelCase__,				dataset_size=7,				batch_size=2,				process_0_expected_batch_sizes=[2, 2],				process_1_expected_batch_sizes=[2, 1],				)


def 			_lowercase       (				):
 __A  :		Any       =			create_accelerator(even_batches=UpperCamelCase__			)

 __A  :		List[str]       =			torch.nn.Linear(1,				1			)
 __A  :		Any       =			accelerator.prepare(UpperCamelCase__			)

 __A  :		List[str]       =			create_dataloader(UpperCamelCase__,				dataset_size=3,				batch_size=1			)

 __A  :		List[Any]       =			[]
 with accelerator.join_uneven_inputs([ddp_model]			):
  for batch_idx, batch in enumerate(UpperCamelCase__			):
   __A  :		int       =			ddp_model(batch[0].float()			)
   __A  :		Any       =			output.sum()
   loss.backward()
   batch_idxs.append(UpperCamelCase__			)

 accelerator.wait_for_everyone()

 if accelerator.process_index == 0:
  assert batch_idxs == [0, 1]
 elif accelerator.process_index == 1:
  assert batch_idxs == [0]


def 			_lowercase       (				UpperCamelCase__       :					List[Any]			):
 with warnings.catch_warnings(record=UpperCamelCase__			) as w:
  with accelerator.join_uneven_inputs([Mock()]			):
   pass

  assert issubclass(w[-1].category,				UpperCamelCase__			)
  assert "only supported for multi-GPU" in str(w[-1].message			)


def 			_lowercase       (				):
 __A  :		str       =			True
 __A  :		Union[str, Any]       =			False
 __A  :		Any       =			create_accelerator(even_batches=UpperCamelCase__			)
 __A  :		Union[str, Any]       =			torch.nn.Linear(1,				1			)
 __A  :		Optional[int]       =			accelerator.prepare(UpperCamelCase__			)
 __A  :		List[Any]       =			create_dataloader(UpperCamelCase__,				dataset_size=3,				batch_size=1			)
 __A  :		Tuple       =			create_dataloader(UpperCamelCase__,				dataset_size=3,				batch_size=1			)

 with accelerator.join_uneven_inputs([ddp_model],				even_batches=UpperCamelCase__			):
  __A  :		str       =			train_dl.batch_sampler.even_batches
  __A  :		Union[str, Any]       =			valid_dl.batch_sampler.even_batches

 assert train_dl_overridden_value == overridden_even_batches
 assert valid_dl_overridden_value == overridden_even_batches
 assert train_dl.batch_sampler.even_batches == default_even_batches
 assert valid_dl.batch_sampler.even_batches == default_even_batches


def 			_lowercase       (				):
 __A  :		Any       =			True
 __A  :		Optional[Any]       =			False
 __A  :		Tuple       =			create_accelerator(even_batches=UpperCamelCase__			)
 __A  :		Tuple       =			torch.nn.Linear(1,				1			)
 __A  :		List[Any]       =			accelerator.prepare(UpperCamelCase__			)
 create_dataloader(UpperCamelCase__,				dataset_size=3,				batch_size=1,				iterable=UpperCamelCase__			)
 __A  :		Any       =			create_dataloader(UpperCamelCase__,				dataset_size=3,				batch_size=1			)

 with warnings.catch_warnings():
  warnings.filterwarnings('ignore'			)
  try:
   with accelerator.join_uneven_inputs([ddp_model],				even_batches=UpperCamelCase__			):
    __A  :		Tuple       =			batch_dl.batch_sampler.even_batches
  except AttributeError:
   # ensure attribute error is not raised when processing iterable dl
   raise AssertionError

 assert batch_dl_overridden_value == overridden_even_batches
 assert batch_dl.batch_sampler.even_batches == default_even_batches


def 			_lowercase       (				):
 __A  :		List[Any]       =			create_accelerator()
 __A  :		Union[str, Any]       =			torch.nn.Linear(1,				1			)
 __A  :		Dict       =			accelerator.prepare(UpperCamelCase__			)
 create_dataloader(UpperCamelCase__,				dataset_size=3,				batch_size=1,				iterable=UpperCamelCase__			)

 with warnings.catch_warnings(record=UpperCamelCase__			) as w:
  with accelerator.join_uneven_inputs([ddp_model],				even_batches=UpperCamelCase__			):
   pass

  assert issubclass(w[-1].category,				UpperCamelCase__			)
  assert "only supported for map-style datasets" in str(w[-1].message			)


def 			_lowercase       (				):
 __A  :		str       =			create_accelerator()

 accelerator.print('Test that even_batches variable ensures uniform batches across processes'			)
 test_default_ensures_even_batch_sizes()

 accelerator.print('Run tests with even_batches disabled'			)
 test_can_disable_even_batches()

 accelerator.print('Test joining uneven inputs'			)
 test_can_join_uneven_inputs()

 accelerator.print('Test overriding even_batches when joining uneven inputs'			)
 test_join_can_override_even_batches()

 accelerator.print('Test overriding even_batches for mixed dataloader types'			)
 test_join_can_override_for_mixed_type_dataloaders()

 accelerator.print('Test overriding even_batches raises a warning for iterable dataloaders'			)
 test_join_raises_warning_for_iterable_when_overriding_even_batches()

 accelerator.print('Test join with non DDP distributed raises warning'			)
 __A  :		Dict       =			accelerator.state.distributed_type
 __A  :		Any       =			DistributedType.FSDP
 test_join_raises_warning_for_non_ddp_distributed(UpperCamelCase__			)
 __A  :		Dict       =			original_state


if __name__ == "__main__":
 main()










'''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,
)


UpperCAmelCase_ :     Dict								=      getLogger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					str,				UpperCamelCase__       :					int = 8,				UpperCamelCase__       :					int = 1024,				UpperCamelCase__       :					List[Any]="val",				UpperCamelCase__       :					int=None,				UpperCamelCase__       :					str=False,				UpperCamelCase__       :					int="summarization",				UpperCamelCase__       :					List[Any]=None,				UpperCamelCase__       :					List[Any]=1,				UpperCamelCase__       :					Dict = None,				UpperCamelCase__       :					Optional[int]="",				**UpperCamelCase__       :					str,				):
 __A  :		Dict       =			str(UpperCamelCase__			)
 assert local_rank is not None
 torch.distributed.init_process_group(backend='nccl',				rank=UpperCamelCase__			)

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

 __A  :		Optional[Any]       =			AutoTokenizer.from_pretrained(UpperCamelCase__			)
 logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""			)  # if this is wrong, check config.model_type.

 if max_source_length is None:
  __A  :		Union[str, Any]       =			tokenizer.model_max_length
 if prefix is None:
  __A  :		List[Any]       =			prefix or getattr(model.config,				'prefix',				''			) or ''
 __A  :		Tuple       =			SeqaSeqDataset(
     UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				max_target_length=1024,				type_path=UpperCamelCase__,				n_obs=UpperCamelCase__,				prefix=UpperCamelCase__,				**UpperCamelCase__,				)
 # 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.
 __A  :		Any       =			ds.make_sortish_sampler(UpperCamelCase__,				distributed=UpperCamelCase__,				add_extra_examples=UpperCamelCase__,				shuffle=UpperCamelCase__			)
 __A  :		Union[str, Any]       =			DataLoader(UpperCamelCase__,				sampler=UpperCamelCase__,				batch_size=UpperCamelCase__,				collate_fn=ds.collate_fn			)
 __A  :		Tuple       =			[]
 for batch in tqdm(UpperCamelCase__			):
  __A  :		Any       =			model.generate(
      input_ids=batch['input_ids'].to(model.device			),				attention_mask=batch['attention_mask'].to(model.device			),				num_return_sequences=UpperCamelCase__,				num_beams=UpperCamelCase__,				**UpperCamelCase__,				)
  __A  :		Dict       =			tokenizer.batch_decode(UpperCamelCase__,				skip_special_tokens=UpperCamelCase__,				clean_up_tokenization_spaces=UpperCamelCase__			)
  __A  :		List[str]       =			batch['ids']
  if num_return_sequences > 1:
   __A  :		str       =			chunks(UpperCamelCase__,				UpperCamelCase__			)  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(UpperCamelCase__			):
   results.append({'pred': pred, 'id': ids[i].item()}			)
 save_json(UpperCamelCase__,				UpperCamelCase__			)
 return results, sampler.num_replicas


def 			_lowercase       (				):
 __A  :		Optional[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=UpperCamelCase__,				help='like cnn_dm/test.source'			)
 parser.add_argument(
     '--model_name',				type=UpperCamelCase__,				help='like facebook/bart-large-cnn,t5-base, etc.',				default='sshleifer/distilbart-xsum-12-3',				)
 parser.add_argument('--save_dir',				type=UpperCamelCase__,				help='where to save',				default='tmp_gen'			)
 parser.add_argument('--max_source_length',				type=UpperCamelCase__,				default=UpperCamelCase__			)
 parser.add_argument(
     '--type_path',				type=UpperCamelCase__,				default='test',				help='which subset to evaluate typically train/val/test'			)
 parser.add_argument('--task',				type=UpperCamelCase__,				default='summarization',				help='used for task_specific_params + metrics'			)
 parser.add_argument('--bs',				type=UpperCamelCase__,				default=8,				required=UpperCamelCase__,				help='batch size'			)
 parser.add_argument(
     '--local_rank',				type=UpperCamelCase__,				default=-1,				required=UpperCamelCase__,				help='should be passed by distributed.launch'			)

 parser.add_argument(
     '--n_obs',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__,				help='How many observations. Defaults to all.'			)
 parser.add_argument(
     '--num_return_sequences',				type=UpperCamelCase__,				default=1,				required=UpperCamelCase__,				help='How many sequences to return'			)
 parser.add_argument(
     '--sync_timeout',				type=UpperCamelCase__,				default=600,				required=UpperCamelCase__,				help='How long should master process wait for other processes to finish.',				)
 parser.add_argument('--src_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument('--tgt_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument(
     '--prefix',				type=UpperCamelCase__,				required=UpperCamelCase__,				default=UpperCamelCase__,				help='will be added to the begininng of src examples'			)
 parser.add_argument('--fp16',				action='store_true'			)
 parser.add_argument('--debug',				action='store_true'			)
 __A  :		int       =			time.time()
 __A     ,__A  :		int       =			parser.parse_known_args()
 __A  :		List[str]       =			parse_numeric_n_bool_cl_kwargs(UpperCamelCase__			)
 if generate_kwargs and args.local_rank <= 0:
  print(f"""parsed the following generate kwargs: {generate_kwargs}"""			)
 __A  :		List[str]       =			Path(args.save_dir + '_tmp'			)
 Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)  # this handles locking.
 __A  :		Optional[Any]       =			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.
 __A  :		List[str]       =			{}
 if args.src_lang is not None:
  __A  :		Dict       =			args.src_lang
 if args.tgt_lang is not None:
  __A  :		Optional[Any]       =			args.tgt_lang

 Path(args.save_dir			).mkdir(exist_ok=UpperCamelCase__			)
 __A     ,__A  :		List[Any]       =			eval_data_dir(
     args.data_dir,				UpperCamelCase__,				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=UpperCamelCase__,				**UpperCamelCase__,				)

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

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


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 __A  :		List[str]       =			[]
 for partial_result in partial_results:
  records.extend(UpperCamelCase__			)
 __A  :		List[Any]       =			sorted(UpperCamelCase__,				key=lambda UpperCamelCase__			: x["id"]			)
 __A  :		Dict       =			[x['pred'] for x in records]
 return preds


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					int			):
 # WAIT FOR lots of .json files
 __A  :		Dict       =			time.time()
 logger.info('waiting for all nodes to finish'			)
 __A  :		int       =			None
 while (time.time() - start_wait) < timeout:
  __A  :		List[Any]       =			list(save_dir.glob('rank_*.json'			)			)
  if len(UpperCamelCase__			) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __A  :		List[Any]       =			lmap(UpperCamelCase__,				UpperCamelCase__			)
   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()






