



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,
)


a__   :					str 						=      getLogger(__name__)

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__ = 8    ,	A__ = 1_024    ,	A__="val"    ,	A__=None    ,	A__=False    ,	A__="summarization"    ,	A__=None    ,	A__=1    ,	A__ = None    ,	A__=""    ,	**A__    ,	):
      lowercase__  =	str(A__						)
      assert local_rank is not None
      torch.distributed.init_process_group(backend='nccl'    ,	rank=A__						)

      lowercase__  =	Path(A__						)
      lowercase__  =	save_dir.joinpath(F'''rank_{local_rank}_output.json'''						)
      torch.cuda.set_device(A__						)
      lowercase__  =	AutoModelForSeqaSeqLM.from_pretrained(A__						).cuda()
      if fpaa:
            lowercase__  =	model.half()
      # determine if we need to increase num_beams
      use_task_specific_params(A__    ,	A__						)  # update config with task specific params
      lowercase__  =	generate_kwargs.pop('num_beams'    ,	model.config.num_beams						)  # AttributeError risk?
      if num_return_sequences > num_beams:
            lowercase__  =	num_return_sequences

      lowercase__  =	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:
            lowercase__  =	tokenizer.model_max_length
      if prefix is None:
            lowercase__  =	prefix or getattr(model.config    ,	'prefix'    ,	''						) or ''
      lowercase__  =	SeqaSeqDataset(
          A__    ,	A__    ,	A__    ,	max_target_length=1_024    ,	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.
      lowercase__  =	ds.make_sortish_sampler(A__    ,	distributed=A__    ,	add_extra_examples=A__    ,	shuffle=A__						)
      lowercase__  =	DataLoader(A__    ,	sampler=A__    ,	batch_size=A__    ,	collate_fn=ds.collate_fn						)
      lowercase__  =	[]
      for batch in tqdm(A__						):
            lowercase__  =	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__    ,	)
            lowercase__  =	tokenizer.batch_decode(A__    ,	skip_special_tokens=A__    ,	clean_up_tokenization_spaces=A__						)
            lowercase__  =	batch['ids']
            if num_return_sequences > 1:
                  lowercase__  =	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 (    ):
      lowercase__  =	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=600    ,	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'						)
      lowercase__  =	time.time()
      lowercase__,						lowercase__  =	parser.parse_known_args()
      lowercase__  =	parse_numeric_n_bool_cl_kwargs(A__						)
      if generate_kwargs and args.local_rank <= 0:
            print(F'''parsed the following generate kwargs: {generate_kwargs}'''						)
      lowercase__  =	Path(args.save_dir + '_tmp'						)
      Path(A__						).mkdir(exist_ok=A__						)  # this handles locking.
      lowercase__  =	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.
      lowercase__  =	{}
      if args.src_lang is not None:
            lowercase__  =	args.src_lang
      if args.tgt_lang is not None:
            lowercase__  =	args.tgt_lang

      Path(args.save_dir						).mkdir(exist_ok=A__						)
      lowercase__,						lowercase__  =	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:
            lowercase__  =	Path(args.save_dir						)
            save_dir.mkdir(exist_ok=A__						)
            lowercase__  =	gather_results_from_each_node(A__    ,	A__    ,	args.sync_timeout						)
            lowercase__  =	combine_partial_results(A__						)
            if args.num_return_sequences > 1:
                  lowercase__  =	save_dir.joinpath('pseudolabel_results.json'						)
                  print(F'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''						)
                  save_json(A__    ,	A__						)
                  return
            lowercase__  =	Path(args.data_dir						).joinpath(args.type_path + '.target'						)
            with open(A__						) as f:
                  lowercase__  =	[x.rstrip() for x in f.readlines()][: len(A__						)]

            # Calculate metrics, save metrics,  and save _generations.txt
            lowercase__  =	'translation' in args.task
            lowercase__  =	calculate_bleu if calc_bleu else calculate_rouge
            lowercase__  =	'bleu' if calc_bleu else 'rouge'
            lowercase__  =	score_fn(A__    ,	A__						)
            lowercase__  =	len(A__						)
            lowercase__  =	time.time() - start_time
            lowercase__  =	round(runtime / metrics['n_obs']    ,	4						)
            lowercase__  =	num_replicas
            # TODO(@stas00): add whatever metadata to metrics
            lowercase__  =	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__						):
      lowercase__  =	[]
      for partial_result in partial_results:
            records.extend(A__						)
      lowercase__  =	sorted(A__    ,	key=lambda A__						: x["id"]						)
      lowercase__  =	[x['pred'] for x in records]
      return preds

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__						):
      # WAIT FOR lots of .json files
      lowercase__  =	time.time()
      logger.info('waiting for all nodes to finish'						)
      lowercase__  =	None
      while (time.time() - start_wait) < timeout:
            lowercase__  =	list(save_dir.glob('rank_*.json'						)						)
            if len(A__						) < num_replicas:
                  continue
            try:
                  # make sure all json files are fully saved
                  lowercase__  =	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()





import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a__   :					List[Any] 						=      get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
class    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Tuple												=     XLMProphetNetTokenizer
   A     :	List[str]												=     False
   A     :	Union[str, Any]												=     True


   def       UpperCAmelCase		(    self	:      Any)						->					int:




         """simple docstring"""

         super().setUp()

         # We have a SentencePiece fixture for testing
         lowercase__  =	XLMProphetNetTokenizer(lowerCAmelCase      ,					keep_accents=lowerCAmelCase)
         tokenizer.save_pretrained(self.tmpdirname)


   def       UpperCAmelCase		(    self	:      Optional[int])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	'[PAD]'
         lowercase__  =	0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase)      ,					lowerCAmelCase)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase)      ,					lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Dict)						->					List[Any]:




         """simple docstring"""

         lowercase__  =	list(self.get_tokenizer().get_vocab().keys())

         self.assertEqual(vocab_keys[0]      ,					'[PAD]')
         self.assertEqual(vocab_keys[1]      ,					'[CLS]')
         self.assertEqual(vocab_keys[-1]      ,					'j')
         self.assertEqual(len(lowerCAmelCase)      ,					10_12)


   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					str:




         """simple docstring"""

         self.assertEqual(self.get_tokenizer().vocab_size      ,					10_12)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	XLMProphetNetTokenizer(lowerCAmelCase      ,					keep_accents=lowerCAmelCase)

         lowercase__  =	tokenizer.tokenize('This is a test')
         self.assertListEqual(lowerCAmelCase      ,					['▁This', '▁is', '▁a', '▁t', 'est'])

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,					)

         lowercase__  =	tokenizer.tokenize('I was born in 92000, and this is falsé.')
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '9',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 'é',
                 '.',
             ]      ,					)
         lowercase__  =	tokenizer.convert_tokens_to_ids(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
             ]      ,					)

         lowercase__  =	tokenizer.convert_ids_to_tokens(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '[UNK]',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 '[UNK]',
                 '.',
             ]      ,					)


   @cached_property
   def       UpperCAmelCase		(    self	:      str)						->					List[Any]:




         """simple docstring"""

         return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')


   @slow
   def       UpperCAmelCase		(    self	:      List[Any])						->					List[str]:




         """simple docstring"""

         lowercase__  =	'Hello World!'
         lowercase__  =	[3_53_89, 66_72, 49, 2]
         self.assertListEqual(lowerCAmelCase      ,					self.big_tokenizer.encode(lowerCAmelCase))



   @slow
   def       UpperCAmelCase		(    self	:      Tuple)						->					str:




         """simple docstring"""

         lowercase__  =	{'input_ids': [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=lowerCAmelCase      ,					model_name='microsoft/xprophetnet-large-wiki100-cased'      ,					revision='1acad1643ddd54a44df6a1b797ada8373685d90e'      ,					)
