





'''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,
)


A__							:  Dict    =getLogger(__name__)
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase = 8				,      lowerCAmelCase = 10_24				,      lowerCAmelCase="val"				,      lowerCAmelCase=None				,      lowerCAmelCase=False				,      lowerCAmelCase="summarization"				,      lowerCAmelCase=None				,      lowerCAmelCase=1				,      lowerCAmelCase = None				,      lowerCAmelCase=""				,      **lowerCAmelCase				,      ):





      """simple docstring"""


      _lowerCAmelCase					=				str(lowerCAmelCase    )
      assert local_rank is not None
      torch.distributed.init_process_group(backend="""nccl"""				,      rank=lowerCAmelCase    )

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

      _lowerCAmelCase					=				AutoTokenizer.from_pretrained(lowerCAmelCase    )
      logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"    )  # if this is wrong, check config.model_type.

      if max_source_length is None:
            _lowerCAmelCase					=				tokenizer.model_max_length
      if prefix is None:
            _lowerCAmelCase					=				prefix or getattr(model.config				,      """prefix"""				,      """"""    ) or """"""
      _lowerCAmelCase					=				SeqaSeqDataset(
          lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      max_target_length=10_24				,      type_path=lowerCAmelCase				,      n_obs=lowerCAmelCase				,      prefix=lowerCAmelCase				,      **lowerCAmelCase				,      )
      # 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.
      _lowerCAmelCase					=				ds.make_sortish_sampler(lowerCAmelCase				,      distributed=lowerCAmelCase				,      add_extra_examples=lowerCAmelCase				,      shuffle=lowerCAmelCase    )
      _lowerCAmelCase					=				DataLoader(lowerCAmelCase				,      sampler=lowerCAmelCase				,      batch_size=lowerCAmelCase				,      collate_fn=ds.collate_fn    )
      _lowerCAmelCase					=				[]
      for batch in tqdm(lowerCAmelCase    ):
            _lowerCAmelCase					=				model.generate(
                input_ids=batch["""input_ids"""].to(model.device    )				,      attention_mask=batch["""attention_mask"""].to(model.device    )				,      num_return_sequences=lowerCAmelCase				,      num_beams=lowerCAmelCase				,      **lowerCAmelCase				,      )
            _lowerCAmelCase					=				tokenizer.batch_decode(lowerCAmelCase				,      skip_special_tokens=lowerCAmelCase				,      clean_up_tokenization_spaces=lowerCAmelCase    )
            _lowerCAmelCase					=				batch["""ids"""]
            if num_return_sequences > 1:
                  _lowerCAmelCase					=				chunks(lowerCAmelCase				,      lowerCAmelCase    )  # batch size chunks, each of size num_return_seq
            for i, pred in enumerate(lowerCAmelCase    ):
                  results.append({"""pred""": pred, """id""": ids[i].item()}    )
      save_json(lowerCAmelCase				,      lowerCAmelCase    )
      return results, sampler.num_replicas
def   UpperCamelCase__ (						):





      """simple docstring"""


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

      parser.add_argument(
          """--n_obs"""				,      type=lowerCAmelCase				,      default=lowerCAmelCase				,      required=lowerCAmelCase				,      help="""How many observations. Defaults to all."""    )
      parser.add_argument(
          """--num_return_sequences"""				,      type=lowerCAmelCase				,      default=1				,      required=lowerCAmelCase				,      help="""How many sequences to return"""    )
      parser.add_argument(
          """--sync_timeout"""				,      type=lowerCAmelCase				,      default=6_00				,      required=lowerCAmelCase				,      help="""How long should master process wait for other processes to finish."""				,      )
      parser.add_argument("""--src_lang"""				,      type=lowerCAmelCase				,      default=lowerCAmelCase				,      required=lowerCAmelCase    )
      parser.add_argument("""--tgt_lang"""				,      type=lowerCAmelCase				,      default=lowerCAmelCase				,      required=lowerCAmelCase    )
      parser.add_argument(
          """--prefix"""				,      type=lowerCAmelCase				,      required=lowerCAmelCase				,      default=lowerCAmelCase				,      help="""will be added to the begininng of src examples"""    )
      parser.add_argument("""--fp16"""				,      action="""store_true"""    )
      parser.add_argument("""--debug"""				,      action="""store_true"""    )
      _lowerCAmelCase					=				time.time()
      _lowerCAmelCase	,				_lowerCAmelCase					=				parser.parse_known_args()
      _lowerCAmelCase					=				parse_numeric_n_bool_cl_kwargs(lowerCAmelCase    )
      if generate_kwargs and args.local_rank <= 0:
            print(f"parsed the following generate kwargs: {generate_kwargs}"    )
      _lowerCAmelCase					=				Path(args.save_dir + """_tmp"""    )
      Path(lowerCAmelCase    ).mkdir(exist_ok=lowerCAmelCase    )  # this handles locking.
      _lowerCAmelCase					=				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.
      _lowerCAmelCase					=				{}
      if args.src_lang is not None:
            _lowerCAmelCase					=				args.src_lang
      if args.tgt_lang is not None:
            _lowerCAmelCase					=				args.tgt_lang

      Path(args.save_dir    ).mkdir(exist_ok=lowerCAmelCase    )
      _lowerCAmelCase	,				_lowerCAmelCase					=				eval_data_dir(
          args.data_dir				,      lowerCAmelCase				,      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=lowerCAmelCase				,      **lowerCAmelCase				,      )

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

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





      """simple docstring"""


      _lowerCAmelCase					=				[]
      for partial_result in partial_results:
            records.extend(lowerCAmelCase    )
      _lowerCAmelCase					=				sorted(lowerCAmelCase				,      key=lambda lowerCAmelCase    : x["id"]    )
      _lowerCAmelCase					=				[x["""pred"""] for x in records]
      return preds
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				time.time()
      logger.info("""waiting for all nodes to finish"""    )
      _lowerCAmelCase					=				None
      while (time.time() - start_wait) < timeout:
            _lowerCAmelCase					=				list(save_dir.glob("""rank_*.json"""    )    )
            if len(lowerCAmelCase    ) < num_replicas:
                  continue
            try:
                  # make sure all json files are fully saved
                  _lowerCAmelCase					=				lmap(lowerCAmelCase				,      lowerCAmelCase    )
                  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
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     List[str]			=  DebertaTokenizer
      _lowercase:     Union[str, Any]			=  True
      _lowercase:     Union[str, Any]			=  DebertaTokenizerFast




      def 							lowercase__       (     self     :					Any    )							-> List[Any]:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            _lowerCAmelCase					=				[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """[UNK]""",
            ]
            _lowerCAmelCase					=				dict(zip(__snake_case     ,     range(len(__snake_case    )    )    )    )
            _lowerCAmelCase					=				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            _lowerCAmelCase					=				{"""unk_token""": """[UNK]"""}

            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]    )
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""merges_file"""]    )
            with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write(json.dumps(__snake_case    ) + """\n"""    )
            with open(self.merges_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write("""\n""".join(__snake_case    )    )




      def 							lowercase__       (     self     :					List[str]     ,     **__snake_case     :					Any    )							-> Dict:
            kwargs.update(self.special_tokens_map    )
            return self.tokenizer_class.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Dict     ,     __snake_case     :					Dict    )							-> Optional[Any]:
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				"""lower newer"""
            return input_text, output_text




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> List[str]:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            _lowerCAmelCase					=				tokenizer.tokenize(__snake_case    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            _lowerCAmelCase					=				tokens + [tokenizer.unk_token]
            _lowerCAmelCase					=				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case    )     ,     __snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Tuple:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				tokenizer("""Hello"""     ,     """World"""    )
            _lowerCAmelCase					=				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
            self.assertListEqual(tokd["""token_type_ids"""]     ,     __snake_case    )




      @slow
      def 							lowercase__       (     self     :					int    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

            _lowerCAmelCase					=				tokenizer.encode("""sequence builders"""     ,     add_special_tokens=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode("""multi-sequence build"""     ,     add_special_tokens=__snake_case    )

            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     """multi-sequence build"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )

            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case    )
            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case     ,     __snake_case    )

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




      @slow
      def 							lowercase__       (     self     :					Any    )							-> str:
            _lowerCAmelCase					=				[self.tokenizer_class]
            if self.test_rust_tokenizer:
                  tokenizer_classes.append(self.rust_tokenizer_class    )

            for tokenizer_class in tokenizer_classes:
                  _lowerCAmelCase					=				tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding=__snake_case    )
                  _lowerCAmelCase					=				[tokenizer.decode(__snake_case     ,     skip_special_tokens=__snake_case    ) for seq in encoding["""input_ids"""]]

                  # fmt: off
                  _lowerCAmelCase					=				{
                      """input_ids""": [
                          [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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],
                          [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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],
                          [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
                      ],
                      """token_type_ids""": [
                          [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, 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, 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, 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, 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]
                      ]
                  }
                  # fmt: on

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  self.assertDictEqual(encoding.data     ,     __snake_case    )

                  for expected, decoded in zip(__snake_case     ,     __snake_case    ):
                        self.assertEqual(__snake_case     ,     __snake_case    )






