

import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

A__        =			logging.getLogger()
A__        =			torch.cuda.is_available()
A__        =			{
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1E-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						'''\n'''.join(snake_case_	)
    Path(snake_case_	).open("w"	).writelines(snake_case_	)


A__        =			[" Sam ate lunch today.", "Sams lunch ingredients."]
A__        =			["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
A__        =			"patrickvonplaten/t5-tiny-random"
A__        =			"sshleifer/t5-tinier-random"
A__        =			"sshleifer/bart-tiny-random"
A__        =			"sshleifer/tiny-mbart"
A__        =			"sshleifer/tiny-marian-en-de"
A__        =			"stas/tiny-wmt19-en-de"


A__        =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





def 							__a       (					__A	):
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(snake_case_ ,       f"{split}.source"	) ,       snake_case_	)
        _dump_articles(os.path.join(snake_case_ ,       f"{split}.target"	) ,       snake_case_	)
    return tmp_dir







class    UpperCamelCase_			(  UpperCamelCase_			):


  """simple docstring"""



  @classmethod
  def      _UpperCamelCase    (	cls    :				Optional[Any]	)					-> int:
      logging.disable(logging.CRITICAL	)  # remove noisy download output from tracebacks
      return cls



  @slow
  @require_torch_gpu
  def      _UpperCamelCase    (	self    :				str	)					-> List[Any]:
      SCREAMING_SNAKE_CASE__					     =						list_models()
      SCREAMING_SNAKE_CASE__					     =						'''sshleifer'''
      SCREAMING_SNAKE_CASE__					     =						[x.modelId for x in model_list if x.modelId.startswith(__UpperCamelCase	)]
      SCREAMING_SNAKE_CASE__					     =						['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
      SCREAMING_SNAKE_CASE__					     =						[]
      for m in model_ids:
          if m in allowed_to_be_broken:
              continue
          try:
              AutoConfig.from_pretrained(__UpperCamelCase	)
          except Exception:
              failures.append(__UpperCamelCase	)
      assert not failures, f"The following models could not be loaded through AutoConfig: {failures}"



  def      _UpperCamelCase    (	self    :				Union[str, Any]	)					-> Dict:
      SCREAMING_SNAKE_CASE__					     =						{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
      self._test_distiller_cli(__UpperCamelCase	)



  def      _UpperCamelCase    (	self    :				Dict	)					-> Any:
      SCREAMING_SNAKE_CASE__					     =						{
          '''student_encoder_layers''': 2,
          '''student_decoder_layers''': 1,
          '''max_epochs''': 4,
          '''val_check_interval''': 0.25,
          '''alpha_hid''': 2.0,
          '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
      }
      SCREAMING_SNAKE_CASE__					     =						self._test_distiller_cli(__UpperCamelCase     ,  check_contents=__UpperCamelCase	)

      SCREAMING_SNAKE_CASE__					     =						list(Path(model.output_dir	).glob("*.ckpt"	)	)
      self.assertEqual(1     ,  len(__UpperCamelCase	)	)
      SCREAMING_SNAKE_CASE__					     =						list(Path(model.output_dir	).glob("**/*.bin"	)	)
      self.assertEqual(len(__UpperCamelCase	)     ,  2	)
      SCREAMING_SNAKE_CASE__					     =						lmap(str.strip     ,  Path(model.hparams.data_dir	).joinpath("test.source"	).open().readlines()	)
      SCREAMING_SNAKE_CASE__					     =						tempfile.mktemp()  # XXX: not being cleaned up
      generate_summaries_or_translations(__UpperCamelCase     ,  __UpperCamelCase     ,  str(model.output_dir / "best_tfmr"	)	)
      self.assertTrue(Path(__UpperCamelCase	).exists()	)

      SCREAMING_SNAKE_CASE__					     =						self.get_auto_remove_tmp_dir()
      convert_pl_to_hf(ckpts[0]     ,  transformer_ckpts[0].parent     ,  __UpperCamelCase	)
      assert os.path.exists(os.path.join(__UpperCamelCase     ,  "pytorch_model.bin"	)	)



  def      _UpperCamelCase    (	self    :				Optional[Any]	)					-> List[str]:
      SCREAMING_SNAKE_CASE__					     =						AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__					     =						model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
      SCREAMING_SNAKE_CASE__					     =						torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]     ,  dtype=torch.long     ,  device=model.device	)
      SCREAMING_SNAKE_CASE__					     =						target_ids[:, :-1].contiguous()  # Why this line?
      SCREAMING_SNAKE_CASE__					     =						target_ids[:, 1:].clone()  # why clone?
      SCREAMING_SNAKE_CASE__					     =						model(
          __UpperCamelCase     ,  attention_mask=__UpperCamelCase     ,  decoder_input_ids=__UpperCamelCase     ,  labels=__UpperCamelCase     ,  use_cache=__UpperCamelCase	).loss

      SCREAMING_SNAKE_CASE__					     =						model(__UpperCamelCase     ,  attention_mask=__UpperCamelCase     ,  decoder_input_ids=__UpperCamelCase     ,  use_cache=__UpperCamelCase	).logits

      SCREAMING_SNAKE_CASE__					     =						nn.functional.log_softmax(__UpperCamelCase     ,  dim=-1	)
      SCREAMING_SNAKE_CASE__					     =						label_smoothed_nll_loss(
          __UpperCamelCase     ,  __UpperCamelCase     ,  0.1     ,  ignore_index=model.config.pad_token_id	)
      with self.assertRaises(__UpperCamelCase	):
          # TODO: understand why this breaks
          self.assertEqual(__UpperCamelCase     ,  __UpperCamelCase	)



  def      _UpperCamelCase    (	self    :				List[Any]	)					-> Tuple:
      SCREAMING_SNAKE_CASE__					     =						{
          '''student_encoder_layers''': 2,
          '''student_decoder_layers''': 1,
          '''num_train_epochs''': 4,
          '''val_check_interval''': 0.25,
          '''alpha_hid''': 2.0,
          '''task''': '''translation''',
          '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
          '''tokenizer_name''': MBART_TINY,
          '''teacher''': MBART_TINY,
          '''src_lang''': '''en_XX''',
          '''tgt_lang''': '''ro_RO''',
      }
      SCREAMING_SNAKE_CASE__					     =						self._test_distiller_cli(__UpperCamelCase     ,  check_contents=__UpperCamelCase	)
      assert model.model.config.model_type == "mbart"

      SCREAMING_SNAKE_CASE__					     =						list(Path(model.output_dir	).glob("*.ckpt"	)	)
      self.assertEqual(1     ,  len(__UpperCamelCase	)	)
      SCREAMING_SNAKE_CASE__					     =						list(Path(model.output_dir	).glob("**/*.bin"	)	)
      SCREAMING_SNAKE_CASE__					     =						list(Path(model.output_dir	).glob("best_tfmr/*"	)	)
      assert len(__UpperCamelCase	) > 2
      self.assertEqual(len(__UpperCamelCase	)     ,  2	)



  def      _UpperCamelCase    (	self    :				int	)					-> Tuple:
      SCREAMING_SNAKE_CASE__					     =						{
          '''student_encoder_layers''': 1,
          '''student_decoder_layers''': 1,
          '''alpha_hid''': 2.0,
          '''teacher''': T5_TINY,
          '''model_name_or_path''': T5_TINY,
          '''tokenizer_name''': T5_TINY,
      }
      self._test_distiller_cli(__UpperCamelCase	)



  def      _UpperCamelCase    (	self    :				List[Any]	)					-> Optional[Any]:
      SCREAMING_SNAKE_CASE__					     =						{
          '''teacher''': T5_TINY,
          '''student''': T5_TINIER,
          '''model_name_or_path''': T5_TINIER,
          '''tokenizer_name''': T5_TINIER,
      }
      self._test_distiller_cli(__UpperCamelCase	)







  def      _UpperCamelCase    (	self    :				List[Any]     ,  A_    :				Dict     ,  A_    :				Dict=True	)					-> List[str]:
      SCREAMING_SNAKE_CASE__					     =						{
          '''label_smoothing''': 0.0,
          '''early_stopping_patience''': -1,
          '''train_batch_size''': 1,
          '''eval_batch_size''': 2,
          '''max_epochs''': 2,
          '''alpha_mlm''': 0.2,
          '''alpha_ce''': 0.8,
          '''do_predict''': True,
          '''model_name_or_path''': '''sshleifer/tinier_bart''',
          '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
          '''val_check_interval''': 0.5,
      }
      default_updates.update(__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__					     =						CHEAP_ARGS.copy()
      SCREAMING_SNAKE_CASE__					     =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
      SCREAMING_SNAKE_CASE__					     =						self.get_auto_remove_tmp_dir()

      args_d.update(data_dir=__UpperCamelCase     ,  output_dir=__UpperCamelCase     ,  **__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__					     =						distill_main(argparse.Namespace(**__UpperCamelCase	)	)
      if not check_contents:
          return model
      SCREAMING_SNAKE_CASE__					     =						os.listdir(__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__					     =						{os.path.basename(__UpperCamelCase	) for p in contents}
      SCREAMING_SNAKE_CASE__					     =						[p for p in contents if p.endswith("ckpt"	)]
      assert len(__UpperCamelCase	) > 0

      self.assertIn("test_generations.txt"     ,  __UpperCamelCase	)
      self.assertIn("test_results.txt"     ,  __UpperCamelCase	)

      SCREAMING_SNAKE_CASE__					     =						load_json(model.metrics_save_path	)
      SCREAMING_SNAKE_CASE__					     =						metrics['''val'''][-1]
      self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]     ,  0.01	)
      self.assertGreaterEqual(1.0     ,  last_step_stats["val_avg_gen_time"]	)
      self.assertIsInstance(last_step_stats[f"val_avg_{model.val_metric}"]     ,  __UpperCamelCase	)
      SCREAMING_SNAKE_CASE__					     =						int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1	)
      self.assertEqual(len(metrics["val"]	)     ,  __UpperCamelCase	)
      self.assertEqual(len(metrics["test"]	)     ,  1	)
      return model







class    UpperCamelCase_			(  UpperCamelCase_			):


  """simple docstring"""



  @parameterized.expand(
      [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]     ,  )
  def      _UpperCamelCase    (	self    :				Any     ,  A_    :				str	)					-> str:
      SCREAMING_SNAKE_CASE__					     =						CHEAP_ARGS.copy()
      SCREAMING_SNAKE_CASE__					     =						'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
      SCREAMING_SNAKE_CASE__					     =						0.1 if task == '''translation''' else 0

      SCREAMING_SNAKE_CASE__					     =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
      SCREAMING_SNAKE_CASE__					     =						self.get_auto_remove_tmp_dir()
      args_d.update(
          data_dir=__UpperCamelCase     ,  model_name_or_path=__UpperCamelCase     ,  tokenizer_name=__UpperCamelCase     ,  train_batch_size=2     ,  eval_batch_size=2     ,  output_dir=__UpperCamelCase     ,  do_predict=__UpperCamelCase     ,  task=__UpperCamelCase     ,  src_lang="en_XX"     ,  tgt_lang="ro_RO"     ,  freeze_encoder=__UpperCamelCase     ,  freeze_embeds=__UpperCamelCase     ,  )
      assert "n_train" in args_d
      SCREAMING_SNAKE_CASE__					     =						argparse.Namespace(**__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__					     =						main(__UpperCamelCase	)

      SCREAMING_SNAKE_CASE__					     =						module.model.get_input_embeddings()
      assert not input_embeds.weight.requires_grad
      if model == T5_TINY:
          SCREAMING_SNAKE_CASE__					     =						module.model.lm_head
          assert not lm_head.weight.requires_grad
          assert (lm_head.weight == input_embeds.weight).all().item()
      elif model == FSMT_TINY:
          SCREAMING_SNAKE_CASE__					     =						module.model.model
          SCREAMING_SNAKE_CASE__					     =						fsmt.decoder.embed_positions
          assert not embed_pos.weight.requires_grad
          assert not fsmt.decoder.embed_tokens.weight.requires_grad
          # check that embeds are not the same
          assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
      else:
          SCREAMING_SNAKE_CASE__					     =						module.model.model
          SCREAMING_SNAKE_CASE__					     =						bart.decoder.embed_positions
          assert not embed_pos.weight.requires_grad
          assert not bart.shared.weight.requires_grad
          # check that embeds are the same
          assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
          assert bart.decoder.embed_tokens == bart.shared

      SCREAMING_SNAKE_CASE__					     =						load_json(module.output_dir / "text_batch.json"	)
      assert isinstance(__UpperCamelCase     ,  __UpperCamelCase	)
      assert len(__UpperCamelCase	) >= 4



  def      _UpperCamelCase    (	self    :				int	)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE__					     =						CHEAP_ARGS.copy()

      SCREAMING_SNAKE_CASE__					     =						'''summarization'''
      SCREAMING_SNAKE_CASE__					     =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)

      args_d.update(
          data_dir=__UpperCamelCase     ,  tokenizer_name=__UpperCamelCase     ,  train_batch_size=2     ,  eval_batch_size=2     ,  do_predict=__UpperCamelCase     ,  task=__UpperCamelCase     ,  src_lang="en_XX"     ,  tgt_lang="ro_RO"     ,  freeze_encoder=__UpperCamelCase     ,  freeze_embeds=__UpperCamelCase     ,  )

      # test models whose config includes the extra_model_args
      SCREAMING_SNAKE_CASE__					     =						BART_TINY
      SCREAMING_SNAKE_CASE__					     =						self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE__					     =						args_d.copy()
      args_da.update(
          model_name_or_path=__UpperCamelCase     ,  output_dir=__UpperCamelCase     ,  )
      SCREAMING_SNAKE_CASE__					     =						('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
      for p in extra_model_params:
          SCREAMING_SNAKE_CASE__					     =						0.5
      SCREAMING_SNAKE_CASE__					     =						argparse.Namespace(**__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__					     =						main(__UpperCamelCase	)
      for p in extra_model_params:
          assert getattr(model.config     ,  __UpperCamelCase	) == 0.5, f"failed to override the model config for param {p}"

      # test models whose config doesn't include the extra_model_args
      SCREAMING_SNAKE_CASE__					     =						T5_TINY
      SCREAMING_SNAKE_CASE__					     =						self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE__					     =						args_d.copy()
      args_da.update(
          model_name_or_path=__UpperCamelCase     ,  output_dir=__UpperCamelCase     ,  )
      SCREAMING_SNAKE_CASE__					     =						'''encoder_layerdrop'''
      SCREAMING_SNAKE_CASE__					     =						0.5
      SCREAMING_SNAKE_CASE__					     =						argparse.Namespace(**__UpperCamelCase	)
      with pytest.raises(__UpperCamelCase	) as excinfo:
          SCREAMING_SNAKE_CASE__					     =						main(__UpperCamelCase	)
      assert str(excinfo.value	) == f"model config doesn\'t have a `{unsupported_param}` attribute"







  def      _UpperCamelCase    (	self    :				Optional[int]	)					-> Optional[int]:
      SCREAMING_SNAKE_CASE__					     =						CHEAP_ARGS.copy()

      SCREAMING_SNAKE_CASE__					     =						'''summarization'''
      SCREAMING_SNAKE_CASE__					     =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)

      SCREAMING_SNAKE_CASE__					     =						BART_TINY
      SCREAMING_SNAKE_CASE__					     =						self.get_auto_remove_tmp_dir()

      args_d.update(
          data_dir=__UpperCamelCase     ,  model_name_or_path=__UpperCamelCase     ,  output_dir=__UpperCamelCase     ,  tokenizer_name=__UpperCamelCase     ,  train_batch_size=2     ,  eval_batch_size=2     ,  do_predict=__UpperCamelCase     ,  task=__UpperCamelCase     ,  src_lang="en_XX"     ,  tgt_lang="ro_RO"     ,  freeze_encoder=__UpperCamelCase     ,  freeze_embeds=__UpperCamelCase     ,  )

      # emulate finetune.py
      SCREAMING_SNAKE_CASE__					     =						argparse.ArgumentParser()
      SCREAMING_SNAKE_CASE__					     =						pl.Trainer.add_argparse_args(__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__					     =						SummarizationModule.add_model_specific_args(__UpperCamelCase     ,  os.getcwd()	)
      SCREAMING_SNAKE_CASE__					     =						{'''--help''': True}

      # --help test
      with pytest.raises(__UpperCamelCase	) as excinfo:
          with CaptureStdout() as cs:
              SCREAMING_SNAKE_CASE__					     =						parser.parse_args(__UpperCamelCase	)
          assert False, "--help is expected to sys.exit"
      assert excinfo.type == SystemExit
      SCREAMING_SNAKE_CASE__					     =						lightning_base.arg_to_scheduler_metavar
      assert expected in cs.out, "--help is expected to list the supported schedulers"

      # --lr_scheduler=non_existing_scheduler test
      SCREAMING_SNAKE_CASE__					     =						'''non_existing_scheduler'''
      SCREAMING_SNAKE_CASE__					     =						{f"--lr_scheduler={unsupported_param}"}
      with pytest.raises(__UpperCamelCase	) as excinfo:
          with CaptureStderr() as cs:
              SCREAMING_SNAKE_CASE__					     =						parser.parse_args(__UpperCamelCase	)
          assert False, "invalid argument is expected to sys.exit"
      assert excinfo.type == SystemExit
      SCREAMING_SNAKE_CASE__					     =						f"invalid choice: \'{unsupported_param}\'"
      assert expected in cs.err, f"should have bailed on invalid choice of scheduler {unsupported_param}"

      # --lr_scheduler=existing_scheduler test
      SCREAMING_SNAKE_CASE__					     =						'''cosine'''
      SCREAMING_SNAKE_CASE__					     =						args_d.copy()
      SCREAMING_SNAKE_CASE__					     =						supported_param
      SCREAMING_SNAKE_CASE__					     =						argparse.Namespace(**__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__					     =						main(__UpperCamelCase	)
      assert (
          getattr(model.hparams     ,  "lr_scheduler"	) == supported_param
      ), f"lr_scheduler={supported_param} shouldn\'t fail"





from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A__        =			"\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n"

A__        =			"\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow's tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n"


A__        =			"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]\n    >>> sari = datasets.load_metric(\"sari\")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 26.953601953601954}\n"





def 							__a       (					__A ,       __A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						[rgram for rgrams in rgramslist for rgram in rgrams]
    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)

    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						Counter()
    for sgram, scount in sgramcounter.items():
        SCREAMING_SNAKE_CASE__					     =						scount * numref

    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						Counter()
    for cgram, ccount in cgramcounter.items():
        SCREAMING_SNAKE_CASE__					     =						ccount * numref

    # KEEP
    SCREAMING_SNAKE_CASE__					     =						sgramcounter_rep & cgramcounter_rep
    SCREAMING_SNAKE_CASE__					     =						keepgramcounter_rep & rgramcounter
    SCREAMING_SNAKE_CASE__					     =						sgramcounter_rep & rgramcounter

    SCREAMING_SNAKE_CASE__					     =						0
    SCREAMING_SNAKE_CASE__					     =						0
    for keepgram in keepgramcountergood_rep:
        keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
        # Fix an alleged bug [2] in the keep score computation.
        # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
        keeptmpscorea += keepgramcountergood_rep[keepgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    #      a target exactly.
    SCREAMING_SNAKE_CASE__					     =						1
    SCREAMING_SNAKE_CASE__					     =						1
    if len(__A	) > 0:
        SCREAMING_SNAKE_CASE__					     =						keeptmpscorea / len(__A	)
    if len(__A	) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        SCREAMING_SNAKE_CASE__					     =						keeptmpscorea / sum(keepgramcounterall_rep.values()	)
    SCREAMING_SNAKE_CASE__					     =						0
    if keepscore_precision > 0 or keepscore_recall > 0:
        SCREAMING_SNAKE_CASE__					     =						2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    SCREAMING_SNAKE_CASE__					     =						sgramcounter_rep - cgramcounter_rep
    SCREAMING_SNAKE_CASE__					     =						delgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE__					     =						sgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE__					     =						0
    SCREAMING_SNAKE_CASE__					     =						0
    for delgram in delgramcountergood_rep:
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    SCREAMING_SNAKE_CASE__					     =						1
    if len(__A	) > 0:
        SCREAMING_SNAKE_CASE__					     =						deltmpscorea / len(__A	)

    # ADDITION
    SCREAMING_SNAKE_CASE__					     =						set(__A	) - set(__A	)
    SCREAMING_SNAKE_CASE__					     =						set(__A	) & set(__A	)
    SCREAMING_SNAKE_CASE__					     =						set(__A	) - set(__A	)

    SCREAMING_SNAKE_CASE__					     =						0
    for addgram in addgramcountergood:
        addtmpscore += 1

    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    SCREAMING_SNAKE_CASE__					     =						1
    SCREAMING_SNAKE_CASE__					     =						1
    if len(__A	) > 0:
        SCREAMING_SNAKE_CASE__					     =						addtmpscore / len(__A	)
    if len(__A	) > 0:
        SCREAMING_SNAKE_CASE__					     =						addtmpscore / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						0
    if addscore_precision > 0 or addscore_recall > 0:
        SCREAMING_SNAKE_CASE__					     =						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						len(__A	)

    SCREAMING_SNAKE_CASE__					     =						ssent.split(" "	)
    SCREAMING_SNAKE_CASE__					     =						csent.split(" "	)
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]

    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    SCREAMING_SNAKE_CASE__					     =						[]
    for rsent in rsents:
        SCREAMING_SNAKE_CASE__					     =						rsent.split(" "	)
        SCREAMING_SNAKE_CASE__					     =						[]
        SCREAMING_SNAKE_CASE__					     =						[]
        SCREAMING_SNAKE_CASE__					     =						[]
        ragramslist.append(__A	)
        for i in range(0 ,       len(__A	) - 1	):
            if i < len(__A	) - 1:
                SCREAMING_SNAKE_CASE__					     =						ragrams[i] + " " + ragrams[i + 1]
                ragrams.append(__A	)
            if i < len(__A	) - 2:
                SCREAMING_SNAKE_CASE__					     =						ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
                ragrams.append(__A	)
            if i < len(__A	) - 3:
                SCREAMING_SNAKE_CASE__					     =						ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
                ragrams.append(__A	)
        ragramslist.append(__A	)
        ragramslist.append(__A	)
        ragramslist.append(__A	)

    for i in range(0 ,       len(__A	) - 1	):
        if i < len(__A	) - 1:
            SCREAMING_SNAKE_CASE__					     =						sagrams[i] + " " + sagrams[i + 1]
            sagrams.append(__A	)
        if i < len(__A	) - 2:
            SCREAMING_SNAKE_CASE__					     =						sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
            sagrams.append(__A	)
        if i < len(__A	) - 3:
            SCREAMING_SNAKE_CASE__					     =						sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
            sagrams.append(__A	)

    for i in range(0 ,       len(__A	) - 1	):
        if i < len(__A	) - 1:
            SCREAMING_SNAKE_CASE__					     =						cagrams[i] + " " + cagrams[i + 1]
            cagrams.append(__A	)
        if i < len(__A	) - 2:
            SCREAMING_SNAKE_CASE__					     =						cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
            cagrams.append(__A	)
        if i < len(__A	) - 3:
            SCREAMING_SNAKE_CASE__					     =						cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
            cagrams.append(__A	)

    ((SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__))					     =						SARIngram(__A ,       __A ,       __A ,       __A	)
    ((SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__))					     =						SARIngram(__A ,       __A ,       __A ,       __A	)
    ((SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__))					     =						SARIngram(__A ,       __A ,       __A ,       __A	)
    ((SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__)     ,		(SCREAMING_SNAKE_CASE__))					     =						SARIngram(__A ,       __A ,       __A ,       __A	)
    SCREAMING_SNAKE_CASE__					     =						sum([keepascore, keepascore, keepascore, keepascore]	) / 4
    SCREAMING_SNAKE_CASE__					     =						sum([delascore, delascore, delascore, delascore]	) / 4
    SCREAMING_SNAKE_CASE__					     =						sum([addascore, addascore, addascore, addascore]	) / 4
    SCREAMING_SNAKE_CASE__					     =						(avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore





def 							__a       (					__A ,       __A = True ,       __A = "13a" ,       __A = True	):
    # Normalization is requried for the ASSET dataset (one of the primary
    # datasets in sentence simplification) to allow using space
    # to split the sentence. Even though Wiki-Auto and TURK datasets,
    # do not require normalization, we do it for consistency.
    # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
    # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

    if lowercase:
        SCREAMING_SNAKE_CASE__					     =						sentence.lower()

    if tokenizer in ["13a", "intl"]:
        if version.parse(sacrebleu.__version__	).major >= 2:
            SCREAMING_SNAKE_CASE__					     =						sacrebleu.metrics.bleu._get_tokenizer(__A	)()(__A	)
        else:
            SCREAMING_SNAKE_CASE__					     =						sacrebleu.TOKENIZERS[tokenizer]()(__A	)
    elif tokenizer == "moses":
        SCREAMING_SNAKE_CASE__					     =						sacremoses.MosesTokenizer().tokenize(__A ,       return_str=__A ,       escape=__A	)
    elif tokenizer == "penn":
        SCREAMING_SNAKE_CASE__					     =						sacremoses.MosesTokenizer().penn_tokenize(__A ,       return_str=__A	)
    else:
        SCREAMING_SNAKE_CASE__					     =						sentence

    if not return_str:
        SCREAMING_SNAKE_CASE__					     =						normalized_sent.split()

    return normalized_sent







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,     _KWARGS_DESCRIPTION			)
class    UpperCamelCase_			(  datasets.Metric			):


  """simple docstring"""



  def      _UpperCamelCase    (	self    :				List[str]	)					-> int:
      return datasets.MetricInfo(
          description=_DESCRIPTION     ,  citation=_CITATION     ,  inputs_description=_KWARGS_DESCRIPTION     ,  features=datasets.Features(
              {
                  "sources": datasets.Value("string"     ,  id="sequence"	),
                  "predictions": datasets.Value("string"     ,  id="sequence"	),
                  "references": datasets.Sequence(datasets.Value("string"     ,  id="sequence"	)     ,  id="references"	),
              }	)     ,  codebase_urls=[
              "https://github.com/cocoxu/simplification/blob/master/SARI.py",
              "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
          ]     ,  reference_urls=["https://www.aclweb.org/anthology/Q16-1029.pdf"]     ,  )







  def      _UpperCamelCase    (	self    :				Optional[Any]     ,  A_    :				int     ,  A_    :				Tuple     ,  A_    :				int	)					-> Dict:
      if not (len(A_	) == len(A_	) == len(A_	)):
          raise ValueError("Sources length must match predictions and references lengths."	)
      SCREAMING_SNAKE_CASE__					     =						0
      for src, pred, refs in zip(A_     ,  A_     ,  A_	):
          sari_score += SARIsent(normalize(A_	)     ,  normalize(A_	)     ,  [normalize(A_	) for sent in refs]	)
      SCREAMING_SNAKE_CASE__					     =						sari_score / len(A_	)
      return {"sari": 100 * sari_score}


