"""simple docstring"""


import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


__UpperCamelCase								=    torch.cuda.is_available()
__UpperCamelCase								=    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__UpperCamelCase								=    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__UpperCamelCase								=    {
    '''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       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   Optional[Any]:
     snake_case_       =      '\n'.join(UpperCAmelCase      )
     Path(UpperCAmelCase      ).open('w'      ).writelines(UpperCAmelCase      )







def       UpperCAmelCase					(	UpperCAmelCase      )       ->   List[str]:
     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(UpperCAmelCase		,      f'{split}.source'      )		,      UpperCAmelCase      )
          _dump_articles(os.path.join(UpperCAmelCase		,      f'{split}.target'      )		,      UpperCAmelCase      )
     return tmp_dir




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       @classmethod
       def 	a_		(				cls)   ->   List[Any]:
            return cls







       @require_torch_multi_gpu
       def 	a_		(				self)   ->   Union[str, Any]:
            snake_case_       =      {
                'no_teacher': True,
                'freeze_encoder': True,
                'gpus': 2,
                'overwrite_output_dir': True,
                'sortish_sampler': True,
            }
            self._test_distiller_cli_fork(lowerCAmelCase__,							check_contents=lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__=True)   ->   str:
            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(lowerCAmelCase__)
            snake_case_       =      CHEAP_ARGS.copy()
            snake_case_       =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
            snake_case_       =      self.get_auto_remove_tmp_dir()
            args_d.update(data_dir=lowerCAmelCase__,							output_dir=lowerCAmelCase__,							**lowerCAmelCase__)

            def convert(lowerCAmelCase__,							lowerCAmelCase__):
                 if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                      return ""
                 if v is False or v is None:
                      return ""
                 if v is True:  # or len(str(v))==0:
                      return f'--{k}'
                 return f'--{k}={v}'

            snake_case_       =      [x for x in (convert(lowerCAmelCase__,							lowerCAmelCase__) for k, v in args_d.items()) if len(lowerCAmelCase__)]
            snake_case_       =      [sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
            execute_subprocess_async(lowerCAmelCase__,							env=self.get_env())

            snake_case_       =      os.listdir(lowerCAmelCase__)
            snake_case_       =      {os.path.basename(lowerCAmelCase__) for p in contents}
            snake_case_       =      [p for p in contents if p.endswith('ckpt')]
            assert len(lowerCAmelCase__) > 0

            self.assertIn('test_generations.txt',							lowerCAmelCase__)
            self.assertIn('test_results.txt',							lowerCAmelCase__)

            # get the following from the module, (we don't have access to `model` here)
            snake_case_       =      os.path.join(lowerCAmelCase__,							'metrics.json')
            snake_case_       =      'rouge2'

            snake_case_       =      load_json(lowerCAmelCase__)
            # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
            print(lowerCAmelCase__)
            snake_case_       =      metrics['val'][-1]
            self.assertGreaterEqual(last_step_stats['val_avg_gen_time'],							0.01)
            self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}'],							lowerCAmelCase__)
            self.assertEqual(len(metrics['test']),							1)
            snake_case_       =      int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1)
            self.assertEqual(len(metrics['val']),							lowerCAmelCase__)


"""simple docstring"""


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets







@contextmanager
def       UpperCAmelCase					(	)       ->   str:
     def filter_log(UpperCAmelCase      ):
          return False if "This IS expected if you are initializing" in record.msg else True

     snake_case_       =      datasets.utils.logging.get_logger('transformers.modeling_utils'      )
     logger.addFilter(UpperCAmelCase      )
     try:
          yield
     finally:
          logger.removeFilter(UpperCAmelCase      )


__UpperCamelCase								=    '''\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
'''

__UpperCamelCase								=    '''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

__UpperCamelCase								=    '''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''




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







       def 	a_		(				self)   ->   Dict:
            return datasets.MetricInfo(
                description=_DESCRIPTION,							citation=_CITATION,							homepage='https://github.com/Tiiiger/bert_score',							inputs_description=_KWARGS_DESCRIPTION,							features=datasets.Features(
                    {
                        'predictions': datasets.Value('string',							id='sequence'),
                        'references': datasets.Sequence(datasets.Value('string',							id='sequence'),							id='references'),
                    }),							codebase_urls=['https://github.com/Tiiiger/bert_score'],							reference_urls=[
                    'https://github.com/Tiiiger/bert_score',
                    'https://arxiv.org/abs/1904.09675',
                ],							)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=None,							lowerCAmelCase__=64,							lowerCAmelCase__=4,							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=None,							lowerCAmelCase__=False,							)   ->   Union[str, Any]:
            snake_case_       =      bert_score.utils.get_hash
            snake_case_       =      bert_score.BERTScorer

            if version.parse(bert_score.__version__) >= version.parse('0.3.10'):
                 snake_case_       =      functools.partial(lowerCAmelCase__,							use_fast_tokenizer=lowerCAmelCase__)
                 snake_case_       =      functools.partial(lowerCAmelCase__,							use_fast_tokenizer=lowerCAmelCase__)
            elif use_fast_tokenizer:
                 raise ImportWarning(
                     'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
                     'You can install it with `pip install "bert-score>=0.3.10"`.')

            if model_type is None:
                 assert lang is not None, "either lang or model_type should be specified"
                 snake_case_       =      bert_score.utils.langamodel[lang.lower()]

            if num_layers is None:
                 snake_case_       =      bert_score.utils.modelalayers[model_type]

            snake_case_       =      get_hash(
                model=lowerCAmelCase__,							num_layers=lowerCAmelCase__,							idf=lowerCAmelCase__,							rescale_with_baseline=lowerCAmelCase__,							use_custom_baseline=baseline_path is not None,							)

            with filter_logging_context():
                 if not hasattr(self,							'cached_bertscorer') or self.cached_bertscorer.hash != hashcode:
                      snake_case_       =      scorer(
                          model_type=lowerCAmelCase__,							num_layers=lowerCAmelCase__,							batch_size=lowerCAmelCase__,							nthreads=lowerCAmelCase__,							all_layers=lowerCAmelCase__,							idf=lowerCAmelCase__,							device=lowerCAmelCase__,							lang=lowerCAmelCase__,							rescale_with_baseline=lowerCAmelCase__,							baseline_path=lowerCAmelCase__,							)

            ((snake_case_) ,			(snake_case_) ,			(snake_case_))       =      self.cached_bertscorer.score(
                cands=lowerCAmelCase__,							refs=lowerCAmelCase__,							verbose=lowerCAmelCase__,							batch_size=lowerCAmelCase__,							)
            snake_case_       =      {
                'precision': P.tolist(),
                'recall': R.tolist(),
                'f1': F.tolist(),
                'hashcode': hashcode,
            }
            return output_dict







       def 	a_		(				self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							**lowerCAmelCase__)   ->   List[str]:
            # References can be strings or lists of strings
            # Let's change strings to lists of strings with one element
            if references is not None:
                 snake_case_       =      [[ref] if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else ref for ref in references]
            super().add_batch(predictions=lowerCAmelCase__,							references=lowerCAmelCase__,							**lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							**lowerCAmelCase__)   ->   List[str]:
            # References can be strings or lists of strings
            # Let's change strings to lists of strings with one element
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      [reference]
            super().add(prediction=lowerCAmelCase__,							reference=lowerCAmelCase__,							**lowerCAmelCase__)

