"""simple docstring"""


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)

__UpperCamelCase								=    logging.getLogger()
__UpperCamelCase								=    torch.cuda.is_available()
__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      )       ->   Dict:
     snake_case_       =      '\n'.join(_UpperCAmelCase      )
     Path(_UpperCAmelCase      ).open('w'      ).writelines(_UpperCAmelCase      )


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


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







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






     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)   ->   Tuple:
            logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
            return cls







       @slow
       @require_torch_gpu
       def 	a_		(				self)   ->   Tuple:
            snake_case_       =      list_models()
            snake_case_       =      'sshleifer'
            snake_case_       =      [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase_)]
            snake_case_       =      ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
            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 	a_		(				self)   ->   int:
            snake_case_       =      {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
            self._test_distiller_cli(UpperCAmelCase_)







       def 	a_		(				self)   ->   Dict:
            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',
            }
            snake_case_       =      self._test_distiller_cli(UpperCAmelCase_,							check_contents=UpperCAmelCase_)

            snake_case_       =      list(Path(model.output_dir).glob('*.ckpt'))
            self.assertEqual(1,							len(UpperCAmelCase_))
            snake_case_       =      list(Path(model.output_dir).glob('**/*.bin'))
            self.assertEqual(len(UpperCAmelCase_),							2)
            snake_case_       =      lmap(str.strip,							Path(model.hparams.data_dir).joinpath('test.source').open().readlines())
            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())

            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 	a_		(				self)   ->   Dict:
            snake_case_       =      AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_)
            snake_case_       =      model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
            snake_case_       =      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]],							dtype=torch.long,							device=model.device)
            snake_case_       =      target_ids[:, :-1].contiguous()  # Why this line?
            snake_case_       =      target_ids[:, 1:].clone()  # why clone?
            snake_case_       =      model(
                UpperCAmelCase_,							attention_mask=UpperCAmelCase_,							decoder_input_ids=UpperCAmelCase_,							labels=UpperCAmelCase_,							use_cache=UpperCAmelCase_).loss

            snake_case_       =      model(UpperCAmelCase_,							attention_mask=UpperCAmelCase_,							decoder_input_ids=UpperCAmelCase_,							use_cache=UpperCAmelCase_).logits

            snake_case_       =      nn.functional.log_softmax(UpperCAmelCase_,							dim=-1)
            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 	a_		(				self)   ->   Optional[int]:
            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',
            }
            snake_case_       =      self._test_distiller_cli(UpperCAmelCase_,							check_contents=UpperCAmelCase_)
            assert model.model.config.model_type == "mbart"

            snake_case_       =      list(Path(model.output_dir).glob('*.ckpt'))
            self.assertEqual(1,							len(UpperCAmelCase_))
            snake_case_       =      list(Path(model.output_dir).glob('**/*.bin'))
            snake_case_       =      list(Path(model.output_dir).glob('best_tfmr/*'))
            assert len(UpperCAmelCase_) > 2
            self.assertEqual(len(UpperCAmelCase_),							2)







       def 	a_		(				self)   ->   Any:
            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 	a_		(				self)   ->   Dict:
            snake_case_       =      {
                'teacher': T5_TINY,
                'student': T5_TINIER,
                'model_name_or_path': T5_TINIER,
                'tokenizer_name': T5_TINIER,
            }
            self._test_distiller_cli(UpperCAmelCase_)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__=True)   ->   Dict:
            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_)
            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=UpperCAmelCase_,							output_dir=UpperCAmelCase_,							**UpperCAmelCase_)
            snake_case_       =      distill_main(argparse.Namespace(**UpperCAmelCase_))
            if not check_contents:
                 return model
            snake_case_       =      os.listdir(UpperCAmelCase_)
            snake_case_       =      {os.path.basename(UpperCAmelCase_) for p in contents}
            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_)

            snake_case_       =      load_json(model.metrics_save_path)
            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_)
            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      (       lowerCAmelCase__     ):







       @parameterized.expand(
           [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY],							)
       def 	a_		(				self,							lowerCAmelCase__)   ->   Dict:
            snake_case_       =      CHEAP_ARGS.copy()
            snake_case_       =      'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
            snake_case_       =      0.1 if task == 'translation' else 0

            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=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
            snake_case_       =      argparse.Namespace(**UpperCAmelCase_)
            snake_case_       =      main(UpperCAmelCase_)

            snake_case_       =      module.model.get_input_embeddings()
            assert not input_embeds.weight.requires_grad
            if model == T5_TINY:
                 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:
                 snake_case_       =      module.model.model
                 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:
                 snake_case_       =      module.model.model
                 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

            snake_case_       =      load_json(module.output_dir / 'text_batch.json')
            assert isinstance(UpperCAmelCase_,							UpperCAmelCase_)
            assert len(UpperCAmelCase_) >= 4







       def 	a_		(				self)   ->   Tuple:
            snake_case_       =      CHEAP_ARGS.copy()

            snake_case_       =      'summarization'
            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
            snake_case_       =      BART_TINY
            snake_case_       =      self.get_auto_remove_tmp_dir()
            snake_case_       =      args_d.copy()
            args_da.update(
                model_name_or_path=UpperCAmelCase_,							output_dir=UpperCAmelCase_,							)
            snake_case_       =      ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
            for p in extra_model_params:
                 snake_case_       =      0.5
            snake_case_       =      argparse.Namespace(**UpperCAmelCase_)
            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
            snake_case_       =      T5_TINY
            snake_case_       =      self.get_auto_remove_tmp_dir()
            snake_case_       =      args_d.copy()
            args_da.update(
                model_name_or_path=UpperCAmelCase_,							output_dir=UpperCAmelCase_,							)
            snake_case_       =      'encoder_layerdrop'
            snake_case_       =      0.5
            snake_case_       =      argparse.Namespace(**UpperCAmelCase_)
            with pytest.raises(UpperCAmelCase_) as excinfo:
                 snake_case_       =      main(UpperCAmelCase_)
            assert str(excinfo.value) == f'model config doesn\'t have a `{unsupported_param}` attribute'







       def 	a_		(				self)   ->   Tuple:
            snake_case_       =      CHEAP_ARGS.copy()

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

            snake_case_       =      BART_TINY
            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
            snake_case_       =      argparse.ArgumentParser()
            snake_case_       =      pl.Trainer.add_argparse_args(UpperCAmelCase_)
            snake_case_       =      SummarizationModule.add_model_specific_args(UpperCAmelCase_,							os.getcwd())
            snake_case_       =      {'--help': True}

            # --help test
            with pytest.raises(UpperCAmelCase_) as excinfo:
                 with CaptureStdout() as cs:
                      snake_case_       =      parser.parse_args(UpperCAmelCase_)
                 assert False, "--help is expected to sys.exit"
            assert excinfo.type == SystemExit
            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
            snake_case_       =      'non_existing_scheduler'
            snake_case_       =      {f'--lr_scheduler={unsupported_param}'}
            with pytest.raises(UpperCAmelCase_) as excinfo:
                 with CaptureStderr() as cs:
                      snake_case_       =      parser.parse_args(UpperCAmelCase_)
                 assert False, "invalid argument is expected to sys.exit"
            assert excinfo.type == SystemExit
            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
            snake_case_       =      'cosine'
            snake_case_       =      args_d.copy()
            snake_case_       =      supported_param
            snake_case_       =      argparse.Namespace(**UpperCAmelCase_)
            snake_case_       =      main(UpperCAmelCase_)
            assert (
                getattr(model.hparams,							'lr_scheduler') == supported_param
            ), f'lr_scheduler={supported_param} shouldn\'t fail'


"""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__)

