


import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
			pass

if is_speech_available():
			from transformers import EncodecFeatureExtractor, MusicgenProcessor


snake_case__     							= random.Random()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :Union[str, Any]=1.0      ,						lowercase__  :Union[str, Any]=None      ,						lowercase__  :List[Any]=None				)       ->  Dict:
						if rng is None:
												__magic_name__				:							int				=      global_rng

						__magic_name__				:							Optional[Any]				=      []
						for batch_idx in range(shape[0]				):
												values.append([]				)
												for _ in range(shape[1]				):
																		values[-1].append(rng.random() * scale				)

						return values



@require_torch
@require_sentencepiece
class     snake_case__  (      unittest.TestCase  ):
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      'facebook/musicgen-small'
										__magic_name__				:							Union[str, Any]				=      tempfile.mkdtemp()
				def  UpperCamelCase_						(  self     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										return TaTokenizer.from_pretrained(self.checkpoint     ,      **_UpperCAmelCase      )
				def  UpperCamelCase_						(  self     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										return EncodecFeatureExtractor.from_pretrained(self.checkpoint     ,      **_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										shutil.rmtree(self.tmpdirname      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      self.get_tokenizer()
										__magic_name__				:							Union[str, Any]				=      self.get_feature_extractor()

										__magic_name__				:							str				=      MusicgenProcessor(tokenizer=_UpperCAmelCase     ,      feature_extractor=_UpperCAmelCase      )

										processor.save_pretrained(self.tmpdirname      )
										__magic_name__				:							Optional[int]				=      MusicgenProcessor.from_pretrained(self.tmpdirname      )

										self.assertEqual(processor.tokenizer.get_vocab()     ,      tokenizer.get_vocab()      )
										self.assertIsInstance(processor.tokenizer     ,      _UpperCAmelCase      )

										self.assertEqual(processor.feature_extractor.to_json_string()     ,      feature_extractor.to_json_string()      )
										self.assertIsInstance(processor.feature_extractor     ,      _UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      MusicgenProcessor(tokenizer=self.get_tokenizer()     ,      feature_extractor=self.get_feature_extractor()      )
										processor.save_pretrained(self.tmpdirname      )

										__magic_name__				:							List[str]				=      self.get_tokenizer(bos_token='(BOS)'     ,      eos_token='(EOS)'      )
										__magic_name__				:							Tuple				=      self.get_feature_extractor(do_normalize=_UpperCAmelCase     ,      padding_value=1.0      )

										__magic_name__				:							Any				=      MusicgenProcessor.from_pretrained(
										    self.tmpdirname     ,      bos_token='(BOS)'     ,      eos_token='(EOS)'     ,      do_normalize=_UpperCAmelCase     ,      padding_value=1.0      )

										self.assertEqual(processor.tokenizer.get_vocab()     ,      tokenizer_add_kwargs.get_vocab()      )
										self.assertIsInstance(processor.tokenizer     ,      _UpperCAmelCase      )

										self.assertEqual(processor.feature_extractor.to_json_string()     ,      feature_extractor_add_kwargs.to_json_string()      )
										self.assertIsInstance(processor.feature_extractor     ,      _UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      self.get_feature_extractor()
										__magic_name__				:							Union[str, Any]				=      self.get_tokenizer()

										__magic_name__				:							Union[str, Any]				=      MusicgenProcessor(tokenizer=_UpperCAmelCase     ,      feature_extractor=_UpperCAmelCase      )

										__magic_name__				:							List[Any]				=      floats_list((3, 1000)      )

										__magic_name__				:							List[Any]				=      feature_extractor(_UpperCAmelCase     ,      return_tensors='np'      )
										__magic_name__				:							Optional[Any]				=      processor(_UpperCAmelCase     ,      return_tensors='np'      )

										for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum()     ,      input_processor[key].sum()     ,      delta=1E-2      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      self.get_feature_extractor()
										__magic_name__				:							List[Any]				=      self.get_tokenizer()

										__magic_name__				:							List[str]				=      MusicgenProcessor(tokenizer=_UpperCAmelCase     ,      feature_extractor=_UpperCAmelCase      )

										__magic_name__				:							Any				=      'This is a test string'

										__magic_name__				:							Optional[Any]				=      processor(text=_UpperCAmelCase      )

										__magic_name__				:							int				=      tokenizer(_UpperCAmelCase      )

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]     ,      encoded_processor[key]      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							int				=      self.get_feature_extractor()
										__magic_name__				:							List[Any]				=      self.get_tokenizer()

										__magic_name__				:							Union[str, Any]				=      MusicgenProcessor(tokenizer=_UpperCAmelCase     ,      feature_extractor=_UpperCAmelCase      )

										__magic_name__				:							int				=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										__magic_name__				:							Optional[int]				=      processor.batch_decode(sequences=_UpperCAmelCase      )
										__magic_name__				:							Optional[int]				=      tokenizer.batch_decode(_UpperCAmelCase      )

										self.assertListEqual(_UpperCAmelCase     ,      _UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      self.get_feature_extractor()
										__magic_name__				:							List[str]				=      self.get_tokenizer()

										__magic_name__				:							Optional[Any]				=      MusicgenProcessor(tokenizer=_UpperCAmelCase     ,      feature_extractor=_UpperCAmelCase      )

										self.assertListEqual(
										    processor.model_input_names     ,      feature_extractor.model_input_names     ,      msg='`processor` and `feature_extractor` model input names do not match'     ,      )





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.get_feature_extractor(padding_side='left'      )
										__magic_name__				:							Optional[int]				=      self.get_tokenizer()

										__magic_name__				:							List[Any]				=      MusicgenProcessor(tokenizer=_UpperCAmelCase     ,      feature_extractor=_UpperCAmelCase      )

										__magic_name__				:							str				=      [floats_list((1, x)      )[0] for x in range(5     ,      20     ,      5      )]
										__magic_name__				:							Optional[Any]				=      processor(_UpperCAmelCase      ).padding_mask

										__magic_name__				:							Any				=      np.asarray(floats_list((3, 20)      )      )[:, None, :]
										__magic_name__				:							str				=      processor.batch_decode(_UpperCAmelCase     ,      padding_mask=_UpperCAmelCase      )

										self.assertIsInstance(_UpperCAmelCase     ,      _UpperCAmelCase      )

										for audio in decoded_audios:
																self.assertIsInstance(_UpperCAmelCase     ,      np.ndarray      )

										self.assertTrue(decoded_audios[0].shape == (1, 10)      )
										self.assertTrue(decoded_audios[1].shape == (1, 15)      )
										self.assertTrue(decoded_audios[2].shape == (1, 20)      )






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def  __SCREAMING_SNAKE_CASE       (				)       ->  Tuple:
						def filter_log(lowercase__  :List[Any]				):
												return False if "This IS expected if you are initializing" in record.msg else True

						__magic_name__				:							Any				=      datasets.utils.logging.get_logger('transformers.modeling_utils'				)
						logger.addFilter(lowercase__				)
						try:
												yield
						finally:
												logger.removeFilter(lowercase__				)


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

snake_case__     							= '\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

snake_case__     							= '\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'



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




										"""simple docstring"""




										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  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=64     ,      _UpperCAmelCase=4     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=False     ,      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      bert_score.utils.get_hash
										__magic_name__				:							Dict				=      bert_score.BERTScorer

										if version.parse(bert_score.__version__      ) >= version.parse('0.3.10'      ):
																__magic_name__				:							Any				=      functools.partial(_UpperCAmelCase     ,      use_fast_tokenizer=_UpperCAmelCase      )
																__magic_name__				:							Tuple				=      functools.partial(_UpperCAmelCase     ,      use_fast_tokenizer=_UpperCAmelCase      )
										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"
																__magic_name__				:							List[Any]				=      bert_score.utils.langamodel[lang.lower()]

										if num_layers is None:
																__magic_name__				:							Optional[Any]				=      bert_score.utils.modelalayers[model_type]

										__magic_name__				:							List[str]				=      get_hash(
										    model=_UpperCAmelCase     ,      num_layers=_UpperCAmelCase     ,      idf=_UpperCAmelCase     ,      rescale_with_baseline=_UpperCAmelCase     ,      use_custom_baseline=baseline_path is not None     ,      )

										with filter_logging_context():
																if not hasattr(self     ,      'cached_bertscorer'      ) or self.cached_bertscorer.hash != hashcode:
																						__magic_name__				:							Optional[int]				=      scorer(
																						    model_type=_UpperCAmelCase     ,      num_layers=_UpperCAmelCase     ,      batch_size=_UpperCAmelCase     ,      nthreads=_UpperCAmelCase     ,      all_layers=_UpperCAmelCase     ,      idf=_UpperCAmelCase     ,      device=_UpperCAmelCase     ,      lang=_UpperCAmelCase     ,      rescale_with_baseline=_UpperCAmelCase     ,      baseline_path=_UpperCAmelCase     ,      )

										((__magic_name__)		, (__magic_name__)		, (__magic_name__))				:							List[Any]				=      self.cached_bertscorer.score(
										    cands=_UpperCAmelCase     ,      refs=_UpperCAmelCase     ,      verbose=_UpperCAmelCase     ,      batch_size=_UpperCAmelCase     ,      )
										__magic_name__				:							int				=      {
										    'precision': P.tolist(),
										    'recall': R.tolist(),
										    'f1': F.tolist(),
										    'hashcode': hashcode,
										}
										return output_dict
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										if references is not None:
																__magic_name__				:							List[str]				=      [[ref] if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ) else ref for ref in references]
										super().add_batch(predictions=_UpperCAmelCase     ,      references=_UpperCAmelCase     ,      **_UpperCAmelCase      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ):
																__magic_name__				:							List[Any]				=      [reference]
										super().add(prediction=_UpperCAmelCase     ,      reference=_UpperCAmelCase     ,      **_UpperCAmelCase      )


