




import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_snake_case            = datasets.logging.get_logger(__name__)


_snake_case            = "\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

_snake_case            = "\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

_snake_case            = "\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

_snake_case            = {
    "bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
    "bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
    "bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
    "bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
    "bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
    "bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
    "BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
    "BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
    "BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
    "BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}







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

					def       a__			(						self       )		->							Tuple:
											return datasets.MetricInfo(
											    description=_DESCRIPTION	,  citation=_CITATION	,  homepage="""https://github.com/google-research/bleurt"""	,  inputs_description=_KWARGS_DESCRIPTION	,  features=datasets.Features(
											        {
											            """predictions""": datasets.Value("""string"""	,  id="""sequence"""       ),
											            """references""": datasets.Value("""string"""	,  id="""sequence"""       ),
											        }       )	,  codebase_urls=["""https://github.com/google-research/bleurt"""]	,  reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""]	,  )

					def       a__			(						self	,  _a       )		->							Union[str, Any]:
											# check that config name specifies a valid BLEURT model
											if self.config_name == "default":
																	logger.warning(
																	    """Using default BLEURT-Base checkpoint for sequence maximum length 128. """
																	    """You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."""       )
																	_A							:  List[Any]    =		"""bleurt-base-128"""

											if self.config_name.lower() in CHECKPOINT_URLS:
																	_A							:  Optional[int]    =		self.config_name.lower()

											elif self.config_name.upper() in CHECKPOINT_URLS:
																	_A							:  List[Any]    =		self.config_name.upper()

											else:
																	raise KeyError(
																	    F'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''       )

											# download the model checkpoint specified by self.config_name and set up the scorer
											_A							:  str    =		dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]       )
											_A							:  Any    =		score.BleurtScorer(os.path.join(_a	,  _a       )       )



					def       a__			(						self	,  _a	,  _a       )		->							Tuple:
											_A							:  Union[str, Any]    =		self.scorer.score(references=_a	,  candidates=_a       )
											return {"scores": scores}







import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
  import torch

if is_datasets_available():
  from datasets import load_dataset

_snake_case            = random.Random()







def        lowerCAmelCase_			(  snake_case_,snake_case_=1.0,snake_case_=None,snake_case_=None  ):
      if rng is None:
            _A							:  str    =		global_rng

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

      return values







class 		lowercase							(     unittest.TestCase	):

     def __init__(						self	,  _a	,  _a=7	,  _a=400	,  _a=2000	,  _a=2048	,  _a=128	,  _a=1	,  _a=512	,  _a=30	,  _a=4_4100	,  )		->							Tuple:
           _A							:  Any    =		parent
           _A							:  str    =		batch_size
           _A							:  Union[str, Any]    =		min_seq_length
           _A							:  int    =		max_seq_length
           _A							:  List[str]    =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           _A							:  Tuple    =		spectrogram_length
           _A							:  int    =		feature_size
           _A							:  str    =		num_audio_channels
           _A							:  Tuple    =		hop_length
           _A							:  List[str]    =		chunk_length
           _A							:  Union[str, Any]    =		sampling_rate

     def       a__			(						self       )		->							Tuple:
           return {
               "spectrogram_length": self.spectrogram_length,
               "feature_size": self.feature_size,
               "num_audio_channels": self.num_audio_channels,
               "hop_length": self.hop_length,
               "chunk_length": self.chunk_length,
               "sampling_rate": self.sampling_rate,
           }



     def       a__			(						self	,  _a=False	,  _a=False       )		->							Optional[int]:
           def _flatten(_a       ):
                 return list(itertools.chain(*_a       )       )

           if equal_length:
                 _A							:  List[str]    =		[floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
           else:
                 # make sure that inputs increase in size
                 _A							:  Union[str, Any]    =		[
                     floats_list((x, self.feature_size)       )
                     for x in range(self.min_seq_length	,  self.max_seq_length	,  self.seq_length_diff       )
                 ]
           if numpify:
                 _A							:  List[Any]    =		[np.asarray(_a       ) for x in speech_inputs]
           return speech_inputs







@require_torch
@require_torchaudio
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		TvltFeatureExtractor

     def       a__			(						self       )		->							Any:
           _A							:  int    =		TvltFeatureExtractionTester(self       )

     def       a__			(						self       )		->							List[Any]:
           _A							:  Tuple    =		self.feature_extraction_class(**self.feat_extract_dict       )
           self.assertTrue(hasattr(_a	,  """spectrogram_length"""       )       )
           self.assertTrue(hasattr(_a	,  """feature_size"""       )       )
           self.assertTrue(hasattr(_a	,  """num_audio_channels"""       )       )
           self.assertTrue(hasattr(_a	,  """hop_length"""       )       )
           self.assertTrue(hasattr(_a	,  """chunk_length"""       )       )
           self.assertTrue(hasattr(_a	,  """sampling_rate"""       )       )

     def       a__			(						self       )		->							Optional[int]:
           _A							:  str    =		self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:  Tuple    =		feat_extract_first.save_pretrained(_a       )[0]
                 check_json_file_has_correct_format(_a       )
                 _A							:  Optional[int]    =		self.feature_extraction_class.from_pretrained(_a       )

           _A							:  Optional[Any]    =		feat_extract_first.to_dict()
           _A							:  int    =		feat_extract_second.to_dict()
           _A							:  int    =		dict_first.pop("""mel_filters"""       )
           _A							:  Optional[int]    =		dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(_a	,  _a       )       )
           self.assertEqual(_a	,  _a       )

     def       a__			(						self       )		->							int:
           _A							:  str    =		self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:  List[str]    =		os.path.join(_a	,  """feat_extract.json"""       )
                 feat_extract_first.to_json_file(_a       )
                 _A							:  Union[str, Any]    =		self.feature_extraction_class.from_json_file(_a       )

           _A							:  Optional[Any]    =		feat_extract_first.to_dict()
           _A							:  Union[str, Any]    =		feat_extract_second.to_dict()
           _A							:  List[Any]    =		dict_first.pop("""mel_filters"""       )
           _A							:  Optional[Any]    =		dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(_a	,  _a       )       )
           self.assertEqual(_a	,  _a       )

     def       a__			(						self       )		->							Optional[Any]:
           # Initialize feature_extractor
           _A							:  Tuple    =		self.feature_extraction_class(**self.feat_extract_dict       )

           # create three inputs of length 800, 1000, and 1200
           _A							:  Optional[int]    =		[floats_list((1, x)       )[0] for x in range(800	,  1400	,  200       )]
           _A							:  List[str]    =		[np.asarray(_a       ) for speech_input in speech_inputs]

           # Test not batched input
           _A							:  Optional[int]    =		feature_extractor(np_speech_inputs[0]	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test batched
           _A							:  str    =		feature_extractor(_a	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test audio masking
           _A							:  Optional[Any]    =		feature_extractor(
               _a	,  return_tensors="""np"""	,  sampling_rate=4_4100	,  mask_audio=_a       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test 2-D numpy arrays are batched.
           _A							:  Dict    =		[floats_list((1, x)       )[0] for x in (800, 800, 800)]
           _A							:  Optional[int]    =		np.asarray(_a       )
           _A							:  Optional[Any]    =		feature_extractor(_a	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).audio_values
           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

     def       a__			(						self	,  _a       )		->							str:
           _A							:  Optional[Any]    =		load_dataset("""hf-internal-testing/librispeech_asr_dummy"""	,  """clean"""	,  split="""validation"""       )
           # automatic decoding with librispeech
           _A							:  Dict    =		ds.sort("""id"""       ).select(range(_a       )       )[:num_samples]["""audio"""]

           return [x["array"] for x in speech_samples]



     def       a__			(						self       )		->							Optional[Any]:
           _A							:  List[str]    =		self._load_datasamples(1       )
           _A							:  List[str]    =		TvltFeatureExtractor()
           _A							:  List[Any]    =		feature_extractor(_a	,  return_tensors="""pt"""       ).audio_values

           self.assertEquals(audio_values.shape	,  (1, 1, 192, 128)       )

           _A							:  int    =		torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]       )
           self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]	,  _a	,  atol=1e-4       )       )

