






import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


UpperCAmelCase_ :						List[str]      =      '''\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",
    author = "Lin, Chin-Yew  and
      Och, Franz Josef",
    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",
    month = "aug 23{--}aug 27",
    year = "2004",
    address = "Geneva, Switzerland",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/C04-1072",
    pages = "501--507",
}
'''

UpperCAmelCase_ :						Optional[Any]      =      '''\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,
the better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
'''

UpperCAmelCase_ :						Union[str, Any]      =      '''
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    \'bleu\': bleu score,
    \'precisions\': geometric mean of n-gram precisions,
    \'brevity_penalty\': brevity penalty,
    \'length_ratio\': ratio of lengths,
    \'translation_length\': translation_length,
    \'reference_length\': reference_length
Examples:

    >>> predictions = [
    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample
    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)
    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric("bleu")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results["bleu"])
    1.0
'''

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





	def 						_A (		self				:					int	):
		return datasets.MetricInfo(
		    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
		        {
		            """predictions""": datasets.Sequence(datasets.Value("""string""" ,  id="""token"""	) ,  id="""sequence"""	),
		            """references""": datasets.Sequence(
		                datasets.Sequence(datasets.Value("""string""" ,  id="""token"""	) ,  id="""sequence"""	) ,  id="""references"""	),
		        }	) ,  codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""] ,  reference_urls=[
		        """https://en.wikipedia.org/wiki/BLEU""",
		        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
		    ] ,  )





	def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Optional[int]=4 ,  __lowerCamelCase				:					Optional[Any]=False	):
		UpperCamelCase     :List[Any]          =	compute_bleu(
		    reference_corpus=__lowerCamelCase ,  translation_corpus=__lowerCamelCase ,  max_order=__lowerCamelCase ,  smooth=__lowerCamelCase	)
		((UpperCamelCase)     ,      (UpperCamelCase)     ,      (UpperCamelCase)     ,      (UpperCamelCase)     ,      (UpperCamelCase)     ,      (UpperCamelCase))     :List[Any]          =	score
		return {
		    "bleu": bleu,
		    "precisions": precisions,
		    "brevity_penalty": bp,
		    "length_ratio": ratio,
		    "translation_length": translation_length,
		    "reference_length": reference_length,
		}








"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase_				     =     random.Random()

if is_torch_available():
       import torch



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase=1.0		,       __UpperCamelCase=None		,       __UpperCamelCase=None ):




  """simple docstring"""







  if rng is None:
    __A									= global_rng

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

  return values


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     Any,       _lowerCamelCase :     List[str],       _lowerCamelCase :     Any=7,       _lowerCamelCase :     Optional[int]=4_00,       _lowerCamelCase :     Optional[int]=20_00,       _lowerCamelCase :     Dict=1,       _lowerCamelCase :     Optional[Any]=0.0,       _lowerCamelCase :     int=1_60_00,       _lowerCamelCase :     Optional[int]=True,       _lowerCamelCase :     Dict=True,       ):

     '''simple docstring'''
     __A									= parent
     __A									= batch_size
     __A									= min_seq_length
     __A									= max_seq_length
     __A									= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     __A									= feature_size
     __A									= padding_value
     __A									= sampling_rate
     __A									= return_attention_mask
     __A									= do_normalize


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     return {
         "feature_size": self.feature_size,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }






   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     Optional[Any]=False,       _lowerCamelCase :     int=False  ):

     '''simple docstring'''
     def _flatten(_lowerCamelCase :     List[str]  ):
       return list(itertools.chain(*_lowerCamelCase  )  )

     if equal_length:
       __A									= floats_list((self.batch_size, self.max_seq_length)  )
     else:
       # make sure that inputs increase in size
       __A									= [
           _flatten(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									= [np.asarray(_lowerCamelCase  ) for x in speech_inputs]

     return speech_inputs





@require_torch
@require_torchaudio
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	int						 = ASTFeatureExtractor


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= ASTFeatureExtractionTester(self  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     # Tests that all call wrap to encode_plus and batch_encode_plus
     __A									= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
     # create three inputs of length 800, 1000, and 1200
     __A									= [floats_list((1, x)  )[0] for x in range(8_00,       14_00,       2_00  )]
     __A									= [np.asarray(_lowerCamelCase  ) for speech_input in speech_inputs]

     # Test not batched input
     __A									= feat_extract(speech_inputs[0],       return_tensors='''np'''  ).input_values
     __A									= feat_extract(np_speech_inputs[0],       return_tensors='''np'''  ).input_values
     self.assertTrue(np.allclose(_lowerCamelCase,       _lowerCamelCase,       atol=1e-3  )  )

     # Test batched
     __A									= feat_extract(_lowerCamelCase,       padding=_lowerCamelCase,       return_tensors='''np'''  ).input_values
     __A									= feat_extract(_lowerCamelCase,       padding=_lowerCamelCase,       return_tensors='''np'''  ).input_values
     for enc_seq_a, enc_seq_a in zip(_lowerCamelCase,       _lowerCamelCase  ):
       self.assertTrue(np.allclose(_lowerCamelCase,       _lowerCamelCase,       atol=1e-3  )  )

     # Test 2-D numpy arrays are batched.
     __A									= [floats_list((1, x)  )[0] for x in (8_00, 8_00, 8_00)]
     __A									= np.asarray(_lowerCamelCase  )
     __A									= feat_extract(_lowerCamelCase,       return_tensors='''np'''  ).input_values
     __A									= feat_extract(_lowerCamelCase,       return_tensors='''np'''  ).input_values
     for enc_seq_a, enc_seq_a in zip(_lowerCamelCase,       _lowerCamelCase  ):
       self.assertTrue(np.allclose(_lowerCamelCase,       _lowerCamelCase,       atol=1e-3  )  )


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     import torch

     __A									= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
     __A									= np.random.rand(1_00  ).astype(np.floataa  )
     __A									= np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
       __A									= feature_extractor.pad([{'''input_values''': inputs}],       return_tensors='''np'''  )
       self.assertTrue(np_processed.input_values.dtype == np.floataa  )
       __A									= feature_extractor.pad([{'''input_values''': inputs}],       return_tensors='''pt'''  )
       self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     from datasets import load_dataset

     __A									= load_dataset('''hf-internal-testing/librispeech_asr_dummy''',       '''clean''',       split='''validation'''  )
     # automatic decoding with librispeech
     __A									= ds.sort('''id'''  ).select(range(_lowerCamelCase  )  )[:num_samples]['''audio''']

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






   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     # fmt: off
     __A									= torch.tensor(
         [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
          -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
          -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
          -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]  )
     # fmt: on

     __A									= self._load_datasamples(1  )
     __A									= ASTFeatureExtractor()
     __A									= feature_extractor(_lowerCamelCase,       return_tensors='''pt'''  ).input_values
     self.assertEquals(input_values.shape,       (1, 10_24, 1_28)  )
     self.assertTrue(torch.allclose(input_values[0, 0, :30],       _lowerCamelCase,       atol=1e-4  )  )




