
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_lowercase											=    random.Random()
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Union[str, Any]			,							UpperCAmelCase_	:     Optional[Any]=1.0			,							UpperCAmelCase_	:     Dict=None			,							UpperCAmelCase_	:     List[str]=None				)			->						Union[str, Any]:
       if rng is None:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=global_rng

       SCREAMING_SNAKE_CASE_							:				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=2_000						,       __A=1						,       __A=0.0						,       __A=16_000						,       __A=True						,       __A=True						,       )						->    Dict:
              SCREAMING_SNAKE_CASE_							:				str		=parent
              SCREAMING_SNAKE_CASE_							:				str		=batch_size
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=min_seq_length
              SCREAMING_SNAKE_CASE_							:				Tuple		=max_seq_length
              SCREAMING_SNAKE_CASE_							:				List[str]		=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=feature_size
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=padding_value
              SCREAMING_SNAKE_CASE_							:				str		=sampling_rate
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=return_attention_mask
              SCREAMING_SNAKE_CASE_							:				str		=do_normalize


       def 	_snake_case				(    self				)						->    Dict:
              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 	_snake_case				(    self						,       __A=False						,       __A=False				)						->    int:
              def _flatten(__A				):
                     return list(itertools.chain(*__A				)				)

              if equal_length:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=floats_list((self.batch_size, self.max_seq_length)				)
              else:
                     # make sure that inputs increase in size
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[
                         _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:
                     SCREAMING_SNAKE_CASE_							:				int		=[np.asarray(__A				) for x in speech_inputs]

              return speech_inputs






class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						WavaVecaFeatureExtractor


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=WavaVecaFeatureExtractionTester(self				)


       def 	_snake_case				(    self						,       __A				)						->    List[str]:
              self.assertTrue(np.all(np.mean(__A						,       axis=0				) < 1e-3				)				)
              self.assertTrue(np.all(np.abs(np.var(__A						,       axis=0				) - 1				) < 1e-3				)				)


       def 	_snake_case				(    self				)						->    Optional[Any]:
              # Tests that all call wrap to encode_plus and batch_encode_plus
              SCREAMING_SNAKE_CASE_							:				Dict		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
              # create three inputs of length 800, 1000, and 1200
              SCREAMING_SNAKE_CASE_							:				List[str]		=[floats_list((1, x)				)[0] for x in range(800						,       1_400						,       200				)]
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[np.asarray(__A				) for speech_input in speech_inputs]

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

              # Test batched
              SCREAMING_SNAKE_CASE_							:				int		=feat_extract(__A						,       return_tensors='''np'''				).input_values
              SCREAMING_SNAKE_CASE_							:				List[Any]		=feat_extract(__A						,       return_tensors='''np'''				).input_values
              for enc_seq_a, enc_seq_a in zip(__A						,       __A				):
                     self.assertTrue(np.allclose(__A						,       __A						,       atol=1e-3				)				)

              # Test 2-D numpy arrays are batched.
              SCREAMING_SNAKE_CASE_							:				Any		=[floats_list((1, x)				)[0] for x in (800, 800, 800)]
              SCREAMING_SNAKE_CASE_							:				List[Any]		=np.asarray(__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=feat_extract(__A						,       return_tensors='''np'''				).input_values
              SCREAMING_SNAKE_CASE_							:				List[str]		=feat_extract(__A						,       return_tensors='''np'''				).input_values
              for enc_seq_a, enc_seq_a in zip(__A						,       __A				):
                     self.assertTrue(np.allclose(__A						,       __A						,       atol=1e-3				)				)


       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=[floats_list((1, x)				)[0] for x in range(800						,       1_400						,       200				)]

              SCREAMING_SNAKE_CASE_							:				List[Any]		=['''longest''', '''max_length''', '''do_not_pad''']
              SCREAMING_SNAKE_CASE_							:				Any		=[None, 1_600, None]
              for max_length, padding in zip(__A						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				int		=feat_extract(__A						,       padding=__A						,       max_length=__A						,       return_tensors='''np'''				)
                     SCREAMING_SNAKE_CASE_							:				List[str]		=processed.input_values

                     self._check_zero_mean_unit_variance(input_values[0][:800]				)
                     self.assertTrue(input_values[0][800:].sum() < 1e-6				)
                     self._check_zero_mean_unit_variance(input_values[1][:1_000]				)
                     self.assertTrue(input_values[0][1_000:].sum() < 1e-6				)
                     self._check_zero_mean_unit_variance(input_values[2][:1_200]				)


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Any		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
              SCREAMING_SNAKE_CASE_							:				Any		=range(800						,       1_400						,       200				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=[floats_list((1, x)				)[0] for x in lengths]

              SCREAMING_SNAKE_CASE_							:				Tuple		=['''longest''', '''max_length''', '''do_not_pad''']
              SCREAMING_SNAKE_CASE_							:				Dict		=[None, 1_600, None]

              for max_length, padding in zip(__A						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=feat_extract(__A						,       max_length=__A						,       padding=__A				)
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=processed.input_values

                     self._check_zero_mean_unit_variance(input_values[0][:800]				)
                     self._check_zero_mean_unit_variance(input_values[1][:1_000]				)
                     self._check_zero_mean_unit_variance(input_values[2][:1_200]				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
              SCREAMING_SNAKE_CASE_							:				Dict		=[floats_list((1, x)				)[0] for x in range(800						,       1_400						,       200				)]
              SCREAMING_SNAKE_CASE_							:				Dict		=feat_extract(
                  __A						,       truncation=__A						,       max_length=1_000						,       padding='''max_length'''						,       return_tensors='''np'''				)
              SCREAMING_SNAKE_CASE_							:				Dict		=processed.input_values

              self._check_zero_mean_unit_variance(input_values[0, :800]				)
              self._check_zero_mean_unit_variance(input_values[1]				)
              self._check_zero_mean_unit_variance(input_values[2]				)


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
              SCREAMING_SNAKE_CASE_							:				Dict		=[floats_list((1, x)				)[0] for x in range(800						,       1_400						,       200				)]
              SCREAMING_SNAKE_CASE_							:				int		=feat_extract(
                  __A						,       truncation=__A						,       max_length=1_000						,       padding='''longest'''						,       return_tensors='''np'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=processed.input_values

              self._check_zero_mean_unit_variance(input_values[0, :800]				)
              self._check_zero_mean_unit_variance(input_values[1, :1_000]				)
              self._check_zero_mean_unit_variance(input_values[2]				)

              # make sure that if max_length < longest -> then pad to max_length
              self.assertTrue(input_values.shape == (3, 1_000)				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[floats_list((1, x)				)[0] for x in range(800						,       1_400						,       200				)]
              SCREAMING_SNAKE_CASE_							:				Any		=feat_extract(
                  __A						,       truncation=__A						,       max_length=2_000						,       padding='''longest'''						,       return_tensors='''np'''				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=processed.input_values

              self._check_zero_mean_unit_variance(input_values[0, :800]				)
              self._check_zero_mean_unit_variance(input_values[1, :1_000]				)
              self._check_zero_mean_unit_variance(input_values[2]				)

              # make sure that if max_length > longest -> then pad to longest
              self.assertTrue(input_values.shape == (3, 1_200)				)


       @require_torch
       def 	_snake_case				(    self				)						->    Any:
              import torch

              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=np.random.rand(100				).astype(np.floataa				)
              SCREAMING_SNAKE_CASE_							:				str		=np_speech_inputs.tolist()

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





       @slow
       @require_torch
       def 	_snake_case				(    self				)						->    Optional[int]:
              # this test makes sure that models that are using
              # group norm don't have their feature extractor return the
              # attention_mask
              for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=WavaVecaConfig.from_pretrained(__A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=WavaVecaFeatureExtractor.from_pretrained(__A				)

                     # only "layer" feature extraction norm should make use of
                     # attention_mask
                     self.assertEqual(feat_extract.return_attention_mask						,       config.feat_extract_norm == '''layer'''				)


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowercase											=    """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

_lowercase											=    """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

_lowercase											=    """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""


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


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return datasets.MetricInfo(
                  description=_DESCRIPTION						,       citation=_CITATION						,       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/google-research/tree/master/rouge''']						,       reference_urls=[
                      '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
                      '''https://github.com/google-research/google-research/tree/master/rouge''',
                  ]						,       )





       def 	_snake_case				(    self						,       __A						,       __A						,       __A=None						,       __A=True						,       __A=False				)						->    List[str]:
              if rouge_types is None:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

              SCREAMING_SNAKE_CASE_							:				Tuple		=rouge_scorer.RougeScorer(rouge_types=__A						,       use_stemmer=__A				)
              if use_aggregator:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=scoring.BootstrapAggregator()
              else:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=[]

              for ref, pred in zip(__A						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				int		=scorer.score(__A						,       __A				)
                     if use_aggregator:
                            aggregator.add_scores(__A				)
                     else:
                            scores.append(__A				)

              if use_aggregator:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=aggregator.aggregate()
              else:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		={}
                     for key in scores[0]:
                            SCREAMING_SNAKE_CASE_							:				Tuple		=[score[key] for score in scores]

              return result
