





from jiwer import compute_measures

import datasets


lowerCAmelCase_    =    '''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

lowerCAmelCase_    =    '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''

lowerCAmelCase_    =    '''
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = datasets.load_metric("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
'''



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






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							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/jitsi/jiwer/'''] ,							reference_urls=[
							        '''https://en.wikipedia.org/wiki/Word_error_rate''',
							    ] ,							)






			def       lowerCamelCase  (self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=False   )					->       Tuple:


							'''simple docstring'''



							if concatenate_texts:
											return compute_measures(__magic_name__ ,							__magic_name__   )["wer"]
							else:
											snake_case_						:   int           =  0
											snake_case_						:   str           =  0
											for prediction, reference in zip(__magic_name__ ,							__magic_name__   ):
															snake_case_						:   List[str]           =  compute_measures(__magic_name__ ,							__magic_name__   )
															incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
															total += measures["substitutions"] + measures["deletions"] + measures["hits"]
											return incorrect / total







import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.albert.modeling_flax_albert import (
	    FlaxAlbertForMaskedLM,
	    FlaxAlbertForMultipleChoice,
	    FlaxAlbertForPreTraining,
	    FlaxAlbertForQuestionAnswering,
	    FlaxAlbertForSequenceClassification,
	    FlaxAlbertForTokenClassification,
	    FlaxAlbertModel,
	)



class  __lowerCAmelCase	(      unittest.TestCase					):






			def __init__(self ,							__magic_name__ ,							__magic_name__=13 ,							__magic_name__=7 ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=99 ,							__magic_name__=32 ,							__magic_name__=5 ,							__magic_name__=4 ,							__magic_name__=37 ,							__magic_name__="gelu" ,							__magic_name__=0.1 ,							__magic_name__=0.1 ,							__magic_name__=512 ,							__magic_name__=16 ,							__magic_name__=2 ,							__magic_name__=0.02 ,							__magic_name__=4 ,							)					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  parent
							snake_case_						:   Optional[Any]           =  batch_size
							snake_case_						:   List[Any]           =  seq_length
							snake_case_						:   Tuple           =  is_training
							snake_case_						:   List[str]           =  use_attention_mask
							snake_case_						:   Any           =  use_token_type_ids
							snake_case_						:   Dict           =  use_labels
							snake_case_						:   Optional[Any]           =  vocab_size
							snake_case_						:   Dict           =  hidden_size
							snake_case_						:   List[Any]           =  num_hidden_layers
							snake_case_						:   Union[str, Any]           =  num_attention_heads
							snake_case_						:   Any           =  intermediate_size
							snake_case_						:   Optional[int]           =  hidden_act
							snake_case_						:   Optional[int]           =  hidden_dropout_prob
							snake_case_						:   Optional[Any]           =  attention_probs_dropout_prob
							snake_case_						:   Optional[int]           =  max_position_embeddings
							snake_case_						:   Optional[int]           =  type_vocab_size
							snake_case_						:   List[Any]           =  type_sequence_label_size
							snake_case_						:   Dict           =  initializer_range
							snake_case_						:   Dict           =  num_choices






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   int           =  ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size   )

							snake_case_						:   Any           =  None
							if self.use_attention_mask:
											snake_case_						:   List[Any]           =  random_attention_mask([self.batch_size, self.seq_length]   )

							snake_case_						:   List[Any]           =  None
							if self.use_token_type_ids:
											snake_case_						:   Dict           =  ids_tensor([self.batch_size, self.seq_length] ,							self.type_vocab_size   )

							snake_case_						:   List[Any]           =  AlbertConfig(
							    vocab_size=self.vocab_size ,							hidden_size=self.hidden_size ,							num_hidden_layers=self.num_hidden_layers ,							num_attention_heads=self.num_attention_heads ,							intermediate_size=self.intermediate_size ,							hidden_act=self.hidden_act ,							hidden_dropout_prob=self.hidden_dropout_prob ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob ,							max_position_embeddings=self.max_position_embeddings ,							type_vocab_size=self.type_vocab_size ,							is_decoder=__magic_name__ ,							initializer_range=self.initializer_range ,							)

							return config, input_ids, token_type_ids, attention_mask






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.prepare_config_and_inputs()
							snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Optional[int]           =  config_and_inputs
							snake_case_						:   int           =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
							return config, inputs_dict




@require_flax
class  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					Optional[int]	      =				(
			    (
			        FlaxAlbertModel,
			        FlaxAlbertForPreTraining,
			        FlaxAlbertForMaskedLM,
			        FlaxAlbertForMultipleChoice,
			        FlaxAlbertForQuestionAnswering,
			        FlaxAlbertForSequenceClassification,
			        FlaxAlbertForTokenClassification,
			        FlaxAlbertForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  FlaxAlbertModelTester(self   )






			@slow
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							for model_class_name in self.all_model_classes:
											snake_case_						:   Dict           =  model_class_name.from_pretrained('''albert-base-v2'''   )
											snake_case_						:   Union[str, Any]           =  model(np.ones((1, 1)   )   )
											self.assertIsNotNone(__magic_name__   )




@require_flax
class  __lowerCAmelCase	(      unittest.TestCase					):






			@slow
			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  FlaxAlbertModel.from_pretrained('''albert-base-v2'''   )
							snake_case_						:   Optional[int]           =  np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
							snake_case_						:   Dict           =  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
							snake_case_						:   Union[str, Any]           =  model(__magic_name__ ,							attention_mask=__magic_name__   )[0]
							snake_case_						:   Tuple           =  (1, 11, 768)
							self.assertEqual(output.shape ,							__magic_name__   )
							snake_case_						:   str           =  np.array(
							    [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]   )

							self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,							__magic_name__ ,							atol=1e-4   )   )
