





"""simple docstring"""




from jiwer import compute_measures

import datasets


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

A__   :					str							=   '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe 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.\n\nThis 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.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A__   :					List[Any]							=   '\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,					_KWARGS_DESCRIPTION )
class 			__magic_name__       (       datasets.Metric ):
       def 							lowercase_							(     self       )     ->	List[str]:





         """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 							lowercase_							(     self ,				A_=None ,				A_=None ,				A_=False       )     ->	str:





         """simple docstring"""




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


"""simple docstring"""




import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_PRETRAINING_MAPPING,
      MobileBertForMaskedLM,
      MobileBertForMultipleChoice,
      MobileBertForNextSentencePrediction,
      MobileBertForPreTraining,
      MobileBertForQuestionAnswering,
      MobileBertForSequenceClassification,
      MobileBertForTokenClassification,
      MobileBertModel,
  )


class 			__magic_name__       :
       def __init__(     self ,				A_ ,				A_=13 ,				A_=7 ,				A_=True ,				A_=True ,				A_=True ,				A_=True ,				A_=99 ,				A_=64 ,				A_=32 ,				A_=5 ,				A_=4 ,				A_=37 ,				A_="gelu" ,				A_=0.1 ,				A_=0.1 ,				A_=512 ,				A_=16 ,				A_=2 ,				A_=0.02 ,				A_=3 ,				A_=4 ,				A_=None ,				)     ->	Dict:





         """simple docstring"""




         _lowercase:			List[Any]       =     parent
         _lowercase:			str       =     batch_size
         _lowercase:			List[str]       =     seq_length
         _lowercase:			Any       =     is_training
         _lowercase:			Union[str, Any]       =     use_input_mask
         _lowercase:			Any       =     use_token_type_ids
         _lowercase:			int       =     use_labels
         _lowercase:			Any       =     vocab_size
         _lowercase:			Optional[int]       =     hidden_size
         _lowercase:			Optional[int]       =     embedding_size
         _lowercase:			Union[str, Any]       =     num_hidden_layers
         _lowercase:			Optional[int]       =     num_attention_heads
         _lowercase:			Optional[int]       =     intermediate_size
         _lowercase:			List[str]       =     hidden_act
         _lowercase:			Dict       =     hidden_dropout_prob
         _lowercase:			Any       =     attention_probs_dropout_prob
         _lowercase:			Tuple       =     max_position_embeddings
         _lowercase:			List[str]       =     type_vocab_size
         _lowercase:			Any       =     type_sequence_label_size
         _lowercase:			int       =     initializer_range
         _lowercase:			Any       =     num_labels
         _lowercase:			int       =     num_choices
         _lowercase:			List[str]       =     scope
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size       )

         _lowercase:			Optional[Any]       =     None
         if self.use_input_mask:
           _lowercase:			Any       =     random_attention_mask([self.batch_size, self.seq_length]       )

         _lowercase:			Tuple       =     None
         if self.use_token_type_ids:
           _lowercase:			str       =     ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size       )

         _lowercase:			Dict       =     None
         _lowercase:			Tuple       =     None
         _lowercase:			List[str]       =     None
         if self.use_labels:
           _lowercase:			str       =     ids_tensor([self.batch_size] ,				self.type_sequence_label_size       )
           _lowercase:			int       =     ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels       )
           _lowercase:			Dict       =     ids_tensor([self.batch_size] ,				self.num_choices       )

         _lowercase:			str       =     self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         return MobileBertConfig(
             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 ,				embedding_size=self.embedding_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=A_ ,				initializer_range=self.initializer_range ,				)
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_       )     ->	Any:





         """simple docstring"""




         _lowercase:			Tuple       =     MobileBertModel(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Tuple       =     model(A_ ,				attention_mask=A_ ,				token_type_ids=A_       )
         _lowercase:			Dict       =     model(A_ ,				token_type_ids=A_       )
         _lowercase:			Optional[int]       =     model(A_       )

         self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertEqual(result.pooler_output.shape ,				(self.batch_size, self.hidden_size)       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_       )     ->	int:





         """simple docstring"""




         _lowercase:			int       =     MobileBertForMaskedLM(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Optional[int]       =     model(A_ ,				attention_mask=A_ ,				token_type_ids=A_ ,				labels=A_       )
         self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_       )     ->	int:





         """simple docstring"""




         _lowercase:			Any       =     MobileBertForNextSentencePrediction(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			int       =     model(
             A_ ,				attention_mask=A_ ,				token_type_ids=A_ ,				labels=A_ ,				)
         self.parent.assertEqual(result.logits.shape ,				(self.batch_size, 2)       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			str       =     MobileBertForPreTraining(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			List[str]       =     model(
             A_ ,				attention_mask=A_ ,				token_type_ids=A_ ,				labels=A_ ,				next_sentence_label=A_ ,				)
         self.parent.assertEqual(result.prediction_logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)       )
         self.parent.assertEqual(result.seq_relationship_logits.shape ,				(self.batch_size, 2)       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     MobileBertForQuestionAnswering(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Dict       =     model(
             A_ ,				attention_mask=A_ ,				token_type_ids=A_ ,				start_positions=A_ ,				end_positions=A_ ,				)
         self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     self.num_labels
         _lowercase:			int       =     MobileBertForSequenceClassification(A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			List[Any]       =     model(A_ ,				attention_mask=A_ ,				token_type_ids=A_ ,				labels=A_       )
         self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Dict       =     self.num_labels
         _lowercase:			Optional[Any]       =     MobileBertForTokenClassification(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Optional[int]       =     model(A_ ,				attention_mask=A_ ,				token_type_ids=A_ ,				labels=A_       )
         self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     self.num_choices
         _lowercase:			List[Any]       =     MobileBertForMultipleChoice(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			Any       =     input_ids.unsqueeze(1       ).expand(-1 ,				self.num_choices ,				-1       ).contiguous()
         _lowercase:			Tuple       =     token_type_ids.unsqueeze(1       ).expand(-1 ,				self.num_choices ,				-1       ).contiguous()
         _lowercase:			List[str]       =     input_mask.unsqueeze(1       ).expand(-1 ,				self.num_choices ,				-1       ).contiguous()
         _lowercase:			Any       =     model(
             A_ ,				attention_mask=A_ ,				token_type_ids=A_ ,				labels=A_ ,				)
         self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)       )



       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			Any       =     self.prepare_config_and_inputs()
         (
             (
             _lowercase
         ) ,				(
             _lowercase
         ) ,				(
             _lowercase
         ) ,				(
             _lowercase
         ) ,				(
             _lowercase
         ) ,				(
             _lowercase
         ) ,				(
             _lowercase
         ) ,				
         ):			Dict       =     config_and_inputs
         _lowercase:			int       =     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_torch
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		(
           (
               MobileBertModel,
               MobileBertForMaskedLM,
               MobileBertForMultipleChoice,
               MobileBertForNextSentencePrediction,
               MobileBertForPreTraining,
               MobileBertForQuestionAnswering,
               MobileBertForSequenceClassification,
               MobileBertForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       UpperCamelCase_   =		(
           {
               '''feature-extraction''': MobileBertModel,
               '''fill-mask''': MobileBertForMaskedLM,
               '''question-answering''': MobileBertForQuestionAnswering,
               '''text-classification''': MobileBertForSequenceClassification,
               '''token-classification''': MobileBertForTokenClassification,
               '''zero-shot''': MobileBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCamelCase_   =		True
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_=False       )     ->	str:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     super()._prepare_for_class(A_ ,				A_ ,				return_labels=A_       )

         if return_labels:
           if model_class in get_values(A_       ):
             _lowercase:			Tuple       =     torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.seq_length) ,				dtype=torch.long ,				device=A_       )
             _lowercase:			Tuple       =     torch.zeros(
                 self.model_tester.batch_size ,				dtype=torch.long ,				device=A_       )
         return inputs_dict
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     MobileBertModelTester(self       )
         _lowercase:			str       =     ConfigTester(self ,				config_class=A_ ,				hidden_size=37       )
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         self.config_tester.run_common_tests()
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			str       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_model(*A_       )
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_masked_lm(*A_       )
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			List[str]       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_multiple_choice(*A_       )
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Any       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*A_       )
       def 							lowercase_							(     self       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_pretraining(*A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_question_answering(*A_       )
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			str       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_sequence_classification(*A_       )



       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Dict       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_token_classification(*A_       )




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  return torch.tensor(
      _UpperCamelCase ,				dtype=torch.long ,				device=_UpperCamelCase ,				)


A__   :					Optional[Any]							=   1e-3







@require_torch
@require_sentencepiece
@require_tokenizers
class 			__magic_name__       (       unittest.TestCase ):
       @slow
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         _lowercase:			Optional[int]       =     MobileBertModel.from_pretrained('''google/mobilebert-uncased'''       ).to(A_       )
         _lowercase:			str       =     _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]       )
         with torch.no_grad():
           _lowercase:			Dict       =     model(A_       )[0]
         _lowercase:			List[Any]       =     torch.Size((1, 9, 512)       )
         self.assertEqual(output.shape ,				A_       )
         _lowercase:			Tuple       =     torch.tensor(
             [
                 [
                     [-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
                     [-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
                     [2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
                 ]
             ] ,				device=A_ ,				)

         # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
         # ~1 difference, it's therefore not a good idea to measure using addition.
         # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
         # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
         _lowercase:			Tuple       =     torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE       )
         _lowercase:			Dict       =     torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE       )

         self.assertTrue(lower_bound and upper_bound       )
