from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase    :  Any										= {
    'auto_factory': ['get_values'],
    'configuration_auto': ['ALL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CONFIG_MAPPING', 'MODEL_NAMES_MAPPING', 'AutoConfig'],
    'feature_extraction_auto': ['FEATURE_EXTRACTOR_MAPPING', 'AutoFeatureExtractor'],
    'image_processing_auto': ['IMAGE_PROCESSOR_MAPPING', 'AutoImageProcessor'],
    'processing_auto': ['PROCESSOR_MAPPING', 'AutoProcessor'],
    'tokenization_auto': ['TOKENIZER_MAPPING', 'AutoTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase    :  List[str]										= [
        'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING',
        'MODEL_FOR_AUDIO_XVECTOR_MAPPING',
        'MODEL_FOR_BACKBONE_MAPPING',
        'MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING',
        'MODEL_FOR_CAUSAL_LM_MAPPING',
        'MODEL_FOR_CTC_MAPPING',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
        'MODEL_FOR_DEPTH_ESTIMATION_MAPPING',
        'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
        'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING',
        'MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING',
        'MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
        'MODEL_FOR_MASKED_LM_MAPPING',
        'MODEL_FOR_MASK_GENERATION_MAPPING',
        'MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
        'MODEL_FOR_OBJECT_DETECTION_MAPPING',
        'MODEL_FOR_PRETRAINING_MAPPING',
        'MODEL_FOR_QUESTION_ANSWERING_MAPPING',
        'MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
        'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
        'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
        'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
        'MODEL_FOR_TEXT_ENCODING_MAPPING',
        'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
        'MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING',
        'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING',
        'MODEL_FOR_VISION_2_SEQ_MAPPING',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING',
        'MODEL_MAPPING',
        'MODEL_WITH_LM_HEAD_MAPPING',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING',
        'AutoModel',
        'AutoBackbone',
        'AutoModelForAudioClassification',
        'AutoModelForAudioFrameClassification',
        'AutoModelForAudioXVector',
        'AutoModelForCausalLM',
        'AutoModelForCTC',
        'AutoModelForDepthEstimation',
        'AutoModelForImageClassification',
        'AutoModelForImageSegmentation',
        'AutoModelForInstanceSegmentation',
        'AutoModelForMaskGeneration',
        'AutoModelForTextEncoding',
        'AutoModelForMaskedImageModeling',
        'AutoModelForMaskedLM',
        'AutoModelForMultipleChoice',
        'AutoModelForNextSentencePrediction',
        'AutoModelForObjectDetection',
        'AutoModelForPreTraining',
        'AutoModelForQuestionAnswering',
        'AutoModelForSemanticSegmentation',
        'AutoModelForSeq2SeqLM',
        'AutoModelForSequenceClassification',
        'AutoModelForSpeechSeq2Seq',
        'AutoModelForTableQuestionAnswering',
        'AutoModelForTokenClassification',
        'AutoModelForUniversalSegmentation',
        'AutoModelForVideoClassification',
        'AutoModelForVision2Seq',
        'AutoModelForVisualQuestionAnswering',
        'AutoModelForDocumentQuestionAnswering',
        'AutoModelWithLMHead',
        'AutoModelForZeroShotImageClassification',
        'AutoModelForZeroShotObjectDetection',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase    :  List[str]										= [
        'TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
        'TF_MODEL_FOR_CAUSAL_LM_MAPPING',
        'TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
        'TF_MODEL_FOR_MASK_GENERATION_MAPPING',
        'TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
        'TF_MODEL_FOR_MASKED_LM_MAPPING',
        'TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
        'TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
        'TF_MODEL_FOR_PRETRAINING_MAPPING',
        'TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
        'TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
        'TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
        'TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
        'TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
        'TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
        'TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
        'TF_MODEL_FOR_TEXT_ENCODING_MAPPING',
        'TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
        'TF_MODEL_FOR_VISION_2_SEQ_MAPPING',
        'TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
        'TF_MODEL_MAPPING',
        'TF_MODEL_WITH_LM_HEAD_MAPPING',
        'TFAutoModel',
        'TFAutoModelForAudioClassification',
        'TFAutoModelForCausalLM',
        'TFAutoModelForImageClassification',
        'TFAutoModelForMaskedImageModeling',
        'TFAutoModelForMaskedLM',
        'TFAutoModelForMaskGeneration',
        'TFAutoModelForMultipleChoice',
        'TFAutoModelForNextSentencePrediction',
        'TFAutoModelForPreTraining',
        'TFAutoModelForDocumentQuestionAnswering',
        'TFAutoModelForQuestionAnswering',
        'TFAutoModelForSemanticSegmentation',
        'TFAutoModelForSeq2SeqLM',
        'TFAutoModelForSequenceClassification',
        'TFAutoModelForSpeechSeq2Seq',
        'TFAutoModelForTableQuestionAnswering',
        'TFAutoModelForTextEncoding',
        'TFAutoModelForTokenClassification',
        'TFAutoModelForVision2Seq',
        'TFAutoModelForZeroShotImageClassification',
        'TFAutoModelWithLMHead',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase    :  Optional[int]										= [
        'FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
        'FLAX_MODEL_FOR_CAUSAL_LM_MAPPING',
        'FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
        'FLAX_MODEL_FOR_MASKED_LM_MAPPING',
        'FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
        'FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
        'FLAX_MODEL_FOR_PRETRAINING_MAPPING',
        'FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
        'FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
        'FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
        'FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
        'FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
        'FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING',
        'FLAX_MODEL_MAPPING',
        'FlaxAutoModel',
        'FlaxAutoModelForCausalLM',
        'FlaxAutoModelForImageClassification',
        'FlaxAutoModelForMaskedLM',
        'FlaxAutoModelForMultipleChoice',
        'FlaxAutoModelForNextSentencePrediction',
        'FlaxAutoModelForPreTraining',
        'FlaxAutoModelForQuestionAnswering',
        'FlaxAutoModelForSeq2SeqLM',
        'FlaxAutoModelForSequenceClassification',
        'FlaxAutoModelForSpeechSeq2Seq',
        'FlaxAutoModelForTokenClassification',
        'FlaxAutoModelForVision2Seq',
    ]


if TYPE_CHECKING:
    from .auto_factory import get_values
    from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
    from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
    from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
    from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
    from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_auto import (
            MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
            MODEL_FOR_AUDIO_XVECTOR_MAPPING,
            MODEL_FOR_BACKBONE_MAPPING,
            MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
            MODEL_FOR_CAUSAL_LM_MAPPING,
            MODEL_FOR_CTC_MAPPING,
            MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
            MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
            MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
            MODEL_FOR_MASK_GENERATION_MAPPING,
            MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
            MODEL_FOR_MASKED_LM_MAPPING,
            MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            MODEL_FOR_OBJECT_DETECTION_MAPPING,
            MODEL_FOR_PRETRAINING_MAPPING,
            MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
            MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_TEXT_ENCODING_MAPPING,
            MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
            MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
            MODEL_FOR_VISION_2_SEQ_MAPPING,
            MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
            MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
            MODEL_MAPPING,
            MODEL_WITH_LM_HEAD_MAPPING,
            AutoBackbone,
            AutoModel,
            AutoModelForAudioClassification,
            AutoModelForAudioFrameClassification,
            AutoModelForAudioXVector,
            AutoModelForCausalLM,
            AutoModelForCTC,
            AutoModelForDepthEstimation,
            AutoModelForDocumentQuestionAnswering,
            AutoModelForImageClassification,
            AutoModelForImageSegmentation,
            AutoModelForInstanceSegmentation,
            AutoModelForMaskedImageModeling,
            AutoModelForMaskedLM,
            AutoModelForMaskGeneration,
            AutoModelForMultipleChoice,
            AutoModelForNextSentencePrediction,
            AutoModelForObjectDetection,
            AutoModelForPreTraining,
            AutoModelForQuestionAnswering,
            AutoModelForSemanticSegmentation,
            AutoModelForSeqaSeqLM,
            AutoModelForSequenceClassification,
            AutoModelForSpeechSeqaSeq,
            AutoModelForTableQuestionAnswering,
            AutoModelForTextEncoding,
            AutoModelForTokenClassification,
            AutoModelForUniversalSegmentation,
            AutoModelForVideoClassification,
            AutoModelForVisionaSeq,
            AutoModelForVisualQuestionAnswering,
            AutoModelForZeroShotImageClassification,
            AutoModelForZeroShotObjectDetection,
            AutoModelWithLMHead,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_auto import (
            TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_CAUSAL_LM_MAPPING,
            TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_MASK_GENERATION_MAPPING,
            TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
            TF_MODEL_FOR_MASKED_LM_MAPPING,
            TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            TF_MODEL_FOR_PRETRAINING_MAPPING,
            TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
            TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
            TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
            TF_MODEL_MAPPING,
            TF_MODEL_WITH_LM_HEAD_MAPPING,
            TFAutoModel,
            TFAutoModelForAudioClassification,
            TFAutoModelForCausalLM,
            TFAutoModelForDocumentQuestionAnswering,
            TFAutoModelForImageClassification,
            TFAutoModelForMaskedImageModeling,
            TFAutoModelForMaskedLM,
            TFAutoModelForMaskGeneration,
            TFAutoModelForMultipleChoice,
            TFAutoModelForNextSentencePrediction,
            TFAutoModelForPreTraining,
            TFAutoModelForQuestionAnswering,
            TFAutoModelForSemanticSegmentation,
            TFAutoModelForSeqaSeqLM,
            TFAutoModelForSequenceClassification,
            TFAutoModelForSpeechSeqaSeq,
            TFAutoModelForTableQuestionAnswering,
            TFAutoModelForTextEncoding,
            TFAutoModelForTokenClassification,
            TFAutoModelForVisionaSeq,
            TFAutoModelForZeroShotImageClassification,
            TFAutoModelWithLMHead,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_auto import (
            FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
            FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_MASKED_LM_MAPPING,
            FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            FLAX_MODEL_FOR_PRETRAINING_MAPPING,
            FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
            FLAX_MODEL_MAPPING,
            FlaxAutoModel,
            FlaxAutoModelForCausalLM,
            FlaxAutoModelForImageClassification,
            FlaxAutoModelForMaskedLM,
            FlaxAutoModelForMultipleChoice,
            FlaxAutoModelForNextSentencePrediction,
            FlaxAutoModelForPreTraining,
            FlaxAutoModelForQuestionAnswering,
            FlaxAutoModelForSeqaSeqLM,
            FlaxAutoModelForSequenceClassification,
            FlaxAutoModelForSpeechSeqaSeq,
            FlaxAutoModelForTokenClassification,
            FlaxAutoModelForVisionaSeq,
        )

else:
    import sys

    __lowerCAmelCase    :  Tuple										= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






import unittest

from cer import CER


__lowerCAmelCase									=				CER()





class     __a     (       unittest.TestCase ):
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ['White House']
        lowercase__:  Optional[int]         =  ['white house']
        # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
        lowercase__:  str         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  ['were wolf']
        lowercase__:  Union[str, Any]         =  ['werewolf']
        # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
        lowercase__:  Tuple         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1					) < 1E-6					)

        lowercase__:  Dict         =  ['werewolf']
        lowercase__:  Optional[Any]         =  ['weae     wolf']
        # S = 1, D = 1, I = 0, N = 8, CER = 0.25
        lowercase__:  Optional[int]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.2_5					) < 1E-6					)

        # consecutive whitespaces case 1
        lowercase__:  Union[str, Any]         =  ['were wolf']
        lowercase__:  int         =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        lowercase__:  int         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0					) < 1E-6					)

        # consecutive whitespaces case 2
        lowercase__:  List[str]         =  ['were   wolf']
        lowercase__:  Tuple         =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        lowercase__:  Dict         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  ['werewolf']
        lowercase__:  Dict         =  ['weaewolf']
        # S = 1, D = 0, I = 0, N = 8, CER = 0.125
        lowercase__:  int         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_2_5					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  int         =  ['werewolf']
        lowercase__:  Optional[int]         =  ['wereawolf']
        # S = 0, D = 1, I = 0, N = 8, CER = 0.125
        lowercase__:  Dict         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_2_5					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ['werewolf']
        lowercase__:  List[str]         =  ['wereolf']
        # S = 0, D = 0, I = 1, N = 8, CER = 0.125
        lowercase__:  Optional[Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.1_2_5					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  List[str]         =  ['werewolf']
        lowercase__:  List[Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertEqual(lowerCAmelCase__							,					0.0					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  int         =  ['werewolf', 'I am your father']
        lowercase__:  Union[str, Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertEqual(lowerCAmelCase__							,					0.0					)

        lowercase__:  List[str]         =  ['werewolf', 'I am your father', 'doge']
        lowercase__:  Any         =  ['werxwolf', 'I       am your father', 'doge']
        # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
        lowercase__:  int         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  List[Any]         =  ['My hovercraft', 'is full of eels']
        lowercase__:  Union[str, Any]         =  ['My hovercraft is full', ' of eels']
        # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
        # whitespace at the front of " of eels" will be strip during preporcessing
        # so need to insert 2 whitespaces
        lowercase__:  Optional[int]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__							,					concatenate_texts=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8					) < 1E-6					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        lowercase__:  Dict         =  ['我能吞下玻璃而不伤身体']
        lowercase__:  Tuple         =  [' 能吞虾玻璃而 不霜身体啦']
        # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
        lowercase__:  str         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5					) < 1E-6					)

        lowercase__:  Any         =  ['我能吞下玻璃', '而不伤身体']
        lowercase__:  Union[str, Any]         =  ['我    能 吞 下 玻 璃', '而不伤身体']
        # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
        lowercase__:  Optional[Any]         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5					) < 1E-6					)

        lowercase__:  int         =  ['我能吞下玻璃而不伤身体']
        lowercase__:  Any         =  cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)
        self.assertFalse(lowerCAmelCase__							,					0.0					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Dict         =  ['']
        lowercase__:  int         =  ['Hypothesis']
        with self.assertRaises(lowerCAmelCase__					):
               cer.compute(predictions=lowerCAmelCase__							,					references=lowerCAmelCase__					)


if __name__ == "__main__":
 unittest.main()


