from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


__lowercase											=      logging.get_logger(__name__)


__lowercase											=      OrderedDict(
    [
        # Base model mapping
        ('''albert''', '''FlaxAlbertModel'''),
        ('''bart''', '''FlaxBartModel'''),
        ('''beit''', '''FlaxBeitModel'''),
        ('''bert''', '''FlaxBertModel'''),
        ('''big_bird''', '''FlaxBigBirdModel'''),
        ('''blenderbot''', '''FlaxBlenderbotModel'''),
        ('''blenderbot-small''', '''FlaxBlenderbotSmallModel'''),
        ('''clip''', '''FlaxCLIPModel'''),
        ('''distilbert''', '''FlaxDistilBertModel'''),
        ('''electra''', '''FlaxElectraModel'''),
        ('''gpt-sw3''', '''FlaxGPT2Model'''),
        ('''gpt2''', '''FlaxGPT2Model'''),
        ('''gpt_neo''', '''FlaxGPTNeoModel'''),
        ('''gptj''', '''FlaxGPTJModel'''),
        ('''longt5''', '''FlaxLongT5Model'''),
        ('''marian''', '''FlaxMarianModel'''),
        ('''mbart''', '''FlaxMBartModel'''),
        ('''mt5''', '''FlaxMT5Model'''),
        ('''opt''', '''FlaxOPTModel'''),
        ('''pegasus''', '''FlaxPegasusModel'''),
        ('''regnet''', '''FlaxRegNetModel'''),
        ('''resnet''', '''FlaxResNetModel'''),
        ('''roberta''', '''FlaxRobertaModel'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormModel'''),
        ('''roformer''', '''FlaxRoFormerModel'''),
        ('''t5''', '''FlaxT5Model'''),
        ('''vision-text-dual-encoder''', '''FlaxVisionTextDualEncoderModel'''),
        ('''vit''', '''FlaxViTModel'''),
        ('''wav2vec2''', '''FlaxWav2Vec2Model'''),
        ('''whisper''', '''FlaxWhisperModel'''),
        ('''xglm''', '''FlaxXGLMModel'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaModel'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        # Model for pre-training mapping
        ('''albert''', '''FlaxAlbertForPreTraining'''),
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''bert''', '''FlaxBertForPreTraining'''),
        ('''big_bird''', '''FlaxBigBirdForPreTraining'''),
        ('''electra''', '''FlaxElectraForPreTraining'''),
        ('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
        ('''roberta''', '''FlaxRobertaForMaskedLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
        ('''roformer''', '''FlaxRoFormerForMaskedLM'''),
        ('''t5''', '''FlaxT5ForConditionalGeneration'''),
        ('''wav2vec2''', '''FlaxWav2Vec2ForPreTraining'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        # Model for Masked LM mapping
        ('''albert''', '''FlaxAlbertForMaskedLM'''),
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''bert''', '''FlaxBertForMaskedLM'''),
        ('''big_bird''', '''FlaxBigBirdForMaskedLM'''),
        ('''distilbert''', '''FlaxDistilBertForMaskedLM'''),
        ('''electra''', '''FlaxElectraForMaskedLM'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''roberta''', '''FlaxRobertaForMaskedLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
        ('''roformer''', '''FlaxRoFormerForMaskedLM'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''blenderbot''', '''FlaxBlenderbotForConditionalGeneration'''),
        ('''blenderbot-small''', '''FlaxBlenderbotSmallForConditionalGeneration'''),
        ('''encoder-decoder''', '''FlaxEncoderDecoderModel'''),
        ('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
        ('''marian''', '''FlaxMarianMTModel'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
        ('''pegasus''', '''FlaxPegasusForConditionalGeneration'''),
        ('''t5''', '''FlaxT5ForConditionalGeneration'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        # Model for Image-classsification
        ('''beit''', '''FlaxBeitForImageClassification'''),
        ('''regnet''', '''FlaxRegNetForImageClassification'''),
        ('''resnet''', '''FlaxResNetForImageClassification'''),
        ('''vit''', '''FlaxViTForImageClassification'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        # Model for Causal LM mapping
        ('''bart''', '''FlaxBartForCausalLM'''),
        ('''bert''', '''FlaxBertForCausalLM'''),
        ('''big_bird''', '''FlaxBigBirdForCausalLM'''),
        ('''electra''', '''FlaxElectraForCausalLM'''),
        ('''gpt-sw3''', '''FlaxGPT2LMHeadModel'''),
        ('''gpt2''', '''FlaxGPT2LMHeadModel'''),
        ('''gpt_neo''', '''FlaxGPTNeoForCausalLM'''),
        ('''gptj''', '''FlaxGPTJForCausalLM'''),
        ('''opt''', '''FlaxOPTForCausalLM'''),
        ('''roberta''', '''FlaxRobertaForCausalLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForCausalLM'''),
        ('''xglm''', '''FlaxXGLMForCausalLM'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForCausalLM'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('''albert''', '''FlaxAlbertForSequenceClassification'''),
        ('''bart''', '''FlaxBartForSequenceClassification'''),
        ('''bert''', '''FlaxBertForSequenceClassification'''),
        ('''big_bird''', '''FlaxBigBirdForSequenceClassification'''),
        ('''distilbert''', '''FlaxDistilBertForSequenceClassification'''),
        ('''electra''', '''FlaxElectraForSequenceClassification'''),
        ('''mbart''', '''FlaxMBartForSequenceClassification'''),
        ('''roberta''', '''FlaxRobertaForSequenceClassification'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForSequenceClassification'''),
        ('''roformer''', '''FlaxRoFormerForSequenceClassification'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForSequenceClassification'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        # Model for Question Answering mapping
        ('''albert''', '''FlaxAlbertForQuestionAnswering'''),
        ('''bart''', '''FlaxBartForQuestionAnswering'''),
        ('''bert''', '''FlaxBertForQuestionAnswering'''),
        ('''big_bird''', '''FlaxBigBirdForQuestionAnswering'''),
        ('''distilbert''', '''FlaxDistilBertForQuestionAnswering'''),
        ('''electra''', '''FlaxElectraForQuestionAnswering'''),
        ('''mbart''', '''FlaxMBartForQuestionAnswering'''),
        ('''roberta''', '''FlaxRobertaForQuestionAnswering'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForQuestionAnswering'''),
        ('''roformer''', '''FlaxRoFormerForQuestionAnswering'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForQuestionAnswering'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        # Model for Token Classification mapping
        ('''albert''', '''FlaxAlbertForTokenClassification'''),
        ('''bert''', '''FlaxBertForTokenClassification'''),
        ('''big_bird''', '''FlaxBigBirdForTokenClassification'''),
        ('''distilbert''', '''FlaxDistilBertForTokenClassification'''),
        ('''electra''', '''FlaxElectraForTokenClassification'''),
        ('''roberta''', '''FlaxRobertaForTokenClassification'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForTokenClassification'''),
        ('''roformer''', '''FlaxRoFormerForTokenClassification'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForTokenClassification'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('''albert''', '''FlaxAlbertForMultipleChoice'''),
        ('''bert''', '''FlaxBertForMultipleChoice'''),
        ('''big_bird''', '''FlaxBigBirdForMultipleChoice'''),
        ('''distilbert''', '''FlaxDistilBertForMultipleChoice'''),
        ('''electra''', '''FlaxElectraForMultipleChoice'''),
        ('''roberta''', '''FlaxRobertaForMultipleChoice'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMultipleChoice'''),
        ('''roformer''', '''FlaxRoFormerForMultipleChoice'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMultipleChoice'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        ('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
    ]
)

__lowercase											=      OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

__lowercase											=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__lowercase											=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__lowercase											=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__lowercase											=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__lowercase											=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__lowercase											=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__lowercase											=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__lowercase											=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__lowercase											=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__lowercase											=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__lowercase											=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__lowercase											=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__lowercase											=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__lowercase											=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		str      			=   FLAX_MODEL_MAPPING
__lowercase											=      auto_class_update(FlaxAutoModel)


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		Any      			=   FLAX_MODEL_FOR_PRETRAINING_MAPPING
__lowercase											=      auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		Optional[int]      			=   FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
__lowercase											=      auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		Optional[Any]      			=   FLAX_MODEL_FOR_MASKED_LM_MAPPING
__lowercase											=      auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		Optional[int]      			=   FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__lowercase											=      auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		Tuple      			=   FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__lowercase											=      auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		Optional[int]      			=   FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
__lowercase											=      auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		Dict      			=   FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
__lowercase											=      auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		List[Any]      			=   FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
__lowercase											=      auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		List[Any]      			=   FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
__lowercase											=      auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		int      			=   FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
__lowercase											=      auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		List[str]      			=   FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
__lowercase											=      auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')


class 			lowerCamelCase_  (     _BaseAutoModelClass				):






    '''simple docstring'''






    a__			:		Any      			=   FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
__lowercase											=      auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		List[Any]      			=   """wavlm"""







    def __init__(			self				,				__lowercase=32				,				__lowercase=768				,				__lowercase=12				,				__lowercase=12				,				__lowercase=3_072				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.02				,				__lowercase=1E-5				,				__lowercase="group"				,				__lowercase="gelu"				,				__lowercase=(512, 512, 512, 512, 512, 512, 512)				,				__lowercase=(5, 2, 2, 2, 2, 2, 2)				,				__lowercase=(10, 3, 3, 3, 3, 2, 2)				,				__lowercase=False				,				__lowercase=128				,				__lowercase=16				,				__lowercase=320				,				__lowercase=800				,				__lowercase=False				,				__lowercase=True				,				__lowercase=0.05				,				__lowercase=10				,				__lowercase=2				,				__lowercase=0.0				,				__lowercase=10				,				__lowercase=320				,				__lowercase=2				,				__lowercase=0.1				,				__lowercase=100				,				__lowercase=256				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase="mean"				,				__lowercase=False				,				__lowercase=False				,				__lowercase=256				,				__lowercase=(512, 512, 512, 512, 1_500)				,				__lowercase=(5, 3, 3, 1, 1)				,				__lowercase=(1, 2, 3, 1, 1)				,				__lowercase=512				,				__lowercase=80				,				__lowercase=0				,				__lowercase=1				,				__lowercase=2				,				__lowercase=False				,				__lowercase=3				,				__lowercase=2				,				__lowercase=3				,				__lowercase=None				,				**__lowercase				,				) ->			Dict:
        super().__init__(**__lowercase				,				pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase)
        __UpperCamelCase  :Any    						=	hidden_size
        __UpperCamelCase  :Tuple    						=	feat_extract_norm
        __UpperCamelCase  :List[str]    						=	feat_extract_activation
        __UpperCamelCase  :int    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	conv_bias
        __UpperCamelCase  :Tuple    						=	num_buckets
        __UpperCamelCase  :Optional[int]    						=	max_bucket_distance
        __UpperCamelCase  :Union[str, Any]    						=	num_conv_pos_embeddings
        __UpperCamelCase  :Optional[Any]    						=	num_conv_pos_embedding_groups
        __UpperCamelCase  :List[Any]    						=	len(self.conv_dim)
        __UpperCamelCase  :Tuple    						=	num_hidden_layers
        __UpperCamelCase  :str    						=	intermediate_size
        __UpperCamelCase  :Union[str, Any]    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	num_attention_heads
        __UpperCamelCase  :str    						=	hidden_dropout
        __UpperCamelCase  :int    						=	attention_dropout
        __UpperCamelCase  :Optional[int]    						=	activation_dropout
        __UpperCamelCase  :str    						=	feat_proj_dropout
        __UpperCamelCase  :List[Any]    						=	final_dropout
        __UpperCamelCase  :int    						=	layerdrop
        __UpperCamelCase  :List[Any]    						=	layer_norm_eps
        __UpperCamelCase  :Optional[int]    						=	initializer_range
        __UpperCamelCase  :Any    						=	num_ctc_classes
        __UpperCamelCase  :Optional[int]    						=	vocab_size
        __UpperCamelCase  :List[Any]    						=	do_stable_layer_norm
        __UpperCamelCase  :str    						=	use_weighted_layer_sum
        __UpperCamelCase  :Any    						=	classifier_proj_size

        if (
            (len(self.conv_stride) != self.num_feat_extract_layers)
            or (len(self.conv_kernel) != self.num_feat_extract_layers)
            or (len(self.conv_dim) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __UpperCamelCase  :Union[str, Any]    						=	apply_spec_augment
        __UpperCamelCase  :Optional[Any]    						=	mask_time_prob
        __UpperCamelCase  :Union[str, Any]    						=	mask_time_length
        __UpperCamelCase  :Optional[int]    						=	mask_time_min_masks
        __UpperCamelCase  :str    						=	mask_feature_prob
        __UpperCamelCase  :Tuple    						=	mask_feature_length

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase  :Optional[Any]    						=	num_codevectors_per_group
        __UpperCamelCase  :List[Any]    						=	num_codevector_groups
        __UpperCamelCase  :str    						=	contrastive_logits_temperature
        __UpperCamelCase  :Tuple    						=	num_negatives
        __UpperCamelCase  :Any    						=	codevector_dim
        __UpperCamelCase  :Union[str, Any]    						=	proj_codevector_dim
        __UpperCamelCase  :Tuple    						=	diversity_loss_weight

        # ctc loss
        __UpperCamelCase  :int    						=	ctc_loss_reduction
        __UpperCamelCase  :Any    						=	ctc_zero_infinity

        # adapter
        __UpperCamelCase  :List[Any]    						=	add_adapter
        __UpperCamelCase  :Dict    						=	adapter_kernel_size
        __UpperCamelCase  :Any    						=	adapter_stride
        __UpperCamelCase  :Optional[int]    						=	num_adapter_layers
        __UpperCamelCase  :Union[str, Any]    						=	output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        __UpperCamelCase  :int    						=	classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :List[str]    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	xvector_output_dim







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        return functools.reduce(operator.mul				,				self.conv_stride				,				1)





