"""simple docstring"""







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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						'switch_transformers'
   lowercase							=						['past_key_values']
   lowercase							=						{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
   def __init__(    self  ,__UpperCamelCase=3_2128  ,__UpperCamelCase=768  ,__UpperCamelCase=64  ,__UpperCamelCase=2048  ,__UpperCamelCase=64  ,__UpperCamelCase=12  ,__UpperCamelCase=3  ,__UpperCamelCase=12  ,__UpperCamelCase=3  ,__UpperCamelCase=12  ,__UpperCamelCase=8  ,__UpperCamelCase=False  ,__UpperCamelCase=0.01  ,__UpperCamelCase="float32"  ,__UpperCamelCase=False  ,__UpperCamelCase=32  ,__UpperCamelCase=128  ,__UpperCamelCase=0.1  ,__UpperCamelCase=1e-6  ,__UpperCamelCase=0.001  ,__UpperCamelCase=0.001  ,__UpperCamelCase=1.0  ,__UpperCamelCase="relu"  ,__UpperCamelCase=True  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,__UpperCamelCase=0  ,__UpperCamelCase=1  ,**__UpperCamelCase  ,)			->   str:


        '''simple docstring'''


        lowercase_			:       List[str]									=			vocab_size
        lowercase_			:       Optional[Any]									=			d_model
        lowercase_			:       Dict									=			d_kv
        lowercase_			:       Dict									=			d_ff

        lowercase_			:       str									=			num_sparse_encoder_layers

        lowercase_			:       List[str]									=			num_layers
        lowercase_			:       int									=			(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        lowercase_			:       Optional[Any]									=			num_sparse_decoder_layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_encoder_layers > 0:
             lowercase_			:       Tuple									=			self.num_layers // self.num_sparse_encoder_layers
        else:
             lowercase_			:       Union[str, Any]									=			self.num_layers  # HACK: this will create 0 sparse layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_decoder_layers > 0:
             lowercase_			:       Union[str, Any]									=			self.num_decoder_layers // self.num_sparse_decoder_layers
        else:
             lowercase_			:       List[Any]									=			self.num_decoder_layers  # HACK: this will create 0 sparse layers

        lowercase_			:       List[Any]									=			num_heads
        lowercase_			:       Dict									=			num_experts
        lowercase_			:       List[str]									=			expert_capacity
        lowercase_			:       Optional[Any]									=			router_bias
        lowercase_			:       Optional[Any]									=			router_jitter_noise
        if router_dtype not in ["float32", "float16", "bfloat16"]:
             raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''    )
        lowercase_			:       Optional[Any]									=			router_dtype

        lowercase_			:       Union[str, Any]									=			router_ignore_padding_tokens
        lowercase_			:       Any									=			relative_attention_num_buckets
        lowercase_			:       List[Any]									=			relative_attention_max_distance

        lowercase_			:       str									=			dropout_rate
        lowercase_			:       Any									=			layer_norm_epsilon
        lowercase_			:       Tuple									=			initializer_factor
        lowercase_			:       str									=			feed_forward_proj
        lowercase_			:       List[Any]									=			use_cache
        lowercase_			:       str									=			add_router_probs

        lowercase_			:       Tuple									=			router_z_loss_coef
        lowercase_			:       int									=			router_aux_loss_coef

        lowercase_			:       List[str]									=			self.feed_forward_proj.split('-'    )
        lowercase_			:       List[str]									=			act_info[-1]
        lowercase_			:       Optional[Any]									=			act_info[0] == 'gated'

        if len(__UpperCamelCase    ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase    ) > 2:
             raise ValueError(
                 f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                 'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                 '\'gated-gelu\' or \'relu\''    )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
             lowercase_			:       Union[str, Any]									=			'gelu_new'

        super().__init__(
            pad_token_id=__UpperCamelCase  ,eos_token_id=__UpperCamelCase  ,is_encoder_decoder=__UpperCamelCase  ,**__UpperCamelCase  ,)


"""simple docstring"""







import functools
import operator

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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								={
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						'wav2vec2'
   def __init__(    self  ,__UpperCamelCase=32  ,__UpperCamelCase=768  ,__UpperCamelCase=12  ,__UpperCamelCase=12  ,__UpperCamelCase=3072  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.0  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.1  ,__UpperCamelCase=0.02  ,__UpperCamelCase=1e-5  ,__UpperCamelCase="group"  ,__UpperCamelCase="gelu"  ,__UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)  ,__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)  ,__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)  ,__UpperCamelCase=False  ,__UpperCamelCase=128  ,__UpperCamelCase=16  ,__UpperCamelCase=False  ,__UpperCamelCase=True  ,__UpperCamelCase=0.05  ,__UpperCamelCase=10  ,__UpperCamelCase=2  ,__UpperCamelCase=0.0  ,__UpperCamelCase=10  ,__UpperCamelCase=0  ,__UpperCamelCase=320  ,__UpperCamelCase=2  ,__UpperCamelCase=0.1  ,__UpperCamelCase=100  ,__UpperCamelCase=256  ,__UpperCamelCase=256  ,__UpperCamelCase=0.1  ,__UpperCamelCase="sum"  ,__UpperCamelCase=False  ,__UpperCamelCase=False  ,__UpperCamelCase=256  ,__UpperCamelCase=(512, 512, 512, 512, 1500)  ,__UpperCamelCase=(5, 3, 3, 1, 1)  ,__UpperCamelCase=(1, 2, 3, 1, 1)  ,__UpperCamelCase=512  ,__UpperCamelCase=0  ,__UpperCamelCase=1  ,__UpperCamelCase=2  ,__UpperCamelCase=False  ,__UpperCamelCase=3  ,__UpperCamelCase=2  ,__UpperCamelCase=3  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,**__UpperCamelCase  ,)			->   Optional[Any]:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase  ,pad_token_id=__UpperCamelCase  ,bos_token_id=__UpperCamelCase  ,eos_token_id=__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			hidden_size
        lowercase_			:       Tuple									=			feat_extract_norm
        lowercase_			:       Dict									=			feat_extract_activation
        lowercase_			:       List[str]									=			list(__UpperCamelCase    )
        lowercase_			:       str									=			list(__UpperCamelCase    )
        lowercase_			:       Dict									=			list(__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			conv_bias
        lowercase_			:       Dict									=			num_conv_pos_embeddings
        lowercase_			:       List[str]									=			num_conv_pos_embedding_groups
        lowercase_			:       Optional[Any]									=			len(self.conv_dim    )
        lowercase_			:       Any									=			num_hidden_layers
        lowercase_			:       List[Any]									=			intermediate_size
        lowercase_			:       List[Any]									=			hidden_act
        lowercase_			:       Optional[int]									=			num_attention_heads
        lowercase_			:       int									=			hidden_dropout
        lowercase_			:       Dict									=			attention_dropout
        lowercase_			:       Union[str, Any]									=			activation_dropout
        lowercase_			:       Tuple									=			feat_proj_dropout
        lowercase_			:       List[str]									=			final_dropout
        lowercase_			:       Union[str, Any]									=			layerdrop
        lowercase_			:       List[str]									=			layer_norm_eps
        lowercase_			:       Optional[int]									=			initializer_range
        lowercase_			:       List[Any]									=			vocab_size
        lowercase_			:       Optional[int]									=			do_stable_layer_norm
        lowercase_			:       Union[str, Any]									=			use_weighted_layer_sum

        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
        lowercase_			:       Dict									=			apply_spec_augment
        lowercase_			:       Optional[int]									=			mask_time_prob
        lowercase_			:       Union[str, Any]									=			mask_time_length
        lowercase_			:       List[str]									=			mask_time_min_masks
        lowercase_			:       List[str]									=			mask_feature_prob
        lowercase_			:       Any									=			mask_feature_length
        lowercase_			:       List[Any]									=			mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        lowercase_			:       List[Any]									=			num_codevectors_per_group
        lowercase_			:       Optional[int]									=			num_codevector_groups
        lowercase_			:       Dict									=			contrastive_logits_temperature
        lowercase_			:       int									=			feat_quantizer_dropout
        lowercase_			:       Optional[int]									=			num_negatives
        lowercase_			:       str									=			codevector_dim
        lowercase_			:       str									=			proj_codevector_dim
        lowercase_			:       Optional[Any]									=			diversity_loss_weight

        # ctc loss
        lowercase_			:       Tuple									=			ctc_loss_reduction
        lowercase_			:       int									=			ctc_zero_infinity

        # adapter
        lowercase_			:       int									=			add_adapter
        lowercase_			:       Dict									=			adapter_kernel_size
        lowercase_			:       List[str]									=			adapter_stride
        lowercase_			:       Dict									=			num_adapter_layers
        lowercase_			:       Dict									=			output_hidden_size or hidden_size
        lowercase_			:       Optional[Any]									=			adapter_attn_dim

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

        # XVector-specific parameters. Feel free to ignore for other classes.
        lowercase_			:       Any									=			list(__UpperCamelCase    )
        lowercase_			:       str									=			list(__UpperCamelCase    )
        lowercase_			:       Any									=			list(__UpperCamelCase    )
        lowercase_			:       Tuple									=			xvector_output_dim







   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        return functools.reduce(operator.mul  ,self.conv_stride  ,1    )

