

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


lowercase_       		=  logging.get_logger(__name__)

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


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'switch_transformers'
    lowerCamelCase			=							['past_key_values']
    lowerCamelCase			=							{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(      self :       Dict,lowercase_ :       Optional[int]=3_2_1_2_8,lowercase_ :       List[Any]=7_6_8,lowercase_ :       Union[str, Any]=6_4,lowercase_ :       Union[str, Any]=2_0_4_8,lowercase_ :       Dict=6_4,lowercase_ :       List[Any]=1_2,lowercase_ :       Optional[int]=3,lowercase_ :       List[Any]=1_2,lowercase_ :       Dict=3,lowercase_ :       Any=1_2,lowercase_ :       Optional[int]=8,lowercase_ :       str=False,lowercase_ :       Dict=0.01,lowercase_ :       Optional[Any]="float32",lowercase_ :       Any=False,lowercase_ :       str=3_2,lowercase_ :       List[Any]=1_2_8,lowercase_ :       int=0.1,lowercase_ :       Union[str, Any]=1E-6,lowercase_ :       Dict=0.001,lowercase_ :       List[Any]=0.001,lowercase_ :       Dict=1.0,lowercase_ :       Optional[int]="relu",lowercase_ :       Dict=True,lowercase_ :       Union[str, Any]=False,lowercase_ :       Union[str, Any]=True,lowercase_ :       List[str]=0,lowercase_ :       int=1,**lowercase_ :       Union[str, Any],)->    Tuple:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   d_model
        A__          =   d_kv
        A__          =   d_ff

        A__          =   num_sparse_encoder_layers

        A__          =   num_layers
        A__          =   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A__          =   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:
            A__          =   self.num_layers // self.num_sparse_encoder_layers
        else:
            A__          =   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:
            A__          =   self.num_decoder_layers // self.num_sparse_decoder_layers
        else:
            A__          =   self.num_decoder_layers  # HACK: this will create 0 sparse layers

        A__          =   num_heads
        A__          =   num_experts
        A__          =   expert_capacity
        A__          =   router_bias
        A__          =   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}'		)
        A__          =   router_dtype

        A__          =   router_ignore_padding_tokens
        A__          =   relative_attention_num_buckets
        A__          =   relative_attention_max_distance

        A__          =   dropout_rate
        A__          =   layer_norm_epsilon
        A__          =   initializer_factor
        A__          =   feed_forward_proj
        A__          =   use_cache
        A__          =   add_router_probs

        A__          =   router_z_loss_coef
        A__          =   router_aux_loss_coef

        A__          =   self.feed_forward_proj.split('-'		)
        A__          =   act_info[-1]
        A__          =   act_info[0] == 'gated'

        if len(lowercase_		) > 1 and act_info[0] != "gated" or len(lowercase_		) > 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":
            A__          =   'gelu_new'

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




from typing import Dict

from .base import GenericTensor, Pipeline


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       int,lowercase_ :       Dict=None,lowercase_ :       Tuple=None,lowercase_ :       List[Any]=None,**lowercase_ :       Any		)->    Optional[Any]:





        '''simple docstring'''







        if tokenize_kwargs is None:
            A__          =   {}

        if truncation is not None:
            if "truncation" in tokenize_kwargs:
                raise ValueError(
                    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'		)
            A__          =   truncation

        A__          =   tokenize_kwargs

        A__          =   {}
        if return_tensors is not None:
            A__          =   return_tensors

        return preprocess_params, {}, postprocess_params


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[Any],**lowercase_ :       Tuple		)->    Dict[str, GenericTensor]:





        '''simple docstring'''







        A__          =   self.framework
        A__          =   self.tokenizer(lowercase_,return_tensors=lowercase_,**lowercase_		)
        return model_inputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        return model_outputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Tuple,lowercase_ :       List[str]=False		)->    Any:





        '''simple docstring'''







        if return_tensors:
            return model_outputs[0]
        if self.framework == "pt":
            return model_outputs[0].tolist()
        elif self.framework == "tf":
            return model_outputs[0].numpy().tolist()


    def __call__(      self :       List[Any],*lowercase_ :       int,**lowercase_ :       Optional[Any]		)->    int:





        '''simple docstring'''







        return super().__call__(*lowercase_,**lowercase_		)

