'''simple docstring'''

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


__a:			List[Any]	       =       logging.get_logger(__name__)

__a:			Optional[int]	       =       {
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "switch_transformers"
       SCREAMING_SNAKE_CASE 		=   ["past_key_values"]
       SCREAMING_SNAKE_CASE 		=   {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

       def __init__(						self     ,       __lowerCAmelCase=32128     ,       __lowerCAmelCase=768     ,       __lowerCAmelCase=64     ,       __lowerCAmelCase=2048     ,       __lowerCAmelCase=64     ,       __lowerCAmelCase=12     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=12     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=12     ,       __lowerCAmelCase=8     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=0.0_1     ,       __lowerCAmelCase="float32"     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=32     ,       __lowerCAmelCase=128     ,       __lowerCAmelCase=0.1     ,       __lowerCAmelCase=1E-6     ,       __lowerCAmelCase=0.0_0_1     ,       __lowerCAmelCase=0.0_0_1     ,       __lowerCAmelCase=1.0     ,       __lowerCAmelCase="relu"     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=0     ,       __lowerCAmelCase=1     ,       **__lowerCAmelCase     ,       )  ->  int:
        lowercase__    :   Tuple						= vocab_size
        lowercase__    :   Any						= d_model
        lowercase__    :   Dict						= d_kv
        lowercase__    :   Any						= d_ff

        lowercase__    :   Tuple						= num_sparse_encoder_layers

        lowercase__    :   Optional[Any]						= num_layers
        lowercase__    :   List[str]						= (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        lowercase__    :   Tuple						= 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__    :   List[Any]						= self.num_layers // self.num_sparse_encoder_layers
        else:
         lowercase__    :   Dict						= 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__    :   int						= self.num_decoder_layers  # HACK: this will create 0 sparse layers

        lowercase__    :   int						= num_heads
        lowercase__    :   Optional[int]						= num_experts
        lowercase__    :   Optional[Any]						= expert_capacity
        lowercase__    :   Dict						= router_bias
        lowercase__    :   str						= 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__    :   List[Any]						= router_dtype

        lowercase__    :   Dict						= router_ignore_padding_tokens
        lowercase__    :   Union[str, Any]						= relative_attention_num_buckets
        lowercase__    :   Tuple						= relative_attention_max_distance

        lowercase__    :   Tuple						= dropout_rate
        lowercase__    :   Optional[int]						= layer_norm_epsilon
        lowercase__    :   Dict						= initializer_factor
        lowercase__    :   str						= feed_forward_proj
        lowercase__    :   Any						= use_cache
        lowercase__    :   Tuple						= add_router_probs

        lowercase__    :   Dict						= router_z_loss_coef
        lowercase__    :   List[str]						= router_aux_loss_coef

        lowercase__    :   int						= self.feed_forward_proj.split('''-'''			)
        lowercase__    :   str						= act_info[-1]
        lowercase__    :   Tuple						= act_info[0] == '''gated'''

        if len(__lowerCAmelCase			) > 1 and act_info[0] != "gated" or len(__lowerCAmelCase			) > 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__    :   List[Any]						= '''gelu_new'''

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


'''simple docstring'''

import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,					[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,					['''default''', 0, 100 * 2**20, 900 * 2**20]					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config		,					'''IN_MEMORY_MAX_SIZE'''		,					UpperCAmelCase					)
 lowercase__    :   List[Any]						= datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  lowercase__    :   str						= dataset_size < in_memory_max_size
 else:
  lowercase__    :   Optional[int]						= False
 lowercase__    :   Optional[Any]						= is_small_dataset(UpperCAmelCase					)
 assert result == expected

