

"""simple docstring"""




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


SCREAMING_SNAKE_CASE_     :			int	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			int	  =			{
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}


class 		_A       (			__a					):
      __a								=	'switch_transformers'
      __a								=	['past_key_values']
      __a								=	{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=32128    ,						SCREAMING_SNAKE_CASE__=768    ,						SCREAMING_SNAKE_CASE__=64    ,						SCREAMING_SNAKE_CASE__=2048    ,						SCREAMING_SNAKE_CASE__=64    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=3    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=3    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=8    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=0.01    ,						SCREAMING_SNAKE_CASE__="float32"    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=32    ,						SCREAMING_SNAKE_CASE__=128    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=1e-6    ,						SCREAMING_SNAKE_CASE__=0.0_01    ,						SCREAMING_SNAKE_CASE__=0.0_01    ,						SCREAMING_SNAKE_CASE__=1.0    ,						SCREAMING_SNAKE_CASE__="relu"    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=0    ,						SCREAMING_SNAKE_CASE__=1    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					str:
         lowerCamelCase__						=			vocab_size
         lowerCamelCase__						=			d_model
         lowerCamelCase__						=			d_kv
         lowerCamelCase__						=			d_ff

         lowerCamelCase__						=			num_sparse_encoder_layers

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

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

         lowerCamelCase__						=			router_ignore_padding_tokens
         lowerCamelCase__						=			relative_attention_num_buckets
         lowerCamelCase__						=			relative_attention_max_distance

         lowerCamelCase__						=			dropout_rate
         lowerCamelCase__						=			layer_norm_epsilon
         lowerCamelCase__						=			initializer_factor
         lowerCamelCase__						=			feed_forward_proj
         lowerCamelCase__						=			use_cache
         lowerCamelCase__						=			add_router_probs

         lowerCamelCase__						=			router_z_loss_coef
         lowerCamelCase__						=			router_aux_loss_coef

         lowerCamelCase__						=			self.feed_forward_proj.split("-"		)
         lowerCamelCase__						=			act_info[-1]
         lowerCamelCase__						=			act_info[0] == "gated"

         if len(SCREAMING_SNAKE_CASE__		) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE__		) > 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":
            lowerCamelCase__						=			"gelu_new"

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







"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_     :			List[str]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




