

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_a		       =   logging.get_logger(__name__)

_a		       =   {
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class 							A_ (		__snake_case					):
     _lowercase			:     Optional[int]								=	"""umt5"""
     _lowercase			:     Optional[int]								=	["""past_key_values"""]


     def __init__(							self			:		Optional[int]			,  UpperCAmelCase			:		Optional[int]=2_5_0_1_1_2			,  UpperCAmelCase			:		str=5_1_2			,  UpperCAmelCase			:		List[str]=6_4			,  UpperCAmelCase			:		List[Any]=1_0_2_4			,  UpperCAmelCase			:		Union[str, Any]=8			,  UpperCAmelCase			:		Union[str, Any]=None			,  UpperCAmelCase			:		int=6			,  UpperCAmelCase			:		Any=3_2			,  UpperCAmelCase			:		int=1_2_8			,  UpperCAmelCase			:		Tuple=0.1			,  UpperCAmelCase			:		Optional[int]=1E-6			,  UpperCAmelCase			:		int=1.0			,  UpperCAmelCase			:		str="gated-gelu"			,  UpperCAmelCase			:		List[str]=True			,  UpperCAmelCase			:		Optional[Any]=True			,  UpperCAmelCase			:		Optional[Any]="T5Tokenizer"			,  UpperCAmelCase			:		Any=True			,  UpperCAmelCase			:		List[str]=0			,  UpperCAmelCase			:		Dict=1			,  UpperCAmelCase			:		Union[str, Any]=0			,  **UpperCAmelCase			:		str			,  )							->	Union[str, Any]:
      super().__init__(
          is_encoder_decoder=UpperCamelCase__			,  tokenizer_class=UpperCamelCase__			,  tie_word_embeddings=UpperCamelCase__			,  pad_token_id=UpperCamelCase__			,  eos_token_id=UpperCamelCase__			,  decoder_start_token_id=UpperCamelCase__			,  **UpperCamelCase__			,  )
      __lowerCAmelCase:   List[str]   	=      vocab_size
      __lowerCAmelCase:   List[Any]   	=      d_model
      __lowerCAmelCase:   Union[str, Any]   	=      d_kv
      __lowerCAmelCase:   Union[str, Any]   	=      d_ff
      __lowerCAmelCase:   int   	=      num_layers
      __lowerCAmelCase:   List[str]   	=      (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      __lowerCAmelCase:   Dict   	=      num_heads
      __lowerCAmelCase:   str   	=      relative_attention_num_buckets
      __lowerCAmelCase:   int   	=      relative_attention_max_distance
      __lowerCAmelCase:   List[str]   	=      dropout_rate
      __lowerCAmelCase:   Optional[Any]   	=      layer_norm_epsilon
      __lowerCAmelCase:   int   	=      initializer_factor
      __lowerCAmelCase:   Any   	=      feed_forward_proj
      __lowerCAmelCase:   Dict   	=      use_cache

      __lowerCAmelCase:   List[str]   	=      self.feed_forward_proj.split('-'					)
      __lowerCAmelCase:   Optional[Any]   	=      act_info[-1]
      __lowerCAmelCase:   Tuple   	=      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\''					)

      if feed_forward_proj == "gated-gelu":
       __lowerCAmelCase:   Optional[Any]   	=      "gelu_new"


     @property
     def   UpperCAmelCase  (							self			:		Any					)							->	Any:
      return self.d_model


     @property
     def   UpperCAmelCase  (							self			:		int					)							->	Tuple:
      return self.num_heads


     @property
     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	Dict:
      return self.num_layers







class 							A_ (		__snake_case					):


     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def   UpperCAmelCase  (							self			:		Any					)							->	Mapping[str, Mapping[int, str]]:
      __lowerCAmelCase:   Dict   	=      {
          "input_ids": {0: "batch", 1: "encoder_sequence"},
          "attention_mask": {0: "batch", 1: "encoder_sequence"},
      }
      if self.use_past:
       __lowerCAmelCase:   int   	=      "past_encoder_sequence + sequence"
       __lowerCAmelCase:   Optional[int]   	=      {0: "batch"}
       __lowerCAmelCase:   Optional[int]   	=      {0: "batch", 1: "past_decoder_sequence + sequence"}
      else:
       __lowerCAmelCase:   List[str]   	=      {0: "batch", 1: "decoder_sequence"}
       __lowerCAmelCase:   Any   	=      {0: "batch", 1: "decoder_sequence"}

      if self.use_past:
       self.fill_with_past_key_values_(UpperCamelCase__			,  direction='inputs'					)

      return common_inputs


     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def   UpperCAmelCase  (							self			:		Tuple					)							->	int:
      return 1_3


     @property
     def   UpperCAmelCase  (							self			:		Tuple					)							->	float:
      return 5E-4








"""simple docstring"""


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


__A						   =			logging.get_logger(__name__)

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





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Union[str, Any]         =  """switch_transformers"""
				SCREAMING_SNAKE_CASE_   :      Tuple         =  ["""past_key_values"""]
				SCREAMING_SNAKE_CASE_   :      Any         =  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

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



						'''simple docstring'''


						__lowerCAmelCase:       int          =			vocab_size
						__lowerCAmelCase:       str          =			d_model
						__lowerCAmelCase:       str          =			d_kv
						__lowerCAmelCase:       str          =			d_ff

						__lowerCAmelCase:       List[str]          =			num_sparse_encoder_layers

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

						__lowerCAmelCase:       Dict          =			num_heads
						__lowerCAmelCase:       Dict          =			num_experts
						__lowerCAmelCase:       Any          =			expert_capacity
						__lowerCAmelCase:       List[Any]          =			router_bias
						__lowerCAmelCase:       int          =			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:       Dict          =			router_dtype

						__lowerCAmelCase:       Optional[Any]          =			router_ignore_padding_tokens
						__lowerCAmelCase:       Union[str, Any]          =			relative_attention_num_buckets
						__lowerCAmelCase:       str          =			relative_attention_max_distance

						__lowerCAmelCase:       Optional[int]          =			dropout_rate
						__lowerCAmelCase:       Optional[Any]          =			layer_norm_epsilon
						__lowerCAmelCase:       int          =			initializer_factor
						__lowerCAmelCase:       Tuple          =			feed_forward_proj
						__lowerCAmelCase:       int          =			use_cache
						__lowerCAmelCase:       int          =			add_router_probs

						__lowerCAmelCase:       Optional[Any]          =			router_z_loss_coef
						__lowerCAmelCase:       Dict          =			router_aux_loss_coef

						__lowerCAmelCase:       Union[str, Any]          =			self.feed_forward_proj.split("-")
						__lowerCAmelCase:       Tuple          =			act_info[-1]
						__lowerCAmelCase:       str          =			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":
								__lowerCAmelCase:       List[str]          =			"gelu_new"

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


