




from typing import Mapping

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


UpperCamelCase__ :   Dict						=							logging.get_logger(__name__)

UpperCamelCase__ :   Any						=							{
    '''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 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       str						=     "umt5"
				__a     :       str						=     ["past_key_values"]






				def __init__(			self      ,snake_case__=250112      ,snake_case__=512      ,snake_case__=64      ,snake_case__=1024      ,snake_case__=8      ,snake_case__=None      ,snake_case__=6      ,snake_case__=32      ,snake_case__=128      ,snake_case__=0.1      ,snake_case__=1E-6      ,snake_case__=1.0      ,snake_case__="gated-gelu"      ,snake_case__=True      ,snake_case__=True      ,snake_case__="T5Tokenizer"      ,snake_case__=True      ,snake_case__=0      ,snake_case__=1      ,snake_case__=0      ,**snake_case__      ,):
							super().__init__(
							    is_encoder_decoder=snake_case__      ,tokenizer_class=snake_case__      ,tie_word_embeddings=snake_case__      ,pad_token_id=snake_case__      ,eos_token_id=snake_case__      ,decoder_start_token_id=snake_case__      ,**snake_case__      ,)
							SCREAMING_SNAKE_CASE_   : Tuple					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : List[str]					  =      d_model
							SCREAMING_SNAKE_CASE_   : Any					  =      d_kv
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      d_ff
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      num_layers
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							SCREAMING_SNAKE_CASE_   : str					  =      num_heads
							SCREAMING_SNAKE_CASE_   : Dict					  =      relative_attention_num_buckets
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      relative_attention_max_distance
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      dropout_rate
							SCREAMING_SNAKE_CASE_   : List[str]					  =      layer_norm_epsilon
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      initializer_factor
							SCREAMING_SNAKE_CASE_   : Tuple					  =      feed_forward_proj
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      use_cache

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

							if len(snake_case__ ) > 1 and act_info[0] != "gated" or len(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\'' )

							if feed_forward_proj == "gated-gelu":
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      'gelu_new'






				@property
				def 		snake_case	(			self ):
							return self.d_model






				@property
				def 		snake_case	(			self ):
							return self.num_heads






				@property
				def 		snake_case	(			self ):
							return self.num_layers







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      {
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      'past_encoder_sequence + sequence'
										SCREAMING_SNAKE_CASE_   : str					  =      {0: 'batch'}
										SCREAMING_SNAKE_CASE_   : Any					  =      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {0: 'batch', 1: 'decoder_sequence'}
										SCREAMING_SNAKE_CASE_   : Any					  =      {0: 'batch', 1: 'decoder_sequence'}

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

							return common_inputs






				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def 		snake_case	(			self ):
							return 13






				@property
				def 		snake_case	(			self ):
							return 5E-4






'''simple docstring'''




from ..utils import DummyObject, requires_backends



class 							lowercase_						(		metaclass=a			):

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= ["keras_nlp"]
  def __init__(  self ,			*a_ ,			**a_  )				->					Union[str, Any]:


    """simple docstring"""
    requires_backends(self ,			['keras_nlp']  )


