from typing import Mapping

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


UpperCamelCase    				=		logging.get_logger(__name__)

UpperCamelCase    				=		{
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}



class     snake_case_			(						__A			):
   __A      :						Optional[int]   =							"longt5"
   __A      :						int   =							["past_key_values"]
   __A      :						Optional[int]   =							{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






   def __init__( self							:	Tuple      ,		lowercase_							:	str=3_21_28      ,		lowercase_							:	str=5_12      ,		lowercase_							:	str=64      ,		lowercase_							:	Dict=20_48      ,		lowercase_							:	Tuple=6      ,		lowercase_							:	int=None      ,		lowercase_							:	Union[str, Any]=8      ,		lowercase_							:	List[str]=1_27      ,		lowercase_							:	Dict=16      ,		lowercase_							:	List[Any]=32      ,		lowercase_							:	List[Any]=1_28      ,		lowercase_							:	int=0.1      ,		lowercase_							:	str=1E-6      ,		lowercase_							:	Optional[int]=1.0      ,		lowercase_							:	Optional[int]="relu"      ,		lowercase_							:	str=True      ,		lowercase_							:	Any="local"      ,		lowercase_							:	Any=True      ,		lowercase_							:	int=0      ,		lowercase_							:	List[Any]=1      ,		**lowercase_							:	Any      ,		)   ->		Any:
       lowercase__					:  List[str]          =							vocab_size
       lowercase__					:  Dict          =							d_model
       lowercase__					:  Optional[Any]          =							d_kv
       lowercase__					:  int          =							d_ff
       lowercase__					:  List[Any]          =							num_layers
       # default = symmetry
       lowercase__					:  List[Any]          =							num_decoder_layers if num_decoder_layers is not None else self.num_layers
       lowercase__					:  List[str]          =							num_heads
       lowercase__					:  List[Any]          =							local_radius
       lowercase__					:  Union[str, Any]          =							global_block_size
       lowercase__					:  Optional[int]          =							relative_attention_num_buckets
       lowercase__					:  Dict          =							relative_attention_max_distance
       lowercase__					:  List[str]          =							dropout_rate
       lowercase__					:  Union[str, Any]          =							layer_norm_epsilon
       lowercase__					:  List[str]          =							initializer_factor
       lowercase__					:  str          =							feed_forward_proj
       lowercase__					:  List[Any]          =							encoder_attention_type
       lowercase__					:  int          =							use_cache

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

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



class     snake_case_			(						__A			):






   @property
   def  __UpperCamelCase	( self							:	List[str]    )   ->		Mapping[str, Mapping[int, str]]:
       lowercase__					:  Optional[int]          =							{
           "input_ids": {0: "batch", 1: "encoder_sequence"},
           "attention_mask": {0: "batch", 1: "encoder_sequence"},
       }
       if self.use_past:
           lowercase__					:  Optional[int]          =							"past_encoder_sequence + sequence"
           lowercase__					:  List[Any]          =							{0: "batch"}
           lowercase__					:  int          =							{0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
           lowercase__					:  Any          =							{0: "batch", 1: "decoder_sequence"}
           lowercase__					:  List[Any]          =							{0: "batch", 1: "decoder_sequence"}

       if self.use_past:
           self.fill_with_past_key_values_(lowercase_      ,		direction="inputs"    )

       return common_inputs







   @property
   def  __UpperCamelCase	( self							:	Dict    )   ->		int:
       return 13




from transformers import PretrainedConfig



class     snake_case_			(						__A			):
   __A      :						List[Any]   =							"custom"






   def __init__( self							:	Any      ,		lowercase_							:	Optional[int]=1      ,		**lowercase_							:	Any    )   ->		int:
       lowercase__					:  List[Any]          =							attribute
       super().__init__(**lowercase_    )



class     snake_case_			(						__A			):
   __A      :						Optional[int]   =							"custom"






   def __init__( self							:	Tuple      ,		lowercase_							:	str=1      ,		**lowercase_							:	List[Any]    )   ->		Dict:
       lowercase__					:  Union[str, Any]          =							attribute



