





from typing import Mapping

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


lowercase__   :    Optional[int] 					=      logging.get_logger(__name__)

lowercase__   :    List[str] 					=      {
    "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     a__ (      UpperCamelCase__   ):
     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						,	A=32128						,	A=512						,	A=64						,	A=2048						,	A=6						,	A=None						,	A=8						,	A=127						,	A=16						,	A=32						,	A=128						,	A=0.1						,	A=1e-6						,	A=1.0						,	A="relu"						,	A=True						,	A="local"						,	A=True						,	A=0						,	A=1						,	**A						,	)						->					Optional[int]:



          '''simple docstring'''



          a											=   vocab_size
          a											=   d_model
          a											=   d_kv
          a											=   d_ff
          a											=   num_layers
          # default = symmetry
          a											=   num_decoder_layers if num_decoder_layers is not None else self.num_layers
          a											=   num_heads
          a											=   local_radius
          a											=   global_block_size
          a											=   relative_attention_num_buckets
          a											=   relative_attention_max_distance
          a											=   dropout_rate
          a											=   layer_norm_epsilon
          a											=   initializer_factor
          a											=   feed_forward_proj
          a											=   encoder_attention_type
          a											=   use_cache

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

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

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







class     a__ (      UpperCamelCase__   ):







     @property
     def     lowerCAmelCase_       (			self				)						->					Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          a											=   {
              "input_ids": {0: "batch", 1: "encoder_sequence"},
              "attention_mask": {0: "batch", 1: "encoder_sequence"},
          }
          if self.use_past:
               a											=   "past_encoder_sequence + sequence"
               a											=   {0: "batch"}
               a											=   {0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
               a											=   {0: "batch", 1: "decoder_sequence"}
               a											=   {0: "batch", 1: "decoder_sequence"}

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

          return common_inputs







     @property
     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          return 13







import safs




class     a__ (      safs.SaFileSystem   ):
     pass

