




from typing import Mapping

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


__SCREAMING_SNAKE_CASE      :				Dict				      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      :				Optional[Any]				      ={
    """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     (						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Union[str, Any]       ="longt5"
     _snake_case:		str       =["past_key_values"]
     _snake_case:		Optional[int]       ={"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




     def __init__(      self					:				Optional[int]		,					A__					:				Any=3_2_1_2_8		,					A__					:				List[Any]=5_1_2		,					A__					:				Optional[int]=6_4		,					A__					:				Optional[int]=2_0_4_8		,					A__					:				Any=6		,					A__					:				str=None		,					A__					:				str=8		,					A__					:				Union[str, Any]=1_2_7		,					A__					:				str=1_6		,					A__					:				Dict=3_2		,					A__					:				List[Any]=1_2_8		,					A__					:				Dict=0.1		,					A__					:				int=1E-6		,					A__					:				Optional[int]=1.0		,					A__					:				List[str]="relu"		,					A__					:				Any=True		,					A__					:				List[Any]="local"		,					A__					:				List[Any]=True		,					A__					:				Tuple=0		,					A__					:				str=1		,					**A__					:				Optional[int]		,					)			->    List[Any]:
           lowerCamelCase__ 					=			vocab_size
           lowerCamelCase__ 					=			d_model
           lowerCamelCase__ 					=			d_kv
           lowerCamelCase__ 					=			d_ff
           lowerCamelCase__ 					=			num_layers
           # default = symmetry
           lowerCamelCase__ 					=			num_decoder_layers if num_decoder_layers is not None else self.num_layers
           lowerCamelCase__ 					=			num_heads
           lowerCamelCase__ 					=			local_radius
           lowerCamelCase__ 					=			global_block_size
           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__ 					=			encoder_attention_type
           lowerCamelCase__ 					=			use_cache

           lowerCamelCase__ 					=			self.feed_forward_proj.split('-'     )
           lowerCamelCase__ 					=			act_info[-1]
           lowerCamelCase__ 					=			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":
                 lowerCamelCase__ 					=			'gelu_new'

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






class      A     (						_lowerCAmelCase						):
     """simple docstring"""

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

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

           return common_inputs




     @property
     def 	__snake_case				(      self					:				Tuple     )			->    int:
           return 1_3





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE      :				int				      ={"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =[
          """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NatForImageClassification""",
          """NatModel""",
          """NatPreTrainedModel""",
          """NatBackbone""",
      ]

if TYPE_CHECKING:
      from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nat import (
                NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                NatBackbone,
                NatForImageClassification,
                NatModel,
                NatPreTrainedModel,
            )

else:
      import sys

      __SCREAMING_SNAKE_CASE      :				Any				      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)