





'''simple docstring'''






from typing import Mapping

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


A__							:  List[str]    =logging.get_logger(__name__)

A__							:  Any    ={
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}




class 					UpperCAmelCase	(     a_  ):
      _lowercase:     List[Any]			=  '''t5'''
      _lowercase:     Optional[int]			=  ['''past_key_values''']
      _lowercase:     List[str]			=  {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




      def __init__(     self     :					Any     ,     __snake_case     :					Any=3_21_28     ,     __snake_case     :					List[str]=5_12     ,     __snake_case     :					Dict=64     ,     __snake_case     :					str=20_48     ,     __snake_case     :					int=6     ,     __snake_case     :					Dict=None     ,     __snake_case     :					List[str]=8     ,     __snake_case     :					List[Any]=32     ,     __snake_case     :					Any=1_28     ,     __snake_case     :					Optional[int]=0.1     ,     __snake_case     :					Optional[int]=1E-6     ,     __snake_case     :					Union[str, Any]=1.0     ,     __snake_case     :					Any="relu"     ,     __snake_case     :					Dict=True     ,     __snake_case     :					Optional[Any]=True     ,     __snake_case     :					Optional[int]=0     ,     __snake_case     :					Any=1     ,     **__snake_case     :					Optional[int]     ,     )							-> Dict:
            _lowerCAmelCase					=				vocab_size
            _lowerCAmelCase					=				d_model
            _lowerCAmelCase					=				d_kv
            _lowerCAmelCase					=				d_ff
            _lowerCAmelCase					=				num_layers
            _lowerCAmelCase					=				(
                num_decoder_layers if num_decoder_layers is not None else self.num_layers
            )  # default = symmetry
            _lowerCAmelCase					=				num_heads
            _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					=				use_cache

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

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

            # for backwards compatibility
            if feed_forward_proj == "gated-gelu":
                  _lowerCAmelCase					=				"""gelu_new"""

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




class 					UpperCAmelCase	(     a_  ):




      @property
      def 							lowercase__       (     self     :					Any    )							-> Optional[int]:
            _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_(__SCREAMING_SNAKE_CASE     ,     direction="""inputs"""    )

            return common_inputs




      @property
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Optional[int]:
            return 13







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

  _SCREAMING_SNAKE_CASE     					=      {
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	initializer_range
   # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   __a							=	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


