'''simple docstring'''






from typing import Mapping

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


_UpperCAmelCase						:    Any     					=					logging.get_logger(__name__)

_UpperCAmelCase						:    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 lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      "longt5"
   __lowerCAmelCase    						=      ["past_key_values"]
   __lowerCAmelCase    						=      {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
   def __init__(			self			: Optional[Any],   UpperCamelCase__			: Dict=3_21_28,   UpperCamelCase__			: Tuple=5_12,   UpperCamelCase__			: Any=64,   UpperCamelCase__			: Optional[Any]=20_48,   UpperCamelCase__			: str=6,   UpperCamelCase__			: Dict=None,   UpperCamelCase__			: Optional[Any]=8,   UpperCamelCase__			: str=1_27,   UpperCamelCase__			: Any=16,   UpperCamelCase__			: Dict=32,   UpperCamelCase__			: List[str]=1_28,   UpperCamelCase__			: str=0.1,   UpperCamelCase__			: str=1e-6,   UpperCamelCase__			: Dict=1.0,   UpperCamelCase__			: int="relu",   UpperCamelCase__			: str=True,   UpperCamelCase__			: Tuple="local",   UpperCamelCase__			: Union[str, Any]=True,   UpperCamelCase__			: Dict=0,   UpperCamelCase__			: Any=1,   **UpperCamelCase__			: Any,   )     -> str:
        _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(UpperCamelCase__    ) > 1 and act_info[0] != "gated" or len(UpperCamelCase__    ) > 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=UpperCamelCase__,   eos_token_id=UpperCamelCase__,   is_encoder_decoder=UpperCamelCase__,   **UpperCamelCase__,   )







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   @property
   def   __UpperCAmelCase      (			self			: Dict    )     -> Mapping[str, Mapping[int, str]]:
        _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_(UpperCamelCase__,   direction='inputs'    )

        return common_inputs

   @property
   def   __UpperCAmelCase      (			self			: Tuple    )     -> int:
        return 13




'''simple docstring'''






from sklearn.neural_network import MLPClassifier

_UpperCAmelCase						:    str     					=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
_UpperCAmelCase						:    Any     					=					[0, 1, 0, 0]


_UpperCAmelCase						:    List[Any]     					=					MLPClassifier(
    solver='''lbfgs''', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


_UpperCAmelCase						:    Optional[Any]     					=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
_UpperCAmelCase						:    Tuple     					=					clf.predict(test)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]							):
     return list(__snake_case							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



