
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =			logging.get_logger(__name__)

_A   =			{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Tuple									=		"informer"
     UpperCAmelCase__    :							Union[str, Any]									=		{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
         "num_hidden_layers": "encoder_layers",
     }





     def __init__(self				:   List[str]    ,   _A				:   Optional[int] = None    ,   _A				:   Optional[int] = None    ,   _A				:   str = "student_t"    ,   _A				:   str = "nll"    ,   _A				:   int = 1    ,   _A				:   List[int] = None    ,   _A				:   Optional[Union[str, bool]] = "mean"    ,   _A				:   int = 0    ,   _A				:   int = 0    ,   _A				:   int = 0    ,   _A				:   int = 0    ,   _A				:   Optional[List[int]] = None    ,   _A				:   Optional[List[int]] = None    ,   _A				:   int = 6_4    ,   _A				:   int = 3_2    ,   _A				:   int = 3_2    ,   _A				:   int = 2    ,   _A				:   int = 2    ,   _A				:   int = 2    ,   _A				:   int = 2    ,   _A				:   bool = True    ,   _A				:   str = "gelu"    ,   _A				:   float = 0.05    ,   _A				:   float = 0.1    ,   _A				:   float = 0.1    ,   _A				:   float = 0.1    ,   _A				:   float = 0.1    ,   _A				:   int = 1_0_0    ,   _A				:   float = 0.02    ,   _A				:   Optional[int]=True    ,   _A				:   str = "prob"    ,   _A				:   int = 5    ,   _A				:   bool = True    ,   **_A				:   int    ,   )						->						int:
         # time series specific configuration
         snake_case								= prediction_length
         snake_case								= context_length or prediction_length
         snake_case								= distribution_output
         snake_case								= loss
         snake_case								= input_size
         snake_case								= num_time_features
         snake_case								= lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
         snake_case								= scaling
         snake_case								= num_dynamic_real_features
         snake_case								= num_static_real_features
         snake_case								= num_static_categorical_features

         # set cardinality
         if cardinality and num_static_categorical_features > 0:
             if len(_A       ) != num_static_categorical_features:
                 raise ValueError(
                     "The cardinality should be a list of the same length as `num_static_categorical_features`"       )
             snake_case								= cardinality
         else:
             snake_case								= [0]

         # set embedding_dimension
         if embedding_dimension and num_static_categorical_features > 0:
             if len(_A       ) != num_static_categorical_features:
                 raise ValueError(
                     "The embedding dimension should be a list of the same length as `num_static_categorical_features`"       )
             snake_case								= embedding_dimension
         else:
             snake_case								= [min(5_0    ,   (cat + 1) // 2       ) for cat in self.cardinality]

         snake_case								= num_parallel_samples

         # Transformer architecture configuration
         snake_case								= input_size * len(self.lags_sequence       ) + self._number_of_features
         snake_case								= d_model
         snake_case								= encoder_attention_heads
         snake_case								= decoder_attention_heads
         snake_case								= encoder_ffn_dim
         snake_case								= decoder_ffn_dim
         snake_case								= encoder_layers
         snake_case								= decoder_layers

         snake_case								= dropout
         snake_case								= attention_dropout
         snake_case								= activation_dropout
         snake_case								= encoder_layerdrop
         snake_case								= decoder_layerdrop

         snake_case								= activation_function
         snake_case								= init_std

         snake_case								= use_cache

         # Informer
         snake_case								= attention_type
         snake_case								= sampling_factor
         snake_case								= distil

         super().__init__(is_encoder_decoder=_A    ,   **_A       )





     @property
     def 		UpperCAmelCase(self				:   Dict       )						->						int:
         return (
             sum(self.embedding_dimension       )
             + self.num_dynamic_real_features
             + self.num_time_features
             + self.num_static_real_features
             + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
         )



from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap



def 						lowercase_      (       A__		)							->							str:
    """simple docstring"""

    return getitem, k



def 						lowercase_      (       A__			,		A__		)							->							str:
    """simple docstring"""

    return setitem, k, v



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    return delitem, k



def 						lowercase_      (       A__			,		A__			,		*A__		)							->							str:
    """simple docstring"""

    try:
        return fun(A__			,		*A__		), None
    except Exception as e:
        return None, e


_A   =			(
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
)

_A   =			[
    _set("key_a", "val_a"),
    _set("key_a", "val_b"),
]

_A   =			[
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
    _del("key_a"),
    _del("key_b"),
    _set("key_a", "val_a"),
    _del("key_a"),
]

_A   =			[
    _get("key_a"),
    _del("key_a"),
    _set("key_a", "val_a"),
    _del("key_a"),
    _del("key_a"),
    _get("key_a"),
]

_A   =			[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

_A   =			[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("key_a", "val_b"),
]



@pytest.mark.parametrize(
    "operations"			,		(
        pytest.param(_add_items			,		id="add items"		),
        pytest.param(_overwrite_items			,		id="overwrite items"		),
        pytest.param(_delete_items			,		id="delete items"		),
        pytest.param(_access_absent_items			,		id="access absent items"		),
        pytest.param(_add_with_resize_up			,		id="add with resize up"		),
        pytest.param(_add_with_resize_down			,		id="add with resize down"		),
    )			,		)
def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    snake_case								= HashMap(initial_block_size=4		)
    snake_case								= {}
    for _, (fun, *args) in enumerate(A__		):
        snake_case					,							snake_case								= _run_operation(A__			,		A__			,		*A__		)
        snake_case					,							snake_case								= _run_operation(A__			,		A__			,		*A__		)
        assert my_res == py_res
        assert str(A__		) == str(A__		)
        assert set(A__		) == set(A__		)
        assert len(A__		) == len(A__		)
        assert set(my.items()		) == set(py.items()		)





def 						lowercase_      (       )							->							Optional[int]:
    """simple docstring"""

    def is_public(A__		) -> bool:
        return not name.startswith("_"		)

    snake_case								= {name for name in dir({}		) if is_public(A__		)}
    snake_case								= {name for name in dir(HashMap()		) if is_public(A__		)}

    assert dict_public_names > hash_public_names

