

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


a_      =     logging.get_logger(__name__)

a_      =     {
    'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
    'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json',
    'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/config.json',
    'funnel-transformer/medium-base': 'https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json',
    'funnel-transformer/intermediate': (
        'https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json'
    ),
    'funnel-transformer/intermediate-base': (
        'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json'
    ),
    'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/config.json',
    'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json',
    'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json',
    'funnel-transformer/xlarge-base': 'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json',
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='funnel'
			lowerCamelCase__							     ={
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'n_head',
			}
			def __init__(      self				:			Optional[int]   ,   a				:			Optional[Any]=3_0522   ,   a				:			str=[4, 4, 4]   ,   a				:			Optional[Any]=None   ,   a				:			List[str]=2   ,   a				:			int=768   ,   a				:			Union[str, Any]=12   ,   a				:			List[Any]=64   ,   a				:			List[str]=3072   ,   a				:			Optional[Any]="gelu_new"   ,   a				:			Optional[int]=0.1   ,   a				:			List[Any]=0.1   ,   a				:			str=0.0   ,   a				:			Tuple=0.1   ,   a				:			Tuple=None   ,   a				:			Union[str, Any]=1e-9   ,   a				:			Optional[Any]="mean"   ,   a				:			Dict="relative_shift"   ,   a				:			Dict=True   ,   a				:			str=True   ,   a				:			Optional[int]=True   ,   **a				:			Dict   ,   )				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							block_sizes
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							[1] * len(a						) if block_repeats is None else block_repeats
				assert len(a						) == len(
				    self.block_repeats						), "`block_sizes` and `block_repeats` should have the same length."
				SCREAMING_SNAKE_CASE :   int					  =							num_decoder_layers
				SCREAMING_SNAKE_CASE :   Dict					  =							d_model
				SCREAMING_SNAKE_CASE :   List[str]					  =							n_head
				SCREAMING_SNAKE_CASE :   Dict					  =							d_head
				SCREAMING_SNAKE_CASE :   int					  =							d_inner
				SCREAMING_SNAKE_CASE :   str					  =							hidden_act
				SCREAMING_SNAKE_CASE :   str					  =							hidden_dropout
				SCREAMING_SNAKE_CASE :   List[str]					  =							attention_dropout
				SCREAMING_SNAKE_CASE :   str					  =							activation_dropout
				SCREAMING_SNAKE_CASE :   int					  =							initializer_range
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							initializer_std
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							layer_norm_eps
				assert pooling_type in [
				    "mean",
				    "max",
				], F"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."
				SCREAMING_SNAKE_CASE :   Tuple					  =							pooling_type
				assert attention_type in [
				    "relative_shift",
				    "factorized",
				], F"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."
				SCREAMING_SNAKE_CASE :   List[str]					  =							attention_type
				SCREAMING_SNAKE_CASE :   Tuple					  =							separate_cls
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							truncate_seq
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pool_q_only

				super().__init__(**a						)
			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Union[str, Any]:

				"""simple docstring"""

				return sum(self.block_sizes						)
			@num_hidden_layers.setter
			def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

				raise NotImplementedError(
				    "This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`."						)
			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Union[str, Any]:

				"""simple docstring"""

				return len(self.block_sizes						)






			@num_blocks.setter
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			Union[str, Any]						)				->							Tuple:

				"""simple docstring"""

				raise NotImplementedError("This model does not support the setting of `num_blocks`. Please set `block_sizes`."						)


from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap

def 			lowerCamelCase__			(       _a):
	return getitem, k

def 			lowerCamelCase__			(       _a				,      _a):
	return setitem, k, v

def 			lowerCamelCase__			(       _a):
	return delitem, k

def 			lowerCamelCase__			(       _a				,      _a				,      *_a):
	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 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							HashMap(initial_block_size=4)
	SCREAMING_SNAKE_CASE :   List[str]					  =							{}
	for _, (fun, *args) in enumerate(_a):
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							_run_operation(_a				,      _a				,      *_a)
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							_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 			lowerCamelCase__			(       ):
	def is_public(_a) -> bool:
		return not name.startswith("_")

	SCREAMING_SNAKE_CASE :   List[str]					  =							{name for name in dir({}) if is_public(_a)}
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							{name for name in dir(HashMap()) if is_public(_a)}

	assert dict_public_names > hash_public_names