




'''simple docstring'''






from typing import Mapping

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


_lowerCAmelCase				     = logging.get_logger(__name__)



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    "mt5"
				A				     =    ["past_key_values"]
				def __init__(self			,  _UpperCAmelCase=2_5_0_1_1_2			,  _UpperCAmelCase=5_1_2			,  _UpperCAmelCase=6_4			,  _UpperCAmelCase=1_0_2_4			,  _UpperCAmelCase=8			,  _UpperCAmelCase=None			,  _UpperCAmelCase=6			,  _UpperCAmelCase=3_2			,  _UpperCAmelCase=1_2_8			,  _UpperCAmelCase=0.1			,  _UpperCAmelCase=1E-6			,  _UpperCAmelCase=1.0			,  _UpperCAmelCase="gated-gelu"			,  _UpperCAmelCase=True			,  _UpperCAmelCase=True			,  _UpperCAmelCase="T5Tokenizer"			,  _UpperCAmelCase=False			,  _UpperCAmelCase=0			,  _UpperCAmelCase=1			,  _UpperCAmelCase=0			,  **_UpperCAmelCase			,  )     ->	Union[str, Any]:
							super().__init__(
							    is_encoder_decoder=_UpperCAmelCase			,  tokenizer_class=_UpperCAmelCase			,  tie_word_embeddings=_UpperCAmelCase			,  pad_token_id=_UpperCAmelCase			,  eos_token_id=_UpperCAmelCase			,  decoder_start_token_id=_UpperCAmelCase			,  **_UpperCAmelCase			,  )
							__UpperCamelCase				:      List[Any]									=       vocab_size
							__UpperCamelCase				:      Optional[int]									=       d_model
							__UpperCamelCase				:      Any									=       d_kv
							__UpperCamelCase				:      Any									=       d_ff
							__UpperCamelCase				:      int									=       num_layers
							__UpperCamelCase				:      str									=       (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							__UpperCamelCase				:      Union[str, Any]									=       num_heads
							__UpperCamelCase				:      Any									=       relative_attention_num_buckets
							__UpperCamelCase				:      Tuple									=       relative_attention_max_distance
							__UpperCamelCase				:      Tuple									=       dropout_rate
							__UpperCamelCase				:      Dict									=       layer_norm_epsilon
							__UpperCamelCase				:      str									=       initializer_factor
							__UpperCamelCase				:      Union[str, Any]									=       feed_forward_proj
							__UpperCamelCase				:      Any									=       use_cache

							__UpperCamelCase				:      Optional[int]									=       self.feed_forward_proj.split("-"      )
							__UpperCamelCase				:      int									=       act_info[-1]
							__UpperCamelCase				:      List[str]									=       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":
										__UpperCamelCase				:      Optional[Any]									=       "gelu_new"
				@property
				def 	a_	(self      )     ->	str:
							return self.d_model
				@property
				def 	a_	(self      )     ->	List[str]:
							return self.num_heads





				@property
				def 	a_	(self      )     ->	str:
							return self.num_layers







class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def 	a_	(self      )     ->	Mapping[str, Mapping[int, str]]:
							__UpperCamelCase				:      str									=       {
							    "input_ids": {0: "batch", 1: "encoder_sequence"},
							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
							}
							if self.use_past:
										__UpperCamelCase				:      Union[str, Any]									=       "past_encoder_sequence + sequence"
										__UpperCamelCase				:      Any									=       {0: "batch"}
										__UpperCamelCase				:      str									=       {0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
										__UpperCamelCase				:      Dict									=       {0: "batch", 1: "decoder_sequence"}
										__UpperCamelCase				:      Dict									=       {0: "batch", 1: "decoder_sequence"}

							if self.use_past:
										self.fill_with_past_key_values_(_UpperCAmelCase			,  direction="inputs"      )

							return common_inputs
				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def 	a_	(self      )     ->	int:
							return 1_3





				@property
				def 	a_	(self      )     ->	float:
							return 5E-4









'''simple docstring'''






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_lowerCAmelCase				     = TypeVar('''T''', bound=bool)



class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      T									=       value
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None
				@property
				def 	a_	(self      )     ->	T:
							return self._value





				@staticmethod
				def 	a_	(_UpperCAmelCase			,  _UpperCAmelCase      )     ->	RandomizedHeapNode[T] | None:
							if not roota:
										return roota

							if not roota:
										return roota

							if roota.value > roota.value:
										__UpperCamelCase		, __UpperCamelCase				:      int									=       roota, roota

							if random.choice([True, False]      ):
										__UpperCamelCase		, __UpperCamelCase				:      Dict									=       roota.right, roota.left

							__UpperCamelCase				:      Any									=       RandomizedHeapNode.merge(roota.left			,  _UpperCAmelCase      )

							return roota







class  A		(			Generic[T]		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase = ()      )     ->	None:
							__UpperCamelCase				:      RandomizedHeapNode[T] | None									=       None

							if data:
										for item in data:
													self.insert(_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      str									=       RandomizedHeapNode.merge(self._root			,  RandomizedHeapNode(_UpperCAmelCase      )      )
				def 	a_	(self      )     ->	T | None:
							__UpperCamelCase				:      Dict									=       self.top()

							if self._root is None:
										return None

							__UpperCamelCase				:      List[str]									=       RandomizedHeapNode.merge(self._root.left			,  self._root.right      )

							return result
				def 	a_	(self      )     ->	T:
							if not self._root:
										raise IndexError("Can't get top element for the empty heap."      )
							return self._root.value
				def 	a_	(self      )     ->	None:
							__UpperCamelCase				:      int									=       None
				def 	a_	(self      )     ->	list[Any]:
							__UpperCamelCase				:      int									=       []
							while self:
										result.append(self.pop()      )

							return result





				def __bool__(self      )     ->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()



