






from __future__ import annotations

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

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

class      UpperCamelCase_							(				Generic[T]       ):




						def __init__(    self		,	lowercase       )			->						None:
													_a  :    T			=						value
													_a  :    RandomizedHeapNode[T] | None			=						None
													_a  :    RandomizedHeapNode[T] | None			=						None




						@property
						def      snake_case__(    self       )			->						T:
													return self._value







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

													if not roota:
																				return roota

													if roota.value > roota.value:
																				_a  :    Dict			=						roota, roota

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

													_a  :    Optional[int]			=						RandomizedHeapNode.merge(roota.left		,	lowerCAmelCase_       )

													return roota

class      UpperCamelCase_							(				Generic[T]       ):




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

													if data:
																				for item in data:
																											self.insert(lowerCAmelCase_       )




						def      snake_case__(    self		,	lowercase       )			->						None:
													_a  :    List[str]			=						RandomizedHeapNode.merge(self._root		,	RandomizedHeapNode(lowerCAmelCase_       )       )




						def      snake_case__(    self       )			->						T | None:
													_a  :    Any			=						self.top()

													if self._root is None:
																				return None

													_a  :    Optional[Any]			=						RandomizedHeapNode.merge(self._root.left		,	self._root.right       )

													return result




						def      snake_case__(    self       )			->						T:
													if not self._root:
																				raise IndexError('''Can\'t get top element for the empty heap.'''       )
													return self._root.value




						def      snake_case__(    self       )			->						None:
													_a  :    Optional[int]			=						None




						def      snake_case__(    self       )			->						list[Any]:
													_a  :    Optional[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()
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase				 =   logging.get_logger(__name__)

UpperCAmelCase				 =   {
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    List[str]			      = 'opt'
   UpperCamelCase_					:    List[str]			      = ['past_key_values']
   def __init__(     self				,  lowerCAmelCase_=5_02_72				,  lowerCAmelCase_=7_68				,  lowerCAmelCase_=12				,  lowerCAmelCase_=30_72				,  lowerCAmelCase_=20_48				,  lowerCAmelCase_=True				,  lowerCAmelCase_=False				,  lowerCAmelCase_=None				,  lowerCAmelCase_=0.1				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=12				,  lowerCAmelCase_="relu"				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=0.02				,  lowerCAmelCase_=True				,  lowerCAmelCase_=1				,  lowerCAmelCase_=2				,  lowerCAmelCase_=2				,  lowerCAmelCase_=True				,  lowerCAmelCase_=True				,  **lowerCAmelCase_				,  )						->Any:
        super().__init__(
            pad_token_id=lowerCAmelCase_				,  bos_token_id=lowerCAmelCase_				,  eos_token_id=lowerCAmelCase_				,  **lowerCAmelCase_				,  )
        a	:     Dict						      =    vocab_size
        a	:     int						      =    max_position_embeddings
        a	:     List[Any]						      =    num_attention_heads
        a	:     Union[str, Any]						      =    word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        a	:     Any						      =    ffn_dim
        a	:     Optional[int]						      =    hidden_size
        a	:     Optional[Any]						      =    num_hidden_layers
        a	:     Optional[int]						      =    dropout
        a	:     Optional[Any]						      =    attention_dropout
        a	:     Union[str, Any]						      =    activation_function
        a	:     Any						      =    init_std
        a	:     List[Any]						      =    layerdrop
        a	:     str						      =    use_cache
        a	:     Any						      =    do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        a	:     List[Any]						      =    enable_bias
        a	:     List[str]						      =    layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        a	:     Dict						      =    _remove_final_layer_norm