
from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar


class 						__magic_name__				(  snake_case      ):





		@abstractmethod
		def __lt__(							self      :		T  ,					lowerCamelCase__      :		T			):
							pass





		@abstractmethod
		def __gt__(							self      :		T  ,					lowerCamelCase__      :		T			):
							pass





		@abstractmethod
		def __eq__(							self      :		T  ,					lowerCamelCase__      :		object			):
							pass



__SCREAMING_SNAKE_CASE     :				List[Any]             =       TypeVar('T', bound=Comparable)


class 						__magic_name__				(  Generic[T]      ):





		def __init__(							self      :		str			):
							lowerCAmelCase						:   list[T]							=       []
							lowerCAmelCase						:   int							=       0





		def __repr__(							self      :		Any			):
							return str(self.h			)





		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		int			):
							if child_idx > 0:
												return (child_idx - 1) // 2
							return None





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		int			):
							lowerCAmelCase						:   Tuple							=       2 * parent_idx + 1
							if left_child_index < self.heap_size:
												return left_child_index
							return None





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		int			):
							lowerCAmelCase						:   Tuple							=       2 * parent_idx + 2
							if right_child_index < self.heap_size:
												return right_child_index
							return None





		def  _A							(							self      :		int  ,					lowerCamelCase__      :		int			):
							if index < self.heap_size:
												lowerCAmelCase						:   int							=       index
												lowerCAmelCase						:   Union[str, Any]							=       self.left_child_idx(lowerCamelCase__			)
												lowerCAmelCase						:   List[Any]							=       self.right_child_idx(lowerCamelCase__			)
												# check which child is larger than its parent
												if left_child is not None and self.h[left_child] > self.h[violation]:
																	lowerCAmelCase						:   int							=       left_child
												if right_child is not None and self.h[right_child] > self.h[violation]:
																	lowerCAmelCase						:   Optional[Any]							=       right_child
												# if violation indeed exists
												if violation != index:
																	# swap to fix the violation
																	lowerCAmelCase		,							lowerCAmelCase						:   Any							=       self.h[index], self.h[violation]
																	# fix the subsequent violation recursively if any
																	self.max_heapify(lowerCamelCase__			)





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		Iterable[T]			):
							lowerCAmelCase						:   Any							=       list(lowerCamelCase__			)
							lowerCAmelCase						:   List[str]							=       len(self.h			)
							if self.heap_size > 1:
												# max_heapify from right to left but exclude leaves (last level)
												for i in range(self.heap_size // 2 - 1  ,					-1  ,					-1			):
																	self.max_heapify(lowerCamelCase__			)





		def  _A							(							self      :		str			):
							if self.heap_size >= 2:
												lowerCAmelCase						:   Union[str, Any]							=       self.h[0]
												lowerCAmelCase						:   List[str]							=       self.h.pop(-1			)
												self.heap_size -= 1
												self.max_heapify(0			)
												return me
							elif self.heap_size == 1:
												self.heap_size -= 1
												return self.h.pop(-1			)
							else:
												raise Exception('''Empty heap'''			)





		def  _A							(							self      :		Optional[Any]  ,					lowerCamelCase__      :		T			):
							self.h.append(lowerCamelCase__			)
							lowerCAmelCase						:   Tuple							=       (self.heap_size - 1) // 2
							self.heap_size += 1
							while idx >= 0:
												self.max_heapify(lowerCamelCase__			)
												lowerCAmelCase						:   Union[str, Any]							=       (idx - 1) // 2





		def  _A							(							self      :		Tuple			):
							lowerCAmelCase						:   List[str]							=       self.heap_size
							for j in range(size - 1  ,					0  ,					-1			):
												lowerCAmelCase		,							lowerCAmelCase						:   Tuple							=       self.h[j], self.h[0]
												self.heap_size -= 1
												self.max_heapify(0			)
							lowerCAmelCase						:   Optional[int]							=       size


if __name__ == "__main__":
		import doctest

		# run doc test
		doctest.testmod()

		# demo
		for unsorted in [
		    [0],
		    [2],
		    [3, 5],
		    [5, 3],
		    [5, 5],
		    [0, 0, 0, 0],
		    [1, 1, 1, 1],
		    [2, 2, 3, 5],
		    [0, 2, 2, 3, 5],
		    [2, 5, 3, 0, 2, 3, 0, 3],
		    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
		    [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
		    [-45, -2, -5],
		]:
				print(f"""unsorted array: {unsorted}""")

				__SCREAMING_SNAKE_CASE     :				Heap[int]             =       Heap()
				heap.build_max_heap(unsorted)
				print(f"""after build heap: {heap}""")

				print(f"""max value: {heap.extract_max()}""")
				print(f"""after max value removed: {heap}""")

				heap.insert(1_00)
				print(f"""after new value 100 inserted: {heap}""")

				heap.heap_sort()
				print(f"""heap-sorted array: {heap}\n""")


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :				int             =       {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}


class 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"gpt2"
		_lowerCAmelCase						=		["past_key_values"]
		_lowerCAmelCase						=		{
		    "hidden_size": "n_embd",
		    "max_position_embeddings": "n_positions",
		    "num_attention_heads": "n_head",
		    "num_hidden_layers": "n_layer",
		}





		def __init__(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Tuple=5_0_2_5_7  ,					lowerCamelCase__      :		str=1_0_2_4  ,					lowerCamelCase__      :		Union[str, Any]=7_6_8  ,					lowerCamelCase__      :		Tuple=1_2  ,					lowerCamelCase__      :		Tuple=1_2  ,					lowerCamelCase__      :		List[str]=None  ,					lowerCamelCase__      :		Tuple="gelu_new"  ,					lowerCamelCase__      :		List[str]=0.1  ,					lowerCamelCase__      :		str=0.1  ,					lowerCamelCase__      :		List[str]=0.1  ,					lowerCamelCase__      :		Dict=1E-5  ,					lowerCamelCase__      :		Optional[Any]=0.0_2  ,					lowerCamelCase__      :		Tuple="cls_index"  ,					lowerCamelCase__      :		str=True  ,					lowerCamelCase__      :		Optional[int]=None  ,					lowerCamelCase__      :		int=True  ,					lowerCamelCase__      :		Tuple=0.1  ,					lowerCamelCase__      :		List[str]=True  ,					lowerCamelCase__      :		Dict=True  ,					lowerCamelCase__      :		Tuple=5_0_2_5_6  ,					lowerCamelCase__      :		Optional[int]=5_0_2_5_6  ,					lowerCamelCase__      :		Dict=False  ,					lowerCamelCase__      :		Tuple=False  ,					**lowerCamelCase__      :		List[str]  ,					):
							lowerCAmelCase						:   List[str]							=       vocab_size
							lowerCAmelCase						:   Dict							=       n_positions
							lowerCAmelCase						:   int							=       n_embd
							lowerCAmelCase						:   Union[str, Any]							=       n_layer
							lowerCAmelCase						:   List[str]							=       n_head
							lowerCAmelCase						:   str							=       n_inner
							lowerCAmelCase						:   Tuple							=       activation_function
							lowerCAmelCase						:   List[Any]							=       resid_pdrop
							lowerCAmelCase						:   Any							=       embd_pdrop
							lowerCAmelCase						:   int							=       attn_pdrop
							lowerCAmelCase						:   Dict							=       layer_norm_epsilon
							lowerCAmelCase						:   List[str]							=       initializer_range
							lowerCAmelCase						:   List[Any]							=       summary_type
							lowerCAmelCase						:   int							=       summary_use_proj
							lowerCAmelCase						:   List[str]							=       summary_activation
							lowerCAmelCase						:   List[str]							=       summary_first_dropout
							lowerCAmelCase						:   Optional[int]							=       summary_proj_to_labels
							lowerCAmelCase						:   Optional[int]							=       scale_attn_weights
							lowerCAmelCase						:   List[str]							=       use_cache
							lowerCAmelCase						:   List[Any]							=       scale_attn_by_inverse_layer_idx
							lowerCAmelCase						:   int							=       reorder_and_upcast_attn

							lowerCAmelCase						:   List[Any]							=       bos_token_id
							lowerCAmelCase						:   Optional[int]							=       eos_token_id

							super().__init__(bos_token_id=lowerCamelCase__  ,					eos_token_id=lowerCamelCase__  ,					**lowerCamelCase__			)



class 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		str  ,					lowerCamelCase__      :		PretrainedConfig  ,					lowerCamelCase__      :		str = "default"  ,					lowerCamelCase__      :		List[PatchingSpec] = None  ,					lowerCamelCase__      :		bool = False  ,					):
							super().__init__(lowerCamelCase__  ,					task=lowerCamelCase__  ,					patching_specs=lowerCamelCase__  ,					use_past=lowerCamelCase__			)
							if not getattr(self._config  ,					'''pad_token_id'''  ,					lowerCamelCase__			):
												# TODO: how to do that better?
												lowerCAmelCase						:   str							=       0





		@property
		def  _A							(							self      :		Optional[Any]			):
							lowerCAmelCase						:   List[Any]							=       OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}			)
							if self.use_past:
												self.fill_with_past_key_values_(lowerCamelCase__  ,					direction='''inputs'''			)
												lowerCAmelCase						:   str							=       {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
												lowerCAmelCase						:   int							=       {0: '''batch''', 1: '''sequence'''}

							return common_inputs





		@property
		def  _A							(							self      :		Optional[int]			):
							return self._config.n_layer





		@property
		def  _A							(							self      :		List[str]			):
							return self._config.n_head





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		PreTrainedTokenizer  ,					lowerCamelCase__      :		int = -1  ,					lowerCamelCase__      :		int = -1  ,					lowerCamelCase__      :		bool = False  ,					lowerCamelCase__      :		Optional[TensorType] = None  ,					):
							lowerCAmelCase						:   Union[str, Any]							=       super(lowerCamelCase__  ,					self			).generate_dummy_inputs(
							    lowerCamelCase__  ,					batch_size=lowerCamelCase__  ,					seq_length=lowerCamelCase__  ,					is_pair=lowerCamelCase__  ,					framework=lowerCamelCase__			)

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase						:   Tuple							=       OrderedDict({'''input_ids''': common_inputs['''input_ids''']}			)

							# Need to add the past_keys
							if self.use_past:
												if not is_torch_available():
																	raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
												else:
																	import torch

																	lowerCAmelCase		,							lowerCAmelCase						:   Any							=       common_inputs['''input_ids'''].shape
																	# Not using the same length for past_key_values
																	lowerCAmelCase						:   Optional[int]							=       seqlen + 2
																	lowerCAmelCase						:   Any							=       (
																	    batch,
																	    self.num_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // self.num_attention_heads,
																	)
																	lowerCAmelCase						:   Any							=       [
																	    (torch.zeros(lowerCamelCase__			), torch.zeros(lowerCamelCase__			)) for _ in range(self.num_layers			)
																	]

							lowerCAmelCase						:   List[str]							=       common_inputs['''attention_mask''']
							if self.use_past:
												lowerCAmelCase						:   Optional[Any]							=       ordered_inputs['''attention_mask'''].dtype
												lowerCAmelCase						:   Optional[Any]							=       torch.cat(
												    [ordered_inputs['''attention_mask'''], torch.ones(lowerCamelCase__  ,					lowerCamelCase__  ,					dtype=lowerCamelCase__			)]  ,					dim=1			)

							return ordered_inputs





		@property
		def  _A							(							self      :		Union[str, Any]			):
							return 1_3
