
import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							import torch

							from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class 				_UpperCamelCase (    unittest.TestCase     ):






			'''simple docstring'''

			@slow
			@unittest.skip("""Model is not available."""					)
			def 					lowerCAmelCase__		(				self       :				Dict					):
							UpperCamelCase_:							Optional[int]		=  """nvidia/megatron-gpt2-345m/"""
							if "MYDIR" in os.environ:
											UpperCamelCase_:							Optional[Any]		=  os.path.join(os.environ["""MYDIR"""]  ,		snake_case_					)
							UpperCamelCase_:							List[str]		=  GPTaLMHeadModel.from_pretrained(snake_case_					)
							model.to(snake_case_					)
							model.half()

							UpperCamelCase_:							Union[str, Any]		=  torch.tensor(
							    [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]  ,		device=snake_case_  ,		dtype=torch.long  ,		)

							with torch.no_grad():
											UpperCamelCase_:							List[Any]		=  model(snake_case_					).logits

							UpperCamelCase_:							Optional[Any]		=  torch.Size((1, 9, 5_0257)					)
							self.assertEqual(output.shape  ,		snake_case_					)

							UpperCamelCase_:							List[Any]		=  torch.tensor(
							    [
							        4.9414,
							        -0.2920,
							        -1.2148,
							        -4.0273,
							        -0.5161,
							        -5.2109,
							        -1.2412,
							        -1.8301,
							        -1.7734,
							        -4.7148,
							        -0.2317,
							        -1.0811,
							        -2.1777,
							        0.4141,
							        -3.7969,
							        -4.0586,
							        -2.5332,
							        -3.3809,
							        4.3867,
							    ]  ,		device=snake_case_  ,		dtype=torch.half  ,		)

							for i in range(19					):
											UpperCamelCase_,      UpperCamelCase_:							List[Any]		=  8 * i // 17, 2792 * i  # along the diagonal
											UpperCamelCase_,      UpperCamelCase_:							List[Any]		=  output[0, r, c], expected_diag[i]
											UpperCamelCase_:							Any		=  f'''row={r} col={c} computed={computed} expected={expected}'''
											self.assertAlmostEqual(snake_case_  ,		snake_case_  ,		delta=1e-4  ,		msg=snake_case_					)












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			: Union[str, Any]				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=value
         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=None




     @property
     def   __A				(	self			: Optional[Any]						)		->   T:


         '''simple docstring'''





         return self._value




     @staticmethod
     def   __A				(	_UpperCamelCase			: RandomizedHeapNode[T] | None				,_UpperCamelCase			: RandomizedHeapNode[T] | None						)		->   RandomizedHeapNode[T] | None:


         '''simple docstring'''





         if not roota:
             return roota

         if not roota:
             return roota

         if roota.value > roota.value:
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=roota, roota

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

         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(roota.left				,_UpperCamelCase						)

         return roota






class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Any				,_UpperCamelCase			: Iterable[T] | None = ()						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None

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




     def   __A				(	self			: Union[str, Any]				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(self._root				,RandomizedHeapNode(_UpperCamelCase						)						)




     def   __A				(	self			: Dict						)		->   T | None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.top()

         if self._root is None:
             return None

         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(self._root.left				,self._root.right						)

         return result




     def   __A				(	self			: Optional[Any]						)		->   T:


         '''simple docstring'''





         if not self._root:
             raise IndexError("""Can't get top element for the empty heap."""						)
         return self._root.value




     def   __A				(	self			: Optional[int]						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None




     def   __A				(	self			: List[Any]						)		->   list[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=[]
         while self:
             result.append(self.pop()						)

         return result




     def __bool__(	self			: Tuple						)		->   bool:


         '''simple docstring'''





         return self._root is not None


if __name__ == "__main__":
    import doctest

    doctest.testmod()






