






from __future__ import annotations

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

__UpperCAmelCase      =				TypeVar("T", bound=bool)
class 	a_(		Generic[T]	):



     """simple docstring"""



     def __init__(		self					:	Tuple  ,  lowerCAmelCase__					:	T)							->	None:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		value
      SCREAMING_SNAKE_CASE				    =		None
      SCREAMING_SNAKE_CASE				    =		None





     @property
     def     __UpperCamelCase						(		self					:	int)							->	T:

      """simple docstring"""
      return self._value





     @staticmethod
     def     __UpperCamelCase						(		lowerCAmelCase__					:	RandomizedHeapNode[T] | None  ,  lowerCAmelCase__					:	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  ,  lowerCAmelCase__)

      return roota

class 	a_(		Generic[T]	):



     """simple docstring"""



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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		None

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





     def     __UpperCamelCase						(		self					:	Optional[int]  ,  lowerCAmelCase__					:	T)							->	None:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		RandomizedHeapNode.merge(self._root  ,  RandomizedHeapNode(lowerCAmelCase__))





     def     __UpperCamelCase						(		self					:	Optional[int])							->	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     __UpperCamelCase						(		self					:	Tuple)							->	T:

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





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		None





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

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		[]
      while self:
       result.append(self.pop())

      return result





     def __bool__(		self					:	Optional[int])							->	bool:

      """simple docstring"""
      return self._root is not None


if __name__ == "__main__":
      import doctest

      doctest.testmod()











import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						@patch('sys.argv'		, ['fakeprogrampath', 'env']						)
						def UpperCAmelCase_					(   self   :   Tuple						)				->     Dict:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							with CaptureStd() as cs:
								transformers.commands.transformers_cli.main()
							self.assertIn('Python version'		, cs.out						)
							self.assertIn('Platform'		, cs.out						)
							self.assertIn('Using distributed or parallel set-up in script?'		, cs.out						)






						@is_pt_tf_cross_test
						@patch(
						    'sys.argv'		, ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']						)
						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     Optional[Any]:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'		, ignore_errors=_A						)  # cleans potential past runs
							transformers.commands.transformers_cli.main()

							# The original repo has no TF weights -- if they exist, they were created by the CLI
							self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'						)						)






						@require_torch
						@patch('sys.argv'		, ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']						)
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     str:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							# # remove any previously downloaded model to start clean
							shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'		, ignore_errors=_A						)

							# run the command
							transformers.commands.transformers_cli.main()

							# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'						)						)






						@require_torch
						@patch(
						    'sys.argv'		, [
						        'fakeprogrampath',
						        'download',
						        'hf-internal-testing/test_dynamic_model_with_tokenizer',
						        '--trust-remote-code',
						        '--cache-dir',
						        '/tmp',
						    ]		, )
						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     str:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							# # remove any previously downloaded model to start clean
							shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'		, ignore_errors=_A						)

							# run the command
							transformers.commands.transformers_cli.main()

							# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'						)						)
							self.assertTrue(
							    os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'						)						)

