

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments

@require_torch
class    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           for model_result in results.values():
               for batch_size, sequence_length in zip(model_result['''bs''']					,		model_result['''ss''']):
                   __a							=    model_result['''result'''][batch_size][sequence_length]
                   self.assertIsNotNone(_lowerCamelCase)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    '''sshleifer/tiny-gpt2'''
           __a							=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		multi_process=_lowerCamelCase					,		)
           __a							=    PyTorchBenchmark(_lowerCamelCase)
           __a							=    benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    '''sgugger/tiny-distilbert-classification'''
           __a							=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		multi_process=_lowerCamelCase					,		only_pretrain_model=_lowerCamelCase					,		)
           __a							=    PyTorchBenchmark(_lowerCamelCase)
           __a							=    benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    '''sshleifer/tiny-gpt2'''
           __a							=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		torchscript=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		multi_process=_lowerCamelCase					,		)
           __a							=    PyTorchBenchmark(_lowerCamelCase)
           __a							=    benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)



       @unittest.skipIf(torch_device == '''cpu'''					,		'''Cant do half precision''')
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    '''sshleifer/tiny-gpt2'''
           __a							=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		fpaa=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		multi_process=_lowerCamelCase					,		)
           __a							=    PyTorchBenchmark(_lowerCamelCase)
           __a							=    benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    '''sshleifer/tiny-gpt2'''
           __a							=    AutoConfig.from_pretrained(_lowerCamelCase)
           # set architectures equal to `None`
           __a							=    None
           __a							=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		multi_process=_lowerCamelCase					,		)
           __a							=    PyTorchBenchmark(_lowerCamelCase					,		configs=[config])
           __a							=    benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    '''sshleifer/tiny-gpt2'''
           __a							=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		multi_process=_lowerCamelCase					,		)
           __a							=    PyTorchBenchmark(_lowerCamelCase)
           __a							=    benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result)
           self.check_results_dict_not_empty(results.memory_train_result)



       @unittest.skipIf(torch_device == '''cpu'''					,		'''Can\'t do half precision''')
       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    '''sshleifer/tiny-gpt2'''
           __a							=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		fpaa=_lowerCamelCase					,		multi_process=_lowerCamelCase					,		)
           __a							=    PyTorchBenchmark(_lowerCamelCase)
           __a							=    benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result)
           self.check_results_dict_not_empty(results.memory_train_result)



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    '''sshleifer/tiny-gpt2'''
           __a							=    AutoConfig.from_pretrained(_lowerCamelCase)
           __a							=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		multi_process=_lowerCamelCase					,		)
           __a							=    PyTorchBenchmark(_lowerCamelCase					,		configs=[config])
           __a							=    benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    '''sshleifer/tinier_bart'''
           __a							=    AutoConfig.from_pretrained(_lowerCamelCase)
           __a							=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		multi_process=_lowerCamelCase					,		)
           __a							=    PyTorchBenchmark(_lowerCamelCase					,		configs=[config])
           __a							=    benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    '''sshleifer/tiny-gpt2'''
           __a							=    AutoConfig.from_pretrained(_lowerCamelCase)
           __a							=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		multi_process=_lowerCamelCase					,		)
           __a							=    PyTorchBenchmark(_lowerCamelCase					,		configs=[config])
           __a							=    benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result)
           self.check_results_dict_not_empty(results.memory_train_result)



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    '''sshleifer/tinier_bart'''
           __a							=    AutoConfig.from_pretrained(_lowerCamelCase)
           __a							=    PyTorchBenchmarkArguments(
               models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		multi_process=_lowerCamelCase					,		)
           __a							=    PyTorchBenchmark(_lowerCamelCase					,		configs=[config])
           __a							=    benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result)
           self.check_results_dict_not_empty(results.memory_train_result)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    '''sshleifer/tiny-gpt2'''
           with tempfile.TemporaryDirectory() as tmp_dir:
               __a							=    PyTorchBenchmarkArguments(
                   models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		save_to_csv=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		inference_time_csv_file=os.path.join(_lowerCamelCase					,		'''inf_time.csv''')					,		train_memory_csv_file=os.path.join(_lowerCamelCase					,		'''train_mem.csv''')					,		inference_memory_csv_file=os.path.join(_lowerCamelCase					,		'''inf_mem.csv''')					,		train_time_csv_file=os.path.join(_lowerCamelCase					,		'''train_time.csv''')					,		env_info_csv_file=os.path.join(_lowerCamelCase					,		'''env.csv''')					,		multi_process=_lowerCamelCase					,		)
               __a							=    PyTorchBenchmark(_lowerCamelCase)
               benchmark.run()
               self.assertTrue(Path(os.path.join(_lowerCamelCase					,		'''inf_time.csv''')).exists())
               self.assertTrue(Path(os.path.join(_lowerCamelCase					,		'''train_time.csv''')).exists())
               self.assertTrue(Path(os.path.join(_lowerCamelCase					,		'''inf_mem.csv''')).exists())
               self.assertTrue(Path(os.path.join(_lowerCamelCase					,		'''train_mem.csv''')).exists())
               self.assertTrue(Path(os.path.join(_lowerCamelCase					,		'''env.csv''')).exists())





       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    '''sshleifer/tiny-gpt2'''

           def _check_summary_is_not_empty(__SCREAMING_SNAKE_CASE							:					Dict):
               self.assertTrue(hasattr(_lowerCamelCase					,		'''sequential'''))
               self.assertTrue(hasattr(_lowerCamelCase					,		'''cumulative'''))
               self.assertTrue(hasattr(_lowerCamelCase					,		'''current'''))
               self.assertTrue(hasattr(_lowerCamelCase					,		'''total'''))

           with tempfile.TemporaryDirectory() as tmp_dir:
               __a							=    PyTorchBenchmarkArguments(
                   models=[MODEL_ID]					,		training=_lowerCamelCase					,		inference=_lowerCamelCase					,		sequence_lengths=[8]					,		batch_sizes=[1]					,		log_filename=os.path.join(_lowerCamelCase					,		'''log.txt''')					,		log_print=_lowerCamelCase					,		trace_memory_line_by_line=_lowerCamelCase					,		multi_process=_lowerCamelCase					,		)
               __a							=    PyTorchBenchmark(_lowerCamelCase)
               __a							=    benchmark.run()
               _check_summary_is_not_empty(result.inference_summary)
               _check_summary_is_not_empty(result.train_summary)
               self.assertTrue(Path(os.path.join(_lowerCamelCase					,		'''log.txt''')).exists())










from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class    _A	:
       UpperCamelCase__  :   int
       UpperCamelCase__  :   TreeNode | None						 =						None
       UpperCamelCase__  :   TreeNode | None						 =						None


__snake_case						:Optional[Any]						=					namedtuple('''CoinsDistribResult''', '''moves excess''')





def 					__snake_case     (      _UpperCAmelCase    ):

    if root is None:
        return 0

    # Validation
    def count_nodes(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_nodes(node.left    ) + count_nodes(node.right    ) + 1

    def count_coins(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_coins(node.left    ) + count_coins(node.right    ) + node.data

    if count_nodes(_UpperCAmelCase    ) != count_coins(_UpperCAmelCase    ):
        raise ValueError('''The nodes number should be same as the number of coins'''    )

    # Main calculation
    def get_distrib(_UpperCAmelCase    ) -> CoinsDistribResult:

        if node is None:
            return CoinsDistribResult(0       ,			1    )

        __a	,       __a							=    get_distrib(node.left    )
        __a	,       __a							=    get_distrib(node.right    )

        __a							=    1 - left_distrib_excess
        __a							=    1 - right_distrib_excess

        __a							=    (
            left_distrib_moves
            + right_distrib_moves
            + abs(_UpperCAmelCase    )
            + abs(_UpperCAmelCase    )
        )
        __a							=    node.data - coins_to_left - coins_to_right

        return CoinsDistribResult(_UpperCAmelCase       ,			_UpperCAmelCase    )

    return get_distrib(_UpperCAmelCase    )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





