from collections.abc import Callable


class 						a__							:



			def __init__(						self				,						UpperCAmelCase = None   )			->   None:
								# Stores actual heap items.
								__a		 =       []
								# Stores indexes of each item for supporting updates and deletion.
								__a		 =       {}
								# Stores current size of heap.
								__a		 =       0
								# Stores function used to evaluate the score of an item on which basis ordering
								# will be done.
								__a		 =       key or (lambda UpperCAmelCase   : x)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   int | None:
								return int((i - 1) / 2   ) if i > 0 else None



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   int | None:
								__a		 =       int(2 * i + 1   )
								return left if 0 < left < self.size else None



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   int | None:
								__a		 =       int(2 * i + 2   )
								return right if 0 < right < self.size else None



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   None:
								__a ,				__a		 =       (
								    self.pos_map[self.arr[j][0]],
								    self.pos_map[self.arr[i][0]],
								)
								# Then swap the items in the list.
								__a ,				__a		 =       self.arr[j], self.arr[i]



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   bool:
								return self.arr[i][1] < self.arr[j][1]



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   int:
								__a		 =       self._left(UpperCAmelCase   )
								__a		 =       self._right(UpperCAmelCase   )
								__a		 =       i

								if left is not None and not self._cmp(UpperCAmelCase				,						UpperCAmelCase   ):
													__a		 =       left
								if right is not None and not self._cmp(UpperCAmelCase				,						UpperCAmelCase   ):
													__a		 =       right

								return valid_parent



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								__a		 =       self._parent(UpperCAmelCase   )
								while parent is not None and not self._cmp(UpperCAmelCase				,						UpperCAmelCase   ):
													self._swap(UpperCAmelCase				,						UpperCAmelCase   )
													__a ,				__a		 =       parent, self._parent(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								__a		 =       self._get_valid_parent(UpperCAmelCase   )
								while valid_parent != index:
													self._swap(UpperCAmelCase				,						UpperCAmelCase   )
													__a ,				__a		 =       valid_parent, self._get_valid_parent(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   None:
								if item not in self.pos_map:
													return
								__a		 =       self.pos_map[item]
								__a		 =       [item, self.key(UpperCAmelCase   )]
								# Make sure heap is right in both up and down direction.
								# Ideally only one of them will make any change.
								self._heapify_up(UpperCAmelCase   )
								self._heapify_down(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   None:
								if item not in self.pos_map:
													return
								__a		 =       self.pos_map[item]
								del self.pos_map[item]
								__a		 =       self.arr[self.size - 1]
								__a		 =       index
								self.size -= 1
								# Make sure heap is right in both up and down direction. Ideally only one
								# of them will make any change- so no performance loss in calling both.
								if self.size > index:
													self._heapify_up(UpperCAmelCase   )
													self._heapify_down(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   None:
								__a		 =       len(self.arr   )
								if arr_len == self.size:
													self.arr.append([item, self.key(UpperCAmelCase   )]   )
								else:
													__a		 =       [item, self.key(UpperCAmelCase   )]
								__a		 =       self.size
								self.size += 1
								self._heapify_up(self.size - 1   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   tuple | None:
								return self.arr[0] if self.size else None







			def __SCREAMING_SNAKE_CASE							(						self   )			->   tuple | None:
								__a		 =       self.get_top()
								if top_item_tuple:
													self.delete_item(top_item_tuple[0]   )
								return top_item_tuple






def 							lowerCAmelCase(     ):
					pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[Any]:
								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(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sgugger/tiny-distilbert-classification'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						only_pretrain_model=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[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 __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[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 __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result   )
								self.check_results_dict_not_empty(results.memory_train_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						[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 __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       'patrickvonplaten/t5-tiny-random'
								__a		 =       AutoConfig.from_pretrained(UpperCAmelCase   )
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase				,						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   )



			@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'   )   ) == 0				,						'Cannot do xla on CPU.'   )
			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       'sshleifer/tiny-gpt2'
								__a		 =       TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]				,						training=UpperCAmelCase				,						inference=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						use_xla=UpperCAmelCase				,						multi_process=UpperCAmelCase				,						)
								__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
								__a		 =       benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result   )
								self.check_results_dict_not_empty(results.memory_inference_result   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       'sshleifer/tiny-gpt2'
								with tempfile.TemporaryDirectory() as tmp_dir:
													__a		 =       TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,						inference=UpperCAmelCase				,						save_to_csv=UpperCAmelCase				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						inference_time_csv_file=os.path.join(UpperCAmelCase				,						'inf_time.csv'   )				,						inference_memory_csv_file=os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )				,						env_info_csv_file=os.path.join(UpperCAmelCase				,						'env.csv'   )				,						multi_process=UpperCAmelCase				,						)
													__a		 =       TensorFlowBenchmark(UpperCAmelCase   )
													benchmark.run()
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_time.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'inf_mem.csv'   )   ).exists()   )
													self.assertTrue(Path(os.path.join(UpperCAmelCase				,						'env.csv'   )   ).exists()   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       'sshleifer/tiny-gpt2'

								def _check_summary_is_not_empty(UpperCAmelCase   ):
													self.assertTrue(hasattr(UpperCAmelCase				,						'sequential'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'cumulative'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'current'   )   )
													self.assertTrue(hasattr(UpperCAmelCase				,						'total'   )   )

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




