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 				__UpperCamelCase   (    self      :       int,_A      :       List[Any]    ):







							"""simple docstring"""






							for model_result in results.values():
									for batch_size, sequence_length in zip(model_result["bs"],model_result["ss"]    ):
											SCREAMING_SNAKE_CASE_  :		Any				      =    model_result["result"][batch_size][sequence_length]
											self.assertIsNotNone(_A    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],eager_mode=_A,multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result    )
							self.check_results_dict_not_empty(results.memory_inference_result    )

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "sgugger/tiny-distilbert-classification"
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,only_pretrain_model=_A,)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result    )
							self.check_results_dict_not_empty(results.memory_inference_result    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result    )
							self.check_results_dict_not_empty(results.memory_inference_result    )

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],eager_mode=_A,multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    TensorFlowBenchmark(_A,[config]    )
							SCREAMING_SNAKE_CASE_  :		int				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result    )
							self.check_results_dict_not_empty(results.memory_inference_result    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmark(_A,[config]    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result    )
							self.check_results_dict_not_empty(results.memory_inference_result    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Any				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Dict				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result    )
							self.check_results_dict_not_empty(results.memory_train_result    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		Any				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TensorFlowBenchmark(_A,[config]    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result    )
							self.check_results_dict_not_empty(results.memory_train_result    )

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "patrickvonplaten/t5-tiny-random"
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoConfig.from_pretrained(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TensorFlowBenchmark(_A,configs=[config]    )
							SCREAMING_SNAKE_CASE_  :		str				      =    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 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "sshleifer/tiny-gpt2"
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TensorFlowBenchmarkArguments(
							    models=[MODEL_ID],training=_A,inference=_A,sequence_lengths=[8],batch_sizes=[1],use_xla=_A,multi_process=_A,)
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    TensorFlowBenchmark(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result    )
							self.check_results_dict_not_empty(results.memory_inference_result    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    "sshleifer/tiny-gpt2"
							with tempfile.TemporaryDirectory() as tmp_dir:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    TensorFlowBenchmarkArguments(
									    models=[MODEL_ID],inference=_A,save_to_csv=_A,sequence_lengths=[8],batch_sizes=[1],inference_time_csv_file=os.path.join(_A,"inf_time.csv"    ),inference_memory_csv_file=os.path.join(_A,"inf_mem.csv"    ),env_info_csv_file=os.path.join(_A,"env.csv"    ),multi_process=_A,)
									SCREAMING_SNAKE_CASE_  :		Dict				      =    TensorFlowBenchmark(_A    )
									benchmark.run()
									self.assertTrue(Path(os.path.join(_A,"inf_time.csv"    )    ).exists()    )
									self.assertTrue(Path(os.path.join(_A,"inf_mem.csv"    )    ).exists()    )
									self.assertTrue(Path(os.path.join(_A,"env.csv"    )    ).exists()    )






					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "sshleifer/tiny-gpt2"

							def _check_summary_is_not_empty(_A      :       Optional[Any]    ):
									self.assertTrue(hasattr(_A,"sequential"    )    )
									self.assertTrue(hasattr(_A,"cumulative"    )    )
									self.assertTrue(hasattr(_A,"current"    )    )
									self.assertTrue(hasattr(_A,"total"    )    )

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

from collections.abc import Sequence
from queue import Queue






class 		a__						:

					def __init__(    self      :       int,_A      :       List[Any],_A      :       Optional[Any],_A      :       Optional[int],_A      :       int=None,_A      :       List[str]=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    start
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    end
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    val
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    left
							SCREAMING_SNAKE_CASE_  :		str				      =    right






					def __repr__(    self      :       Tuple    ):







							"""simple docstring"""






							return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'







class 		a__						:

					def __init__(    self      :       Any,_A      :       Sequence,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    collection
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    function
							if self.collection:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._build_tree(0,len(_A    ) - 1    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							self._update_tree(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							return self._query_range(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       int    ):







							"""simple docstring"""






							if start == end:
									return SegmentTreeNode(_A,_A,self.collection[start]    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._build_tree(_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._build_tree(mid + 1,_A    )
							return SegmentTreeNode(_A,_A,self.fn(left.val,right.val    ),_A,_A    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       int,_A      :       Tuple,_A      :       Dict    ):







							"""simple docstring"""






							if node.start == i and node.end == i:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    val
									return
							if i <= node.mid:
									self._update_tree(node.left,_A,_A    )
							else:
									self._update_tree(node.right,_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.fn(node.left.val,node.right.val    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str],_A      :       Optional[int],_A      :       Optional[Any]    ):







							"""simple docstring"""






							if node.start == i and node.end == j:
									return node.val

							if i <= node.mid:
									if j <= node.mid:
											# range in left child tree
											return self._query_range(node.left,_A,_A    )
									else:
											# range in left child tree and right child tree
											return self.fn(
											    self._query_range(node.left,_A,node.mid    ),self._query_range(node.right,node.mid + 1,_A    ),)
							else:
									# range in right child tree
									return self._query_range(node.right,_A,_A    )






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							if self.root is not None:
									SCREAMING_SNAKE_CASE_  :		int				      =    Queue()
									queue.put(self.root    )
									while not queue.empty():
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    queue.get()
											yield node

											if node.left is not None:
													queue.put(node.left    )

											if node.right is not None:
													queue.put(node.right    )


if __name__ == "__main__":
		import operator

		for fn in [operator.add, max, min]:
				print('''*''' * 50)
				__lowerCamelCase					:	int       =  SegmentTree([2, 1, 5, 3, 4], fn)
				for node in arr.traverse():
						print(node)
				print()

				arr.update(1, 5)
				for node in arr.traverse():
						print(node)
				print()

				print(arr.query_range(3, 4))  # 7
				print(arr.query_range(2, 2))  # 5
				print(arr.query_range(1, 3))  # 13
				print()
