


'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

UpperCAmelCase__  			=				TypeVar('''T''')
class 						a__					(					Generic[T]   ):





			'''simple docstring'''

			def __init__(  self							:			List[str]			,    lowerCAmelCase_							:			list[T]			,    lowerCAmelCase_							:			Callable[[T, T], T]	)      ->		None:
						__A=	None

						__A=	len(lowerCAmelCase_	)
						__A=	[any_type for _ in range(self.N	)] + arr
						__A=	fnc
						self.build()
			def 					lowerCAmelCase     (  self							:			int	)      ->		None:
						for p in range(self.N - 1			,    0			,    -1	):
									__A=	self.fn(self.st[p * 2]			,    self.st[p * 2 + 1]	)
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			T	)      ->		None:
						p += self.N
						__A=	v
						while p > 1:
									__A=	p // 2
									__A=	self.fn(self.st[p * 2]			,    self.st[p * 2 + 1]	)

			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			int	)      ->		T | None:  # noqa: E741
						__A,	__A=	l + self.N, r + self.N

						__A=	None
						while l <= r:
									if l % 2 == 1:
												__A=	self.st[l] if res is None else self.fn(lowerCAmelCase_			,    self.st[l]	)
									if r % 2 == 0:
												__A=	self.st[r] if res is None else self.fn(lowerCAmelCase_			,    self.st[r]	)
									__A,	__A=	(l + 1) // 2, (r - 1) // 2
						return res


if __name__ == "__main__":
					from functools import reduce

					UpperCAmelCase__  			=				[1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

					UpperCAmelCase__  			=				{
					    0: 7,
					    1: 2,
					    2: 6,
					    3: -1_4,
					    4: 5,
					    5: 4,
					    6: 7,
					    7: -1_0,
					    8: 9,
					    9: 1_0,
					    1_0: 1_2,
					    1_1: 1,
					}

					UpperCAmelCase__  			=				SegmentTree(test_array, min)
					UpperCAmelCase__  			=				SegmentTree(test_array, max)
					UpperCAmelCase__  			=				SegmentTree(test_array, lambda a, b: a + b)






					def 						UpperCAmelCase__(				):






								"""simple docstring"""




								for i in range(len(_SCREAMING_SNAKE_CASE ) ):
											for j in range(_SCREAMING_SNAKE_CASE,len(_SCREAMING_SNAKE_CASE ) ):
														__A=	reduce(_SCREAMING_SNAKE_CASE,test_array[i : j + 1] )
														__A=	reduce(_SCREAMING_SNAKE_CASE,test_array[i : j + 1] )
														__A=	reduce(lambda _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE : a + b,test_array[i : j + 1] )
														assert min_range == min_segment_tree.query(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
														assert max_range == max_segment_tree.query(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
														assert sum_range == sum_segment_tree.query(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )

					test_all_segments()

					for index, value in test_updates.items():
										UpperCAmelCase__  			=				value
										min_segment_tree.update(index, value)
										max_segment_tree.update(index, value)
										sum_segment_tree.update(index, value)
										test_all_segments()







'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
					from transformers import (
					    AutoConfig,
					    BertConfig,
					    GPTaConfig,
					    TaConfig,
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					)
					from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
					from transformers import (
					    AutoModel,
					    AutoModelForCausalLM,
					    AutoModelForMaskedLM,
					    AutoModelForPreTraining,
					    AutoModelForQuestionAnswering,
					    AutoModelForSeqaSeqLM,
					    AutoModelForSequenceClassification,
					    AutoModelWithLMHead,
					    BertForMaskedLM,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    BertModel,
					    GPTaLMHeadModel,
					    RobertaForMaskedLM,
					    TaForConditionalGeneration,
					)
@is_pt_tf_cross_test
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			@slow
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		List[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModel.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModel.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Optional[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForPreTraining.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForPreTraining.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			int	)      ->		Optional[int]:
						for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForCausalLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForCausalLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForCausalLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[Any]:
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[int]:
						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForMaskedLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForMaskedLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForMaskedLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Dict:
						for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									__A,	__A=	TFAutoModelForSeqaSeqLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									__A,	__A=	AutoModelForSeqaSeqLM.from_pretrained(
									    lowerCAmelCase_			,    output_loading_info=lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		List[Any]:
						# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						for model_name in ["bert-base-uncased"]:
									__A=	AutoConfig.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	TFAutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

									__A=	AutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)
									self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			int	)      ->		List[str]:
						__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

						__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						__A=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_pt=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)

						__A=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase_			,    from_tf=lowerCAmelCase_	)
						self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)
						self.assertEqual(model.num_parameters()			,    14_410	)
						self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_	)			,    14_410	)



