






from collections.abc import Sequence
from queue import Queue




class 							_lowerCAmelCase:





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None				):
         UpperCamelCase_:   Tuple        =							start
         UpperCamelCase_:   Tuple        =							end
         UpperCamelCase_:   Optional[Any]        =							val
         UpperCamelCase_:   List[str]        =							(start + end) // 2
         UpperCamelCase_:   List[str]        =							left
         UpperCamelCase_:   List[Any]        =							right





       def __repr__(   self				):
         return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''







class 							_lowerCAmelCase:





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   str        =							collection
         UpperCamelCase_:   Optional[Any]        =							function
         if self.collection:
           UpperCamelCase_:   str        =							self._build_tree(0    ,     len(_lowerCamelCase				) - 1				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         self._update_tree(self.root    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         return self._query_range(self.root    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         if start == end:
           return SegmentTreeNode(_lowerCamelCase    ,     _lowerCamelCase    ,     self.collection[start]				)
         UpperCamelCase_:   int        =							(start + end) // 2
         UpperCamelCase_:   List[str]        =							self._build_tree(_lowerCamelCase    ,     _lowerCamelCase				)
         UpperCamelCase_:   Any        =							self._build_tree(mid + 1    ,     _lowerCamelCase				)
         return SegmentTreeNode(_lowerCamelCase    ,     _lowerCamelCase    ,     self.fn(left.val    ,     right.val				)    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         if node.start == i and node.end == i:
           UpperCamelCase_:   List[Any]        =							val
           return
         if i <= node.mid:
           self._update_tree(node.left    ,     _lowerCamelCase    ,     _lowerCamelCase				)
         else:
           self._update_tree(node.right    ,     _lowerCamelCase    ,     _lowerCamelCase				)
         UpperCamelCase_:   Any        =							self.fn(node.left.val    ,     node.right.val				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         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    ,     _lowerCamelCase    ,     _lowerCamelCase				)
           else:
             # range in left child tree and right child tree
             return self.fn(
                 self._query_range(node.left    ,     _lowerCamelCase    ,     node.mid				)    ,     self._query_range(node.right    ,     node.mid + 1    ,     _lowerCamelCase				)    ,     )
         else:
           # range in right child tree
           return self._query_range(node.right    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self				):
         if self.root is not None:
           UpperCamelCase_:   Union[str, Any]        =							Queue()
           queue.put(self.root				)
           while not queue.empty():
             UpperCamelCase_:   List[str]        =							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)
      A_						:						List[str]						      =					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()




import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
				import torch

				from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class     __a			:



	def __init__(    self				:     Union[str, Any]					,						snake_case_				:     Dict					,						snake_case_				:     Tuple=None					,						snake_case_				:     List[str]=None					,						snake_case_				:     Union[str, Any]=None					,						snake_case_				:     str="resnet50"					,						snake_case_				:     List[Any]=3					,						snake_case_				:     Optional[int]=32					,						snake_case_				:     Union[str, Any]=3					,						snake_case_				:     Tuple=True					,						snake_case_				:     List[str]=True					,						)->				Optional[Any]:
						__lowerCAmelCase						 =parent
						__lowerCAmelCase						 =out_indices if out_indices is not None else [4]
						__lowerCAmelCase						 =stage_names
						__lowerCAmelCase						 =out_features
						__lowerCAmelCase						 =backbone
						__lowerCAmelCase						 =batch_size
						__lowerCAmelCase						 =image_size
						__lowerCAmelCase						 =num_channels
						__lowerCAmelCase						 =use_pretrained_backbone
						__lowerCAmelCase						 =is_training



	def 		UpperCamelCase				(    self				:     int)->				Dict:
						__lowerCAmelCase						 =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
						__lowerCAmelCase						 =self.get_config()

						return config, pixel_values



	def 		UpperCamelCase				(    self				:     Optional[int])->				Optional[int]:
						return TimmBackboneConfig(
						    image_size=self.image_size					,						num_channels=self.num_channels					,						out_features=self.out_features					,						out_indices=self.out_indices					,						stage_names=self.stage_names					,						use_pretrained_backbone=self.use_pretrained_backbone					,						backbone=self.backbone					,						)



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     int					,						snake_case_				:     Union[str, Any])->				str:
						__lowerCAmelCase						 =TimmBackbone(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						with torch.no_grad():
											__lowerCAmelCase						 =model(snake_case_)
						self.parent.assertEqual(
						    result.feature_map[-1].shape					,						(self.batch_size, model.channels[-1], 14, 14)					,						)



	def 		UpperCamelCase				(    self				:     List[str])->				Union[str, Any]:
						__lowerCAmelCase						 =self.prepare_config_and_inputs()
						__lowerCAmelCase ,						__lowerCAmelCase						 =config_and_inputs
						__lowerCAmelCase						 ={"""pixel_values""": pixel_values}
						return config, inputs_dict






@require_torch
@require_timm
class     __a			(  SCREAMING_SNAKE_CASE						,     SCREAMING_SNAKE_CASE						,     SCREAMING_SNAKE_CASE						,     unittest.TestCase					):
	SCREAMING_SNAKE_CASE  	=							(TimmBackbone,) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE  	=							{"feature-extraction": TimmBackbone} if is_torch_available() else {}
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False



	def 		UpperCamelCase				(    self				:     Union[str, Any])->				str:
						__lowerCAmelCase						 =TimmBackboneModelTester(self)
						__lowerCAmelCase						 =ConfigTester(self					,						config_class=snake_case_					,						has_text_modality=snake_case_)



	def 		UpperCamelCase				(    self				:     Tuple)->				Optional[Any]:
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()



	def 		UpperCamelCase				(    self				:     Any)->				Dict:
						__lowerCAmelCase						 ="""resnet18"""
						__lowerCAmelCase						 ="""microsoft/resnet-18"""

						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_					,						use_timm_backbone=snake_case_)
						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_)

						self.assertEqual(len(timm_model.out_features)					,						len(transformers_model.out_features))
						self.assertEqual(len(timm_model.stage_names)					,						len(transformers_model.stage_names))
						self.assertEqual(timm_model.channels					,						transformers_model.channels)
						# Out indices are set to the last layer by default. For timm models, we don't know
						# the number of layers in advance, so we set it to (-1,), whereas for transformers
						# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
						self.assertEqual(timm_model.out_indices					,						(-1,))
						self.assertEqual(transformers_model.out_indices					,						[len(timm_model.stage_names) - 1])

						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_					,						use_timm_backbone=snake_case_					,						out_indices=[1, 2, 3])
						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_					,						out_indices=[1, 2, 3])

						self.assertEqual(timm_model.out_indices					,						transformers_model.out_indices)
						self.assertEqual(len(timm_model.out_features)					,						len(transformers_model.out_features))
						self.assertEqual(timm_model.channels					,						transformers_model.channels)



	@unittest.skip("""TimmBackbone doesn't support feed forward chunking""")
	def 		UpperCamelCase				(    self				:     Dict)->				Any:
						pass



	@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute""")
	def 		UpperCamelCase				(    self				:     Tuple)->				Dict:
						pass



	@unittest.skip("""TimmBackbone initialization is managed on the timm side""")
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				List[str]:
						pass



	@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
	def 		UpperCamelCase				(    self				:     List[str])->				List[str]:
						pass



	@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
	def 		UpperCamelCase				(    self				:     int)->				int:
						pass



	@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint""")
	def 		UpperCamelCase				(    self				:     Dict)->				int:
						pass



	@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
	def 		UpperCamelCase				(    self				:     Any)->				Dict:
						pass



	@unittest.skip("""model weights aren't tied in TimmBackbone.""")
	def 		UpperCamelCase				(    self				:     int)->				Tuple:
						pass



	@unittest.skip("""model weights aren't tied in TimmBackbone.""")
	def 		UpperCamelCase				(    self				:     Optional[int])->				Union[str, Any]:
						pass



	@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
	def 		UpperCamelCase				(    self				:     Optional[Any])->				Optional[Any]:
						pass



	@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
	def 		UpperCamelCase				(    self				:     Any)->				List[Any]:
						pass



	@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration.""")
	def 		UpperCamelCase				(    self				:     Any)->				Tuple:
						pass



	@unittest.skip("""TimmBackbone doesn't support output_attentions.""")
	def 		UpperCamelCase				(    self				:     List[Any])->				str:
						pass



	@unittest.skip("""Safetensors is not supported by timm.""")
	def 		UpperCamelCase				(    self				:     Dict)->				Optional[int]:
						pass



	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
	def 		UpperCamelCase				(    self				:     Optional[int])->				Tuple:
						pass



	def 		UpperCamelCase				(    self				:     int)->				Union[str, Any]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__lowerCAmelCase						 =model_class(snake_case_)
											__lowerCAmelCase						 =inspect.signature(model.forward)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__lowerCAmelCase						 =[*signature.parameters.keys()]

											__lowerCAmelCase						 =["""pixel_values"""]
											self.assertListEqual(arg_names[:1]					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Dict)->				Optional[Any]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCAmelCase						 =True
						__lowerCAmelCase						 =self.has_attentions

						# no need to test all models as different heads yield the same functionality
						__lowerCAmelCase						 =self.all_model_classes[0]
						__lowerCAmelCase						 =model_class(snake_case_)
						model.to(snake_case_)

						__lowerCAmelCase						 =self._prepare_for_class(snake_case_					,						snake_case_)
						__lowerCAmelCase						 =model(**snake_case_)
						__lowerCAmelCase						 =outputs[0][-1]

						# Encoder-/Decoder-only models
						__lowerCAmelCase						 =outputs.hidden_states[0]
						hidden_states.retain_grad()

						if self.has_attentions:
											__lowerCAmelCase						 =outputs.attentions[0]
											attentions.retain_grad()

						output.flatten()[0].backward(retain_graph=snake_case_)

						self.assertIsNotNone(hidden_states.grad)

						if self.has_attentions:
											self.assertIsNotNone(attentions.grad)



	def 		UpperCamelCase				(    self				:     Tuple)->				List[Any]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__lowerCAmelCase						 =model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											__lowerCAmelCase						 =model(**snake_case_)

											self.assertEqual(len(result.feature_maps)					,						len(config.out_indices))
											self.assertEqual(len(model.channels)					,						len(config.out_indices))

											# Check output of last stage is taken if out_features=None, out_indices=None
											__lowerCAmelCase						 =copy.deepcopy(snake_case_)
											__lowerCAmelCase						 =None
											__lowerCAmelCase						 =model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											__lowerCAmelCase						 =model(**snake_case_)

											self.assertEqual(len(result.feature_maps)					,						1)
											self.assertEqual(len(model.channels)					,						1)

											# Check backbone can be initialized with fresh weights
											__lowerCAmelCase						 =copy.deepcopy(snake_case_)
											__lowerCAmelCase						 =False
											__lowerCAmelCase						 =model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											__lowerCAmelCase						 =model(**snake_case_)




