

"""simple docstring"""





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 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					def __init__(       self			:				List[Any]   ,lowercase_			:				List[str]   ,lowercase_			:				Any=None   ,lowercase_			:				Any=None   ,lowercase_			:				Optional[int]=None   ,lowercase_			:				Optional[int]="resnet50"   ,lowercase_			:				str=3   ,lowercase_			:				List[str]=3_2   ,lowercase_			:				Any=3   ,lowercase_			:				Dict=True   ,lowercase_			:				Union[str, Any]=True   ,):
						lowerCAmelCase__	:						List[str]  				=	parent
						lowerCAmelCase__	:						List[str]  				=	out_indices if out_indices is not None else [4]
						lowerCAmelCase__	:						List[str]  				=	stage_names
						lowerCAmelCase__	:						Optional[int]  				=	out_features
						lowerCAmelCase__	:						int  				=	backbone
						lowerCAmelCase__	:						List[Any]  				=	batch_size
						lowerCAmelCase__	:						str  				=	image_size
						lowerCAmelCase__	:						int  				=	num_channels
						lowerCAmelCase__	:						Tuple  				=	use_pretrained_backbone
						lowerCAmelCase__	:						Dict  				=	is_training







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Dict  				=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
						lowerCAmelCase__	:						Dict  				=	self.get_config()

						return config, pixel_values







					def        __lowerCAmelCase (       self			:				List[str]		):
						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        __lowerCAmelCase (       self			:				int   ,lowercase_			:				str   ,lowercase_			:				Dict		):
						lowerCAmelCase__	:						int  				=	TimmBackbone(config=lowercase_		)
						model.to(lowercase_		)
						model.eval()
						with torch.no_grad():
							lowerCAmelCase__	:						Union[str, Any]  				=	model(lowercase_		)
						self.parent.assertEqual(
						    result.feature_map[-1].shape   ,(self.batch_size, model.channels[-1], 1_4, 1_4)   ,)







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						int  				=	self.prepare_config_and_inputs()
						lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	config_and_inputs
						lowerCAmelCase__	:						Dict  				=	{'''pixel_values''': pixel_values}
						return config, inputs_dict







@require_torch
@require_timm
class 				SCREAMING_SNAKE_CASE					(		a_    ,							a_    ,							a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       (TimmBackbone,) if is_torch_available() else ()
					lowercase__				 =       {"feature-extraction": TimmBackbone} if is_torch_available() else {}
					lowercase__				 =       False
					lowercase__				 =       False
					lowercase__				 =       False
					lowercase__				 =       False







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						List[Any]  				=	TimmBackboneModelTester(self		)
						lowerCAmelCase__	:						str  				=	ConfigTester(self   ,config_class=lowercase_   ,has_text_modality=lowercase_		)







					def        __lowerCAmelCase (       self			:				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        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						List[str]  				=	'''resnet18'''
						lowerCAmelCase__	:						List[str]  				=	'''microsoft/resnet-18'''

						lowerCAmelCase__	:						Tuple  				=	AutoBackbone.from_pretrained(lowercase_   ,use_timm_backbone=lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	AutoBackbone.from_pretrained(lowercase_		)

						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__	:						List[str]  				=	AutoBackbone.from_pretrained(lowercase_   ,use_timm_backbone=lowercase_   ,out_indices=[1, 2, 3]		)
						lowerCAmelCase__	:						List[str]  				=	AutoBackbone.from_pretrained(lowercase_   ,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        __lowerCAmelCase (       self			:				Optional[int]		):
						pass







					@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''		)
					def        __lowerCAmelCase (       self			:				List[Any]		):
						pass







					@unittest.skip('''TimmBackbone initialization is managed on the timm side'''		)
					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''		)
					def        __lowerCAmelCase (       self			:				Dict		):
						pass







					@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''		)
					def        __lowerCAmelCase (       self			:				List[Any]		):
						pass







					@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''		)
					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						pass







					@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''		)
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						pass







					@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''		)
					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						pass







					@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''		)
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						pass







					@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''		)
					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''		)
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						pass







					@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''		)
					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''		)
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						pass







					@unittest.skip('''Safetensors is not supported by timm.'''		)
					def        __lowerCAmelCase (       self			:				List[Any]		):
						pass







					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
					def        __lowerCAmelCase (       self			:				List[Any]		):
						pass







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	self.model_tester.prepare_config_and_inputs_for_common()

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

							lowerCAmelCase__	:						Union[str, Any]  				=	['''pixel_values''']
							self.assertListEqual(arg_names[:1]   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase__	:						Optional[int]  				=	True
						lowerCAmelCase__	:						Union[str, Any]  				=	self.has_attentions

						# no need to test all models as different heads yield the same functionality
						lowerCAmelCase__	:						List[Any]  				=	self.all_model_classes[0]
						lowerCAmelCase__	:						List[str]  				=	model_class(lowercase_		)
						model.to(lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	self._prepare_for_class(lowercase_   ,lowercase_		)
						lowerCAmelCase__	:						Any  				=	model(**lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	outputs[0][-1]

						# Encoder-/Decoder-only models
						lowerCAmelCase__	:						List[Any]  				=	outputs.hidden_states[0]
						hidden_states.retain_grad()

						if self.has_attentions:
							lowerCAmelCase__	:						Optional[Any]  				=	outputs.attentions[0]
							attentions.retain_grad()

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

						self.assertIsNotNone(hidden_states.grad		)

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







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							lowerCAmelCase__	:						Dict  				=	model_class(lowercase_		)
							model.to(lowercase_		)
							model.eval()
							lowerCAmelCase__	:						Tuple  				=	model(**lowercase_		)

							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__	:						List[str]  				=	copy.deepcopy(lowercase_		)
							lowerCAmelCase__	:						Dict  				=	None
							lowerCAmelCase__	:						Optional[int]  				=	model_class(lowercase_		)
							model.to(lowercase_		)
							model.eval()
							lowerCAmelCase__	:						Optional[Any]  				=	model(**lowercase_		)

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

							# Check backbone can be initialized with fresh weights
							lowerCAmelCase__	:						List[str]  				=	copy.deepcopy(lowercase_		)
							lowerCAmelCase__	:						int  				=	False
							lowerCAmelCase__	:						Dict  				=	model_class(lowercase_		)
							model.to(lowercase_		)
							model.eval()
							lowerCAmelCase__	:						Any  				=	model(**lowercase_		)







"""simple docstring"""





import random

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	[], [], []
	for element in data:
		if element < pivot:
			less.append(A_ )
		elif element > pivot:
			greater.append(A_ )
		else:
			equal.append(A_ )
	return less, equal, greater





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	# index = len(items) // 2 when trying to find the median
	#   (value of index when items is sorted)

	# invalid input
	if index >= len(A_ ) or index < 0:
		return None

	lowerCAmelCase__	:						str  				=	items[random.randint(0						,     len(A_ ) - 1 )]
	lowerCAmelCase__	:						Optional[Any]  				=	0
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	_partition(A_						,     A_ )
	lowerCAmelCase__	:						str  				=	len(A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	len(A_ )

	# index is the pivot
	if m <= index < m + count:
		return pivot
	# must be in smaller
	elif m > index:
		return quick_select(A_						,     A_ )
	# must be in larger
	else:
		return quick_select(A_						,     index - (m + count) )




