





"""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 					_lowerCAmelCase   :
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase=None    , __UpperCAmelCase=None    , __UpperCAmelCase=None    , __UpperCAmelCase="resnet50"    , __UpperCAmelCase=3    , __UpperCAmelCase=3_2    , __UpperCAmelCase=3    , __UpperCAmelCase=True    , __UpperCAmelCase=True    , ):



         '''simple docstring'''

         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      snake_case						(    self      ):



         '''simple docstring'''

         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      snake_case						(    self      ):



         '''simple docstring'''

         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      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         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      snake_case						(    self      ):



         '''simple docstring'''

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






@require_torch
@require_timm
class 					_lowerCAmelCase   (     a_  ,   a_  ,   a_  ,   unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :Optional[int]		  =					(TimmBackbone,) if is_torch_available() else ()
       __magic_name__     :Optional[Any]		  =					{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
       __magic_name__     :Any		  =					False
       __magic_name__     :Tuple		  =					False
       __magic_name__     :Optional[Any]		  =					False
       __magic_name__     :Dict		  =					False






       def      snake_case						(    self      ):



         '''simple docstring'''

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         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      snake_case						(    self      ):



         '''simple docstring'''

         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      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @unittest.skip('TimmBackbone initialization is managed on the timm side'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






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



         '''simple docstring'''

         pass






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



         '''simple docstring'''

         pass






       @unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @unittest.skip('model weights aren\'t tied in TimmBackbone.'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @unittest.skip('model weights aren\'t tied in TimmBackbone.'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @unittest.skip('TimmBackbone doesn\'t support output_attentions.'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @unittest.skip('Safetensors is not supported by timm.'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         pass






       def      snake_case						(    self      ):



         '''simple docstring'''

         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      snake_case						(    self      ):



         '''simple docstring'''

         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      snake_case						(    self      ):



         '''simple docstring'''

         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) )




