





"""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 							lowercase__			:

			"""simple docstring"""







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






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			parent
									UpperCamelCase				:		Optional[Any]				   =			out_indices if out_indices is not None else [4]
									UpperCamelCase				:		int				   =			stage_names
									UpperCamelCase				:		List[str]				   =			out_features
									UpperCamelCase				:		List[str]				   =			backbone
									UpperCamelCase				:		Tuple				   =			batch_size
									UpperCamelCase				:		Optional[int]				   =			image_size
									UpperCamelCase				:		Optional[Any]				   =			num_channels
									UpperCamelCase				:		List[Any]				   =			use_pretrained_backbone
									UpperCamelCase				:		Tuple				   =			is_training







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
									UpperCamelCase				:		Dict				   =			self.get_config()

									return config, pixel_values







			def   _a      (      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   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			TimmBackbone(config=_A    )
									model.to(_A    )
									model.eval()
									with torch.no_grad():
															UpperCamelCase				:		str				   =			model(_A    )
									self.parent.assertEqual(
									    result.feature_map[-1].shape						,   (self.batch_size, model.channels[-1], 1_4, 1_4)						,   )







			def   _a      (      self    ):






									'''simple docstring'''







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






@require_torch
@require_timm
class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     unittest.TestCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				Union[str, Any]										=  (TimmBackbone,) if is_torch_available() else ()
			__lowerCAmelCase     :				Union[str, Any]										=  {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
			__lowerCAmelCase     :				str										=  False
			__lowerCAmelCase     :				Union[str, Any]										=  False
			__lowerCAmelCase     :				Union[str, Any]										=  False
			__lowerCAmelCase     :				Optional[int]										=  False







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			TimmBackboneModelTester(self    )
									UpperCamelCase				:		int				   =			ConfigTester(self						,   config_class=_A						,   has_text_modality=_A    )







			def   _a      (      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   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			"""resnet18"""
									UpperCamelCase				:		List[Any]				   =			"""microsoft/resnet-18"""

									UpperCamelCase				:		List[Any]				   =			AutoBackbone.from_pretrained(_A						,   use_timm_backbone=_A    )
									UpperCamelCase				:		Tuple				   =			AutoBackbone.from_pretrained(_A    )

									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]    )

									UpperCamelCase				:		Any				   =			AutoBackbone.from_pretrained(_A						,   use_timm_backbone=_A						,   out_indices=[1, 2, 3]    )
									UpperCamelCase				:		List[str]				   =			AutoBackbone.from_pretrained(_A						,   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   _a      (      self    ):






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







			@unittest.skip("""TimmBackbone doesn't support output_attentions."""    )
			def   _a      (      self    ):






									'''simple docstring'''







									pass







			@unittest.skip("""Safetensors is not supported by timm."""    )
			def   _a      (      self    ):






									'''simple docstring'''







									pass







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






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase   ,						UpperCamelCase				:		Dict				   =			self.model_tester.prepare_config_and_inputs_for_common()

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

															UpperCamelCase				:		Dict				   =			["""pixel_values"""]
															self.assertListEqual(arg_names[:1]						,   _A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase				:		List[str]				   =			True
									UpperCamelCase				:		Optional[int]				   =			self.has_attentions

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

									UpperCamelCase				:		Union[str, Any]				   =			self._prepare_for_class(_A						,   _A    )
									UpperCamelCase				:		Optional[int]				   =			model(**_A    )
									UpperCamelCase				:		str				   =			outputs[0][-1]

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

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

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

									self.assertIsNotNone(hidden_states.grad    )

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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															UpperCamelCase				:		Optional[Any]				   =			model_class(_A    )
															model.to(_A    )
															model.eval()
															UpperCamelCase				:		List[Any]				   =			model(**_A    )

															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
															UpperCamelCase				:		List[Any]				   =			copy.deepcopy(_A    )
															UpperCamelCase				:		Optional[int]				   =			None
															UpperCamelCase				:		Dict				   =			model_class(_A    )
															model.to(_A    )
															model.eval()
															UpperCamelCase				:		Tuple				   =			model(**_A    )

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

															# Check backbone can be initialized with fresh weights
															UpperCamelCase				:		Optional[int]				   =			copy.deepcopy(_A    )
															UpperCamelCase				:		Optional[int]				   =			False
															UpperCamelCase				:		Optional[int]				   =			model_class(_A    )
															model.to(_A    )
															model.eval()
															UpperCamelCase				:		Union[str, Any]				   =			model(**_A    )










"""simple docstring"""


from bisect import bisect
from itertools import accumulate

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Dict				   =			sorted(zip(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )							,     key=lambda SCREAMING_SNAKE_CASE     : x[0] / x[1]							,     reverse=SCREAMING_SNAKE_CASE     )
						UpperCamelCase   ,						UpperCamelCase				:		int				   =			[i[0] for i in r], [i[1] for i in r]
						UpperCamelCase				:		Optional[Any]				   =			list(accumulate(SCREAMING_SNAKE_CASE     )     )
						UpperCamelCase				:		Optional[Any]				   =			bisect(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						return (
						    0
						    if k == 0
						    else sum(vl[:k]     ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
						    if k != n
						    else sum(vl[:k]     )
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()



