





'''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  _A :

		def __init__(   self					:   Tuple  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Optional[Any]=None  ,      __magic_name__					:   Dict=None  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   int="resnet50"  ,      __magic_name__					:   Dict=3  ,      __magic_name__					:   Union[str, Any]=32  ,      __magic_name__					:   Union[str, Any]=3  ,      __magic_name__					:   Dict=True  ,      __magic_name__					:   List[str]=True  ,      )					->    Tuple:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Tuple			)					->    str:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
					__snake_case					:      List[Any]							      =     self.get_config()

					return config, pixel_values

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Any:


					"""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 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   List[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     TimmBackbone(config=__magic_name__			)
					model.to(__magic_name__			)
					model.eval()
					with torch.no_grad():
								__snake_case					:      Union[str, Any]							      =     model(__magic_name__			)
					self.parent.assertEqual(
					    result.feature_map[-1].shape  ,      (self.batch_size, model.channels[-1], 14, 14)  ,      )

		def 	lowercase__     (   self					:   str			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      int							      =     self.prepare_config_and_inputs()
					__snake_case      ,     __snake_case					:      Dict							      =     config_and_inputs
					__snake_case					:      Any							      =     {"""pixel_values""": pixel_values}
					return config, inputs_dict





@require_torch
@require_timm
class  _A (					__lowercase						,					__lowercase						,					__lowercase						,					unittest.TestCase		):
		lowercase__:   str				     =   (TimmBackbone,) if is_torch_available() else ()
		lowercase__:   Optional[int]				     =   {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
		lowercase__:   Tuple				     =   False
		lowercase__:   List[str]				     =   False
		lowercase__:   Any				     =   False
		lowercase__:   Optional[int]				     =   False

		def 	lowercase__     (   self					:   Dict			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Dict							      =     TimmBackboneModelTester(self			)
					__snake_case					:      Tuple							      =     ConfigTester(self  ,      config_class=__magic_name__  ,      has_text_modality=__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    str:


					"""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 	lowercase__     (   self					:   Optional[int]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     """resnet18"""
					__snake_case					:      Union[str, Any]							      =     """microsoft/resnet-18"""

					__snake_case					:      Union[str, Any]							      =     AutoBackbone.from_pretrained(__magic_name__  ,      use_timm_backbone=__magic_name__			)
					__snake_case					:      Any							      =     AutoBackbone.from_pretrained(__magic_name__			)

					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]			)

					__snake_case					:      Union[str, Any]							      =     AutoBackbone.from_pretrained(__magic_name__  ,      use_timm_backbone=__magic_name__  ,      out_indices=[1, 2, 3]			)
					__snake_case					:      Tuple							      =     AutoBackbone.from_pretrained(__magic_name__  ,      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 	lowercase__     (   self					:   Dict			)					->    Dict:


					"""simple docstring"""

					pass

		@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""			)
		def 	lowercase__     (   self					:   str			)					->    List[str]:


					"""simple docstring"""

					pass

		@unittest.skip("""TimmBackbone initialization is managed on the timm side"""			)
		def 	lowercase__     (   self					:   Any			)					->    Optional[int]:


					"""simple docstring"""

					pass

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


					"""simple docstring"""

					pass

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


					"""simple docstring"""

					pass

		@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""			)
		def 	lowercase__     (   self					:   Dict			)					->    Optional[Any]:


					"""simple docstring"""

					pass

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


					"""simple docstring"""

					pass

		@unittest.skip("""model weights aren't tied in TimmBackbone."""			)
		def 	lowercase__     (   self					:   str			)					->    Any:


					"""simple docstring"""

					pass

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


					"""simple docstring"""

					pass

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


					"""simple docstring"""

					pass

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


					"""simple docstring"""

					pass

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


					"""simple docstring"""

					pass

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


					"""simple docstring"""

					pass

		@unittest.skip("""Safetensors is not supported by timm."""			)
		def 	lowercase__     (   self					:   Optional[Any]			)					->    Tuple:


					"""simple docstring"""

					pass

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


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   Optional[Any]			)					->    Dict:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      List[Any]							      =     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__snake_case					:      Optional[Any]							      =     model_class(__magic_name__			)
								__snake_case					:      Union[str, Any]							      =     inspect.signature(model.forward			)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								__snake_case					:      str							      =     [*signature.parameters.keys()]

								__snake_case					:      int							      =     ["""pixel_values"""]
								self.assertListEqual(arg_names[:1]  ,      __magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      List[Any]							      =     self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case					:      Tuple							      =     True
					__snake_case					:      List[Any]							      =     self.has_attentions

					# no need to test all models as different heads yield the same functionality
					__snake_case					:      Tuple							      =     self.all_model_classes[0]
					__snake_case					:      Dict							      =     model_class(__magic_name__			)
					model.to(__magic_name__			)

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

					# Encoder-/Decoder-only models
					__snake_case					:      Union[str, Any]							      =     outputs.hidden_states[0]
					hidden_states.retain_grad()

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

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

					self.assertIsNotNone(hidden_states.grad			)

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

		def 	lowercase__     (   self					:   List[str]			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Optional[int]							      =     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__snake_case					:      Optional[int]							      =     model_class(__magic_name__			)
								model.to(__magic_name__			)
								model.eval()
								__snake_case					:      Any							      =     model(**__magic_name__			)

								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
								__snake_case					:      Dict							      =     copy.deepcopy(__magic_name__			)
								__snake_case					:      Optional[Any]							      =     None
								__snake_case					:      int							      =     model_class(__magic_name__			)
								model.to(__magic_name__			)
								model.eval()
								__snake_case					:      int							      =     model(**__magic_name__			)

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

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







from __future__ import annotations

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase = None   )     ->     list[list[str]]:
					SCREAMING_SNAKE_CASE							:     Dict						=				word_bank or []
					# create a table
					SCREAMING_SNAKE_CASE							:     int						=				len(__lowerCAmelCase   ) + 1

					SCREAMING_SNAKE_CASE							:     list[list[list[str]]]						=				[]
					for _ in range(__lowerCAmelCase   ):
										table.append([]   )
					# seed value
					SCREAMING_SNAKE_CASE							:     Tuple						=				[[]]  # because empty string has empty combination

					# iterate through the indices
					for i in range(__lowerCAmelCase   ):
										# condition
										if table[i] != []:
															for word in word_bank:
																				# slice condition
																				if target[i : i + len(__lowerCAmelCase   )] == word:
																									SCREAMING_SNAKE_CASE							:     list[list[str]]						=				[
																									    [word, *way] for way in table[i]
																									]
																									# adds the word to every combination the current position holds
																									# now,push that combination to the table[i+len(word)]
																									table[i + len(__lowerCAmelCase   )] += new_combinations

    # combinations are in reverse order so reverse for better output
					for combination in table[len(__lowerCAmelCase   )]:
										combination.reverse()

					return table[len(__lowerCAmelCase   )]


if __name__ == "__main__":
			print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
			print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
			print(
			    all_construct(
			        """hexagonosaurus""",
			        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
			    )
			)