






"""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						:


							def __init__(			self      :						List[Any]			,    snake_case      :						Any			,    snake_case      :						Union[str, Any]=None			,    snake_case      :						Optional[Any]=None			,    snake_case      :						List[str]=None			,    snake_case      :						str="resnet50"			,    snake_case      :						str=3			,    snake_case      :						Tuple=32			,    snake_case      :						Optional[Any]=3			,    snake_case      :						Tuple=True			,    snake_case      :						Tuple=True			,    ):
									'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
									'''simple docstring'''





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

									return config, pixel_values


							def 						_UpperCamelCase       (			self      :						List[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 						_UpperCamelCase       (			self      :						int			,    snake_case      :						Dict			,    snake_case      :						str							):
									'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						Any							):
									'''simple docstring'''





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






@require_torch
@require_timm
class 							__SCREAMING_SNAKE_CASE						(				_A       ,		_A       ,		_A       ,		unittest.TestCase		):
							snake_case_     =							(TimmBackbone,) if is_torch_available() else ()
							snake_case_     =							{'feature-extraction': TimmBackbone} if is_torch_available() else {}
							snake_case_     =							False
							snake_case_     =							False
							snake_case_     =							False
							snake_case_     =							False


							def 						_UpperCamelCase       (			self      :						Tuple							):
									'''simple docstring'''





									A__						:  List[Any]		  =  TimmBackboneModelTester(self							)
									A__						:  Optional[Any]		  =  ConfigTester(self			,    config_class=snake_case			,    has_text_modality=snake_case							)


							def 						_UpperCamelCase       (			self      :						List[Any]							):
									'''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 						_UpperCamelCase       (			self      :						List[Any]							):
									'''simple docstring'''





									A__						:  Optional[Any]		  =  "resnet18"
									A__						:  int		  =  "microsoft/resnet-18"

									A__						:  List[Any]		  =  AutoBackbone.from_pretrained(snake_case			,    use_timm_backbone=snake_case							)
									A__						:  List[Any]		  =  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]							)

									A__						:  str		  =  AutoBackbone.from_pretrained(snake_case			,    use_timm_backbone=snake_case			,    out_indices=[1, 2, 3]							)
									A__						:  str		  =  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      :						List[Any]							):
									'''simple docstring'''





									pass


							@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""							)
							def 						_UpperCamelCase       (			self      :						Dict							):
									'''simple docstring'''





									pass


							@unittest.skip("""TimmBackbone initialization is managed on the timm side"""							)
							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
									'''simple docstring'''





									pass


							@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""							)
							def 						_UpperCamelCase       (			self      :						Any							):
									'''simple docstring'''





									pass


							@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""							)
							def 						_UpperCamelCase       (			self      :						List[str]							):
									'''simple docstring'''





									pass


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





									pass


							@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""							)
							def 						_UpperCamelCase       (			self      :						Optional[int]							):
									'''simple docstring'''





									pass


							@unittest.skip("""model weights aren't tied in TimmBackbone."""							)
							def 						_UpperCamelCase       (			self      :						Any							):
									'''simple docstring'''





									pass


							@unittest.skip("""model weights aren't tied in TimmBackbone."""							)
							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
									'''simple docstring'''





									pass


							@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""							)
							def 						_UpperCamelCase       (			self      :						List[Any]							):
									'''simple docstring'''





									pass


							@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""							)
							def 						_UpperCamelCase       (			self      :						List[Any]							):
									'''simple docstring'''





									pass


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





									pass


							@unittest.skip("""TimmBackbone doesn't support output_attentions."""							)
							def 						_UpperCamelCase       (			self      :						str							):
									'''simple docstring'''





									pass


							@unittest.skip("""Safetensors is not supported by timm."""							)
							def 						_UpperCamelCase       (			self      :						Dict							):
									'''simple docstring'''





									pass


							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
									'''simple docstring'''





									pass


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
									'''simple docstring'''





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

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

											A__						:  Optional[Any]		  =  ["pixel_values"]
											self.assertListEqual(arg_names[:1]			,    snake_case							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
									'''simple docstring'''





									A__						:  Optional[Any]		  =  self.model_tester.prepare_config_and_inputs_for_common()
									A__						:  Any		  =  True
									A__						:  str		  =  self.has_attentions

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

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

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

									if self.has_attentions:
											A__						:  Optional[Any]		  =  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      :						Union[str, Any]							):
									'''simple docstring'''





									A__						:  Tuple		  =  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											A__						:  List[str]		  =  model_class(snake_case							)
											model.to(snake_case							)
											model.eval()
											A__						:  Tuple		  =  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
											A__						:  Optional[Any]		  =  copy.deepcopy(snake_case							)
											A__						:  List[str]		  =  None
											A__						:  List[Any]		  =  model_class(snake_case							)
											model.to(snake_case							)
											model.eval()
											A__						:  List[Any]		  =  model(**snake_case							)

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

											# Check backbone can be initialized with fresh weights
											A__						:  Tuple		  =  copy.deepcopy(snake_case							)
											A__						:  Tuple		  =  False
											A__						:  Tuple		  =  model_class(snake_case							)
											model.to(snake_case							)
											model.eval()
											A__						:  Optional[int]		  =  model(**snake_case							)














"""simple docstring"""



import baseaa




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->bytes:
						return baseaa.baaencode(string.encode("""utf-8"""      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					bytes      )		->str:
						return baseaa.baadecode(UpperCAmelCase__      ).decode("""utf-8"""      )


if __name__ == "__main__":
						A_    =							'''Hello World!'''
						A_    =							baseaa_encode(test)
						print(encoded)

						A_    =							baseaa_decode(encoded)
						print(decoded)






