




'''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			:    Union[str, Any]				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    Optional[int]=None				,     lowerCAmelCase_			:    Any=None				,     lowerCAmelCase_			:    Optional[Any]="resnet50"				,     lowerCAmelCase_			:    Any=3				,     lowerCAmelCase_			:    Tuple=32				,     lowerCAmelCase_			:    Union[str, Any]=3				,     lowerCAmelCase_			:    Dict=True				,     lowerCAmelCase_			:    Optional[Any]=True				,     )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							parent
														_a      	=							out_indices if out_indices is not None else [4]
														_a      	=							stage_names
														_a      	=							out_features
														_a      	=							backbone
														_a      	=							batch_size
														_a      	=							image_size
														_a      	=							num_channels
														_a      	=							use_pretrained_backbone
														_a      	=							is_training







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


														"""simple docstring"""


														_a      	=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
														_a      	=							self.get_config()

														return config, pixel_values







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				str:


														"""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   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    Union[str, Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							TimmBackbone(config=lowerCAmelCase_   )
														model.to(lowerCAmelCase_   )
														model.eval()
														with torch.no_grad():
																					_a      	=							model(lowerCAmelCase_   )
														self.parent.assertEqual(
														    result.feature_map[-1].shape				,     (self.batch_size, model.channels[-1], 14, 14)				,     )







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.prepare_config_and_inputs()
														_a	,				_a      	=							config_and_inputs
														_a      	=							{'''pixel_values''': pixel_values}
														return config, inputs_dict



@require_torch
@require_timm
class      A       (						_a	,_a	,_a	,unittest.TestCase       ):
							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			:    List[str]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							TimmBackboneModelTester(self   )
														_a      	=							ConfigTester(self				,     config_class=lowerCAmelCase_				,     has_text_modality=lowerCAmelCase_   )







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


														"""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   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							'''resnet18'''
														_a      	=							'''microsoft/resnet-18'''

														_a      	=							AutoBackbone.from_pretrained(lowerCAmelCase_				,     use_timm_backbone=lowerCAmelCase_   )
														_a      	=							AutoBackbone.from_pretrained(lowerCAmelCase_   )

														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      	=							AutoBackbone.from_pretrained(lowerCAmelCase_				,     use_timm_backbone=lowerCAmelCase_				,     out_indices=[1, 2, 3]   )
														_a      	=							AutoBackbone.from_pretrained(lowerCAmelCase_				,     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]   )     ->				int:


														"""simple docstring"""


														pass







							@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''   )
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Optional[int]:


														"""simple docstring"""


														pass







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


														"""simple docstring"""


														pass







							@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''   )
							def   __lowerCAmelCase     (    self			:    str   )     ->				Any:


														"""simple docstring"""


														pass







							@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''   )
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														pass







							@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''   )
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Tuple:


														"""simple docstring"""


														pass







							@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''   )
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


														pass







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


														"""simple docstring"""


														pass







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


														"""simple docstring"""


														pass







							@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''   )
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														pass







							@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''   )
							def   __lowerCAmelCase     (    self			:    Any   )     ->				int:


														"""simple docstring"""


														pass







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


														"""simple docstring"""


														pass







							@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''   )
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Tuple:


														"""simple docstring"""


														pass







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


														"""simple docstring"""


														pass







							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Optional[int]:


														"""simple docstring"""


														pass







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Dict:


														"""simple docstring"""


														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()

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

																					_a      	=							['''pixel_values''']
																					self.assertListEqual(arg_names[:1]				,     lowerCAmelCase_   )







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


														"""simple docstring"""


														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()
														_a      	=							True
														_a      	=							self.has_attentions

														# no need to test all models as different heads yield the same functionality
														_a      	=							self.all_model_classes[0]
														_a      	=							model_class(lowerCAmelCase_   )
														model.to(lowerCAmelCase_   )

														_a      	=							self._prepare_for_class(lowerCAmelCase_				,     lowerCAmelCase_   )
														_a      	=							model(**lowerCAmelCase_   )
														_a      	=							outputs[0][-1]

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

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

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

														self.assertIsNotNone(hidden_states.grad   )

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







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Any:


														"""simple docstring"""


														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_a      	=							model_class(lowerCAmelCase_   )
																					model.to(lowerCAmelCase_   )
																					model.eval()
																					_a      	=							model(**lowerCAmelCase_   )

																					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      	=							copy.deepcopy(lowerCAmelCase_   )
																					_a      	=							None
																					_a      	=							model_class(lowerCAmelCase_   )
																					model.to(lowerCAmelCase_   )
																					model.eval()
																					_a      	=							model(**lowerCAmelCase_   )

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

																					# Check backbone can be initialized with fresh weights
																					_a      	=							copy.deepcopy(lowerCAmelCase_   )
																					_a      	=							False
																					_a      	=							model_class(lowerCAmelCase_   )
																					model.to(lowerCAmelCase_   )
																					model.eval()
																					_a      	=							model(**lowerCAmelCase_   )







'''simple docstring'''


import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def 						snake_case_       (UpperCamelCase			:    dict ):





							'''simple docstring'''


							return (data["data"], data["target"])
def 						snake_case_       (UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray ):





							'''simple docstring'''


							_a      	=							XGBRegressor(verbosity=0					,		random_state=42 )
							xgb.fit(UpperCamelCase					,		UpperCamelCase )
							# Predict target for test data
							_a      	=							xgb.predict(UpperCamelCase )
							_a      	=							predictions.reshape(len(UpperCamelCase )					,		1 )
							return predictions
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							fetch_california_housing()
							_a	,				_a      	=							data_handling(UpperCamelCase )
							_a	,				_a	,				_a	,				_a      	=							train_test_split(
							    UpperCamelCase					,		UpperCamelCase					,		test_size=0.25					,		random_state=1 )
							_a      	=							xgboost(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							# Error printing
							print(f'Mean Absolute Error : {mean_absolute_error(UpperCamelCase					,		UpperCamelCase )}' )
							print(f'Mean Square Error  : {mean_squared_error(UpperCamelCase					,		UpperCamelCase )}' )


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()

