


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__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase="resnet50"		,     UpperCAmelCase=3		,     UpperCAmelCase=3_2		,     UpperCAmelCase=3		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     )     ->   str:
      _lowercase							      =parent
      _lowercase							      =out_indices if out_indices is not None else [4]
      _lowercase							      =stage_names
      _lowercase							      =out_features
      _lowercase							      =backbone
      _lowercase							      =batch_size
      _lowercase							      =image_size
      _lowercase							      =num_channels
      _lowercase							      =use_pretrained_backbone
      _lowercase							      =is_training





     def      __A					(self       )     ->   Tuple:
      _lowercase							      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
      _lowercase							      =self.get_config()

      return config, pixel_values





     def      __A					(self       )     ->   Optional[Any]:
      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		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   int:
      _lowercase							      =TimmBackbone(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      with torch.no_grad():
       _lowercase							      =model(UpperCAmelCase       )
      self.parent.assertEqual(
          result.feature_map[-1].shape		,     (self.batch_size, model.channels[-1], 1_4, 1_4)		,     )





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =self.prepare_config_and_inputs()
      _lowercase      ,    _lowercase							      =config_and_inputs
      _lowercase							      ={'''pixel_values''': pixel_values}
      return config, inputs_dict







@require_torch
@require_timm
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(TimmBackbone,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      			=			{'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =TimmBackboneModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     has_text_modality=UpperCAmelCase       )





     def      __A					(self       )     ->   Tuple:
      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       )     ->   Optional[Any]:
      _lowercase							      ='''resnet18'''
      _lowercase							      ='''microsoft/resnet-18'''

      _lowercase							      =AutoBackbone.from_pretrained(UpperCAmelCase		,     use_timm_backbone=UpperCAmelCase       )
      _lowercase							      =AutoBackbone.from_pretrained(UpperCAmelCase       )

      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]       )

      _lowercase							      =AutoBackbone.from_pretrained(UpperCAmelCase		,     use_timm_backbone=UpperCAmelCase		,     out_indices=[1, 2, 3]       )
      _lowercase							      =AutoBackbone.from_pretrained(UpperCAmelCase		,     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       )     ->   Optional[Any]:
      pass





     @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''       )
     def      __A					(self       )     ->   Tuple:
      pass





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





     @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''       )
     def      __A					(self       )     ->   Tuple:
      pass





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





     @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''       )
     def      __A					(self       )     ->   Any:
      pass





     @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''       )
     def      __A					(self       )     ->   Optional[Any]:
      pass





     @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''       )
     def      __A					(self       )     ->   Tuple:
      pass





     @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''       )
     def      __A					(self       )     ->   int:
      pass





     @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''       )
     def      __A					(self       )     ->   Tuple:
      pass





     @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''       )
     def      __A					(self       )     ->   Tuple:
      pass





     @unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''       )
     def      __A					(self       )     ->   int:
      pass





     @unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''       )
     def      __A					(self       )     ->   List[str]:
      pass





     @unittest.skip('''Safetensors is not supported by timm.'''       )
     def      __A					(self       )     ->   Dict:
      pass





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





     def      __A					(self       )     ->   str:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()

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

       _lowercase							      =['''pixel_values''']
       self.assertListEqual(arg_names[:1]		,     UpperCAmelCase       )





     def      __A					(self       )     ->   Union[str, Any]:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()
      _lowercase							      =True
      _lowercase							      =self.has_attentions

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

      _lowercase							      =self._prepare_for_class(UpperCAmelCase		,     UpperCAmelCase       )
      _lowercase							      =model(**UpperCAmelCase       )
      _lowercase							      =outputs[0][-1]

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

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

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

      self.assertIsNotNone(hidden_states.grad       )

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





     def      __A					(self       )     ->   str:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       _lowercase							      =model_class(UpperCAmelCase       )
       model.to(UpperCAmelCase       )
       model.eval()
       _lowercase							      =model(**UpperCAmelCase       )

       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
       _lowercase							      =copy.deepcopy(UpperCAmelCase       )
       _lowercase							      =None
       _lowercase							      =model_class(UpperCAmelCase       )
       model.to(UpperCAmelCase       )
       model.eval()
       _lowercase							      =model(**UpperCAmelCase       )

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

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






'''simple docstring'''




import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def       __lowerCamelCase  (					)      ->			Any:
							raise RuntimeError("""CUDA out of memory."""		)

class 			UpperCamelCase_ (							nn.Module			):





	def __init__(  self    )			->			Any:
								super().__init__()
								UpperCAmelCase     :		Tuple							     =		nn.Linear(3		,		4    )
								UpperCAmelCase     :		Tuple							     =		nn.BatchNormad(4    )
								UpperCAmelCase     :		int							     =		nn.Linear(4		,		5    )






	def  _lowercase(  self		,		A    )			->			Any:
								return self.lineara(self.batchnorm(self.lineara(A    )    )    )




class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		mock_training_loop_function("""hello"""    )
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )
								self.assertListEqual([bs, arga]		,		[8, """hello"""]    )





	def  _lowercase(  self    )			->			Any:
								@find_executable_batch_size(starting_batch_size=0    )
								def mock_training_loop_function(A    ):
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[int]:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															if batch_size > 0:
																						raise_fake_out_of_memory()
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[Any]:
								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A		,		A    ):
															if batch_size != 8:
																						raise raise_fake_out_of_memory()

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function(128		,		"""hello"""		,		"""world"""    )
															self.assertIn("""Batch size was passed into `f`"""		,		cm.exception.args[0]    )
															self.assertIn("""`f(arg1='hello', arg2='world')"""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			int:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															raise ValueError("""Oops, we had an error!"""    )

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""Oops, we had an error!"""		,		cm.exception.args[0]    )






	@require_cuda
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.cuda.memory_allocated()
								UpperCAmelCase     :		List[str]							     =		ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()		,		A    )
								UpperCAmelCase     :		Tuple							     =		release_memory(A    )
								self.assertEqual(torch.cuda.memory_allocated()		,		A    )

