





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     :






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     List[Any]							,	a				:     str=None							,	a				:     List[str]=None							,	a				:     Optional[int]=None							,	a				:     Union[str, Any]="resnet50"							,	a				:     Union[str, Any]=3							,	a				:     Any=32							,	a				:     str=3							,	a				:     Dict=True							,	a				:     Dict=True							,	)	->List[str]:
													SCREAMING_SNAKE_CASE__     :				int   =					parent
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					out_indices if out_indices is not None else [4]
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					stage_names
													SCREAMING_SNAKE_CASE__     :				Any   =					out_features
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					backbone
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					batch_size
													SCREAMING_SNAKE_CASE__     :				Any   =					image_size
													SCREAMING_SNAKE_CASE__     :				Any   =					num_channels
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					use_pretrained_backbone
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					is_training

							def      A_  (      self				:     Union[str, Any]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.get_config()

													return config, pixel_values

							def      A_  (      self				:     Tuple    )	->str:
													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				:     Dict							,	a				:     Optional[Any]							,	a				:     Any    )	->Any:
													SCREAMING_SNAKE_CASE__     :				int   =					TimmBackbone(config=a    )
													model.to(a    )
													model.eval()
													with torch.no_grad():
																			SCREAMING_SNAKE_CASE__     :				Dict   =					model(a    )
													self.parent.assertEqual(
													    result.feature_map[-1].shape							,	(self.batch_size, model.channels[-1], 14, 14)							,	)






							def      A_  (      self				:     List[str]    )	->int:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE__     :				int   =					config_and_inputs
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					{"pixel_values": pixel_values}
													return config, inputs_dict







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






							"""simple docstring"""

							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      A_  (      self				:     List[str]    )	->Any:
													SCREAMING_SNAKE_CASE__     :				int   =					TimmBackboneModelTester(self    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					ConfigTester(self							,	config_class=a							,	has_text_modality=a    )

							def      A_  (      self				:     Any    )	->Optional[int]:
													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				:     List[Any]    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					"resnet18"
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					"microsoft/resnet-18"

													SCREAMING_SNAKE_CASE__     :				Tuple   =					AutoBackbone.from_pretrained(a							,	use_timm_backbone=a    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					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]    )

													SCREAMING_SNAKE_CASE__     :				str   =					AutoBackbone.from_pretrained(a							,	use_timm_backbone=a							,	out_indices=[1, 2, 3]    )
													SCREAMING_SNAKE_CASE__     :				Any   =					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				:     Dict    )	->Optional[int]:
													pass

							@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"    )
							def      A_  (      self				:     Optional[Any]    )	->List[Any]:
													pass

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

							@unittest.skip("TimmBackbone models doesn't have inputs_embeds"    )
							def      A_  (      self				:     Dict    )	->Optional[int]:
													pass

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

							@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"    )
							def      A_  (      self				:     List[str]    )	->Any:
													pass

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

							@unittest.skip("model weights aren't tied in TimmBackbone."    )
							def      A_  (      self				:     List[str]    )	->List[str]:
													pass

							@unittest.skip("model weights aren't tied in TimmBackbone."    )
							def      A_  (      self				:     Optional[int]    )	->Union[str, Any]:
													pass

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

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

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

							@unittest.skip("TimmBackbone doesn't support output_attentions."    )
							def      A_  (      self				:     int    )	->Tuple:
													pass

							@unittest.skip("Safetensors is not supported by timm."    )
							def      A_  (      self				:     int    )	->List[Any]:
													pass

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

							def      A_  (      self				:     Dict    )	->Any:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.model_tester.prepare_config_and_inputs_for_common()

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

																			SCREAMING_SNAKE_CASE__     :				int   =					["pixel_values"]
																			self.assertListEqual(arg_names[:1]							,	a    )

							def      A_  (      self				:     Optional[int]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Any   =					self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE__     :				Any   =					True
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.has_attentions

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

													SCREAMING_SNAKE_CASE__     :				Any   =					self._prepare_for_class(a							,	a    )
													SCREAMING_SNAKE_CASE__     :				int   =					model(**a    )
													SCREAMING_SNAKE_CASE__     :				int   =					outputs[0][-1]

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

													if self.has_attentions:
																			SCREAMING_SNAKE_CASE__     :				List[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				:     Optional[int]    )	->List[str]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					model_class(a    )
																			model.to(a    )
																			model.eval()
																			SCREAMING_SNAKE_CASE__     :				int   =					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
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					copy.deepcopy(a    )
																			SCREAMING_SNAKE_CASE__     :				str   =					None
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					model_class(a    )
																			model.to(a    )
																			model.eval()
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					model(**a    )

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

																			# Check backbone can be initialized with fresh weights
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					copy.deepcopy(a    )
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					False
																			SCREAMING_SNAKE_CASE__     :				Any   =					model_class(a    )
																			model.to(a    )
																			model.eval()
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					model(**a    )






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list			,   _lowerCamelCase      :  int | None = None			,   _lowerCamelCase      :  int | None = None     ):





						'''simple docstring'''


						if start is None:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					0

						if end is None:
												SCREAMING_SNAKE_CASE__     :				Any   =					len(_lowerCamelCase     ) - 1

						if start >= end:
												return

						SCREAMING_SNAKE_CASE__     :				List[str]   =					(start + end) // 2

						slowsort(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )
						slowsort(_lowerCamelCase			,   mid + 1			,   _lowerCamelCase     )

						if sequence[end] < sequence[mid]:
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sequence[mid], sequence[end]

						slowsort(_lowerCamelCase			,   _lowerCamelCase			,   end - 1     )


if __name__ == "__main__":
				from doctest import testmod

				testmod()