






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        __UpperCamelCase    :







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







		__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      :							List[str]			)		-> int:
		"""simple docstring"""







		__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      :							Any			)		-> 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  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							int			)		-> List[str]:
		"""simple docstring"""







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







	def  _a   (  self      :							Optional[int]			)		-> List[Any]:
		"""simple docstring"""







		__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        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:str   =       (TimmBackbone,) if is_torch_available() else ()
	__snake_case	:Dict   =       {'feature-extraction': TimmBackbone} if is_torch_available() else {}
	__snake_case	:Union[str, Any]   =       False
	__snake_case	:List[Any]   =       False
	__snake_case	:Tuple   =       False
	__snake_case	:Dict   =       False







	def  _a   (  self      :							str			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  TimmBackboneModelTester(self			)
		__lowercase             =  ConfigTester(self       ,				config_class=_lowerCAmelCase       ,				has_text_modality=_lowerCAmelCase			)







	def  _a   (  self      :							str			)		-> List[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  _a   (  self      :							str			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  """resnet18"""
		__lowercase             =  """microsoft/resnet-18"""

		__lowercase             =  AutoBackbone.from_pretrained(_lowerCAmelCase       ,				use_timm_backbone=_lowerCAmelCase			)
		__lowercase             =  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]			)

		__lowercase             =  AutoBackbone.from_pretrained(_lowerCAmelCase       ,				use_timm_backbone=_lowerCAmelCase       ,				out_indices=[1, 2, 3]			)
		__lowercase             =  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  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		pass







	@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""			)
	def  _a   (  self      :							Union[str, Any]			)		-> Optional[Any]:
		"""simple docstring"""







		pass







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







		pass







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







		pass







	@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""			)
	def  _a   (  self      :							List[str]			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







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







		pass







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







		pass







	@unittest.skip("""model weights aren't tied in TimmBackbone."""			)
	def  _a   (  self      :							Tuple			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	@unittest.skip("""model weights aren't tied in TimmBackbone."""			)
	def  _a   (  self      :							Optional[int]			)		-> str:
		"""simple docstring"""







		pass







	@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""			)
	def  _a   (  self      :							Dict			)		-> str:
		"""simple docstring"""







		pass







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







		pass







	@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""			)
	def  _a   (  self      :							Union[str, Any]			)		-> Any:
		"""simple docstring"""







		pass







	@unittest.skip("""TimmBackbone doesn't support output_attentions."""			)
	def  _a   (  self      :							Any			)		-> str:
		"""simple docstring"""







		pass







	@unittest.skip("""Safetensors is not supported by timm."""			)
	def  _a   (  self      :							Optional[Any]			)		-> List[str]:
		"""simple docstring"""







		pass







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







		pass







	def  _a   (  self      :							Optional[int]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__lowercase             =  model_class(_lowerCAmelCase			)
			__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]       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Optional[Any]			)		-> str:
		"""simple docstring"""







		__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(_lowerCAmelCase			)
		model.to(_lowerCAmelCase			)

		__lowercase             =  self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  model(**_lowerCAmelCase			)
		__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=_lowerCAmelCase			)

		self.assertIsNotNone(hidden_states.grad			)

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







	def  _a   (  self      :							int			)		-> Tuple:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__lowercase             =  model_class(_lowerCAmelCase			)
			model.to(_lowerCAmelCase			)
			model.eval()
			__lowercase             =  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
			__lowercase             =  copy.deepcopy(_lowerCAmelCase			)
			__lowercase             =  None
			__lowercase             =  model_class(_lowerCAmelCase			)
			model.to(_lowerCAmelCase			)
			model.eval()
			__lowercase             =  model(**_lowerCAmelCase			)

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

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__UpperCamelCase      :      Tuple     						=						{
    """configuration_swiftformer""": [
        """SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwiftFormerConfig""",
        """SwiftFormerOnnxConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      :      Optional[Any]     						=						[
						    """SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """SwiftFormerForImageClassification""",
						    """SwiftFormerModel""",
						    """SwiftFormerPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						__UpperCamelCase      :      List[Any]     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

