






"""simple docstring"""
import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import SwiftFormerForImageClassification, SwiftFormerModel
			from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor







class      UpperCamelCase_			:





	def __init__( self   :					Any      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					Optional[Any]=13      ,      lowerCAmelCase_   :					str=3      ,      lowerCAmelCase_   :					Tuple=True      ,      lowerCAmelCase_   :					Union[str, Any]=True      ,      lowerCAmelCase_   :					Union[str, Any]=0.1      ,      lowerCAmelCase_   :					Tuple=0.1      ,      lowerCAmelCase_   :					List[Any]=224      ,      lowerCAmelCase_   :					Optional[int]=1_000      ,      lowerCAmelCase_   :					int=[3, 3, 6, 4]      ,      lowerCAmelCase_   :					Union[str, Any]=[48, 56, 112, 220]      ,      )		->     Optional[Any]:
				UpperCAmelCase_	:					List[Any]     	=   parent
				UpperCAmelCase_	:					Dict     	=   batch_size
				UpperCAmelCase_	:					Union[str, Any]     	=   num_channels
				UpperCAmelCase_	:					Union[str, Any]     	=   is_training
				UpperCAmelCase_	:					Dict     	=   use_labels
				UpperCAmelCase_	:					List[Any]     	=   hidden_dropout_prob
				UpperCAmelCase_	:					Any     	=   attention_probs_dropout_prob
				UpperCAmelCase_	:					Dict     	=   num_labels
				UpperCAmelCase_	:					Dict     	=   image_size
				UpperCAmelCase_	:					Optional[int]     	=   layer_depths
				UpperCAmelCase_	:					Optional[Any]     	=   embed_dims





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Optional[int]:
				UpperCAmelCase_	:					Union[str, Any]     	=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				UpperCAmelCase_	:					List[Any]     	=   None
				if self.use_labels:
							UpperCAmelCase_	:					str     	=   ids_tensor([self.batch_size]      ,      self.num_labels		)

				UpperCAmelCase_	:					List[Any]     	=   self.get_config()

				return config, pixel_values, labels





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Optional[int]:
				return SwiftFormerConfig(
				    depths=self.layer_depths      ,      embed_dims=self.embed_dims      ,      mlp_ratio=4      ,      downsamples=[True, True, True, True]      ,      hidden_act="gelu"      ,      num_labels=self.num_labels      ,      down_patch_size=3      ,      down_stride=2      ,      down_pad=1      ,      drop_rate=0.0      ,      drop_path_rate=0.0      ,      use_layer_scale=lowerCAmelCase_      ,      layer_scale_init_value=1e-5      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					Any		)		->     Any:
				UpperCAmelCase_	:					List[Any]     	=   SwiftFormerModel(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					Any     	=   model(lowerCAmelCase_		)
				self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.embed_dims[-1], 7, 7)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					int		)		->     List[str]:
				UpperCAmelCase_	:					Any     	=   self.num_labels
				UpperCAmelCase_	:					int     	=   SwiftFormerForImageClassification(lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					Optional[int]     	=   model(lowerCAmelCase_      ,      labels=lowerCAmelCase_		)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)		)

				UpperCAmelCase_	:					Union[str, Any]     	=   SwiftFormerForImageClassification(lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()

				UpperCAmelCase_	:					int     	=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
				UpperCAmelCase_	:					Dict     	=   model(lowerCAmelCase_		)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Optional[int]:
				((UpperCAmelCase_)	,      (UpperCAmelCase_)	,      (UpperCAmelCase_))	:					List[Any]     	=   self.prepare_config_and_inputs()
				UpperCAmelCase_	:					Any     	=   {"pixel_values": pixel_values}
				return config, inputs_dict







@require_torch
class      UpperCamelCase_			(__A							,		__A							,		unittest.TestCase	):
	__magic_name__       =							(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	__magic_name__       =							(
	    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__magic_name__       =							False
	__magic_name__       =							False
	__magic_name__       =							False
	__magic_name__       =							False
	__magic_name__       =							False





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     int:
				UpperCAmelCase_	:					Any     	=   SwiftFormerModelTester(self		)
				UpperCAmelCase_	:					int     	=   ConfigTester(
				    self      ,      config_class=lowerCAmelCase_      ,      has_text_modality=lowerCAmelCase_      ,      hidden_size=37      ,      num_attention_heads=12      ,      num_hidden_layers=12      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Any:
				self.config_tester.run_common_tests()





	@unittest.skip(reason="SwiftFormer does not use inputs_embeds"		)
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Union[str, Any]:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     str:
				UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase_	:					List[str]     	=   model_class(lowerCAmelCase_		)
							UpperCAmelCase_	:					int     	=   model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowerCAmelCase_      ,      nn.Linear		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[Any]:
				UpperCAmelCase_	,      UpperCAmelCase_	:					str     	=   self.model_tester.prepare_config_and_inputs_for_common()

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

							UpperCAmelCase_	:					str     	=   ["pixel_values"]
							self.assertListEqual(arg_names[:1]      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Optional[int]:
				UpperCAmelCase_	:					str     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     List[Any]:
				UpperCAmelCase_	:					Tuple     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Tuple:
				for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_	:					int     	=   SwiftFormerModel.from_pretrained(lowerCAmelCase_		)
							self.assertIsNotNone(lowerCAmelCase_		)





	@unittest.skip(reason="SwiftFormer does not output attentions"		)
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Any:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[Any]:
				def check_hidden_states_output(lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					str		):
							UpperCAmelCase_	:					Optional[int]     	=   model_class(lowerCAmelCase_		)
							model.to(lowerCAmelCase_		)
							model.eval()

							with torch.no_grad():
										UpperCAmelCase_	:					Union[str, Any]     	=   model(**self._prepare_for_class(lowerCAmelCase_      ,      lowerCAmelCase_		)		)

							UpperCAmelCase_	:					Optional[Any]     	=   outputs.hidden_states

							UpperCAmelCase_	:					Any     	=   8
							self.assertEqual(len(lowerCAmelCase_		)      ,      lowerCAmelCase_		)  # TODO

							# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
							# with the width and height being successively divided by 2, after every 2 blocks
							for i in range(len(lowerCAmelCase_		)		):
										self.assertEqual(
										    hidden_states[i].shape      ,      torch.Size(
										        [
										            self.model_tester.batch_size,
										            self.model_tester.embed_dims[i // 2],
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										        ]		)      ,      )

				UpperCAmelCase_	,      UpperCAmelCase_	:					str     	=   self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase_	:					str     	=   True
							check_hidden_states_output(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							UpperCAmelCase_	:					int     	=   True

							check_hidden_states_output(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Optional[Any]:
				def _config_zero_init(lowerCAmelCase_   :					int		):
							UpperCAmelCase_	:					str     	=   copy.deepcopy(lowerCAmelCase_		)
							for key in configs_no_init.__dict__.keys():
										if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
													setattr(lowerCAmelCase_      ,      lowerCAmelCase_      ,      1e-10		)
										if isinstance(getattr(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)      ,      lowerCAmelCase_		):
													UpperCAmelCase_	:					Any     	=   _config_zero_init(getattr(lowerCAmelCase_      ,      lowerCAmelCase_		)		)
													setattr(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)
							return configs_no_init

				UpperCAmelCase_	,      UpperCAmelCase_	:					int     	=   self.model_tester.prepare_config_and_inputs_for_common()

				UpperCAmelCase_	:					str     	=   _config_zero_init(lowerCAmelCase_		)
				for model_class in self.all_model_classes:
							UpperCAmelCase_	:					Dict     	=   model_class(config=lowerCAmelCase_		)
							for name, param in model.named_parameters():
										if param.requires_grad:
													self.assertIn(
													    ((param.data.mean() * 1e9) / 1e9).round().item()      ,      [0.0, 1.0]      ,      msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""      ,      )





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





def 							snake_case (  ):
			UpperCAmelCase_	:					List[str]     	=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
			return image







@require_torch
@require_vision
class      UpperCamelCase_			(unittest.TestCase	):





	@cached_property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Any:
				return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"		) if is_vision_available() else None





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     int:
				UpperCAmelCase_	:					Any     	=   SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"		).to(lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   self.default_image_processor
				UpperCAmelCase_	:					Dict     	=   prepare_img()
				UpperCAmelCase_	:					List[str]     	=   image_processor(images=lowerCAmelCase_      ,      return_tensors="pt"		).to(lowerCAmelCase_		)

				# forward pass
				with torch.no_grad():
							UpperCAmelCase_	:					Optional[int]     	=   model(**lowerCAmelCase_		)

				# verify the logits
				UpperCAmelCase_	:					Union[str, Any]     	=   torch.Size((1, 1_000)		)
				self.assertEqual(outputs.logits.shape      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Tuple     	=   torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]]		).to(lowerCAmelCase_		)
				self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,      lowerCAmelCase_      ,      atol=1e-4		)		)











"""simple docstring"""
from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			:
	__magic_name__       =							42
	__magic_name__       =							None





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     Optional[int]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     Optional[Any]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Union[str, Any]		)		->     str:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Dict:
				if not self.is_available():
							raise RuntimeError(
							    f"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."""		)





	@classmethod
	def 	_SCREAMING_SNAKE_CASE ( cls   :					Tuple		)		->     List[str]:
				return f"""`pip install {cls.pip_package or cls.name}`"""







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''optuna'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     List[str]:
				return is_optuna_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     int:
				return run_hp_search_optuna(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Dict		)		->     Optional[Any]:
				return default_hp_space_optuna(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''ray'''
	__magic_name__       =							'''\'ray[tune]\''''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     Optional[int]:
				return is_ray_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     List[str]:
				return run_hp_search_ray(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Tuple		)		->     Optional[int]:
				return default_hp_space_ray(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''sigopt'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     List[Any]:
				return is_sigopt_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					str		)		->     str:
				return run_hp_search_sigopt(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Any		)		->     Any:
				return default_hp_space_sigopt(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''wandb'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     int:
				return is_wandb_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Optional[Any]		)		->     Optional[Any]:
				return run_hp_search_wandb(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]		)		->     Any:
				return default_hp_space_wandb(lowerCAmelCase_		)





lowerCamelCase_							      = {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}

def 							snake_case (  ):
			UpperCAmelCase_	:					List[str]     	=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
			if len(A__		) > 0:
						UpperCAmelCase_	:					Optional[Any]     	=   available_backends[0].name
						if len(A__		) > 1:
									logger.info(
									    F"""{len(A__		)} hyperparameter search backends available. Using {name} as the default."""		)
						return name
			raise RuntimeError(
			    "No hyperparameter search backend available.\n"
			    + "\n".join(
			        F""" - To install {backend.name} run {backend.pip_install()}"""
			        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()		)		)



