


'''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        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=13      ,						snake_case=3      ,						snake_case=True      ,						snake_case=True      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=224      ,						snake_case=1000      ,						snake_case=[3, 3, 6, 4]      ,						snake_case=[48, 56, 112, 220]      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       parent
						UpperCamelCase__					=       batch_size
						UpperCamelCase__					=       num_channels
						UpperCamelCase__					=       is_training
						UpperCamelCase__					=       use_labels
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       num_labels
						UpperCamelCase__					=       image_size
						UpperCamelCase__					=       layer_depths
						UpperCamelCase__					=       embed_dims




			def 				snake_case__				(							self     ):

						'''simple docstring'''


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

						UpperCamelCase__					=       None
						if self.use_labels:
									UpperCamelCase__					=       ids_tensor([self.batch_size]      ,						self.num_labels     )

						UpperCamelCase__					=       self.get_config()

						return config, pixel_values, labels




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						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=snake_case      ,						layer_scale_init_value=1E-5      ,						)




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       SwiftFormerModel(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case     )
						self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.embed_dims[-1], 7, 7)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.num_labels
						UpperCamelCase__					=       SwiftFormerForImageClassification(snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case      ,						labels=snake_case     )
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_labels)     )

						UpperCamelCase__					=       SwiftFormerForImageClassification(snake_case     )
						model.to(snake_case     )
						model.eval()

						UpperCamelCase__					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )
						UpperCamelCase__					=       model(snake_case     )
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_labels)     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						((UpperCamelCase__),      (UpperCamelCase__),      (UpperCamelCase__))					=       self.prepare_config_and_inputs()
						UpperCamelCase__					=       {"pixel_values": pixel_values}
						return config, inputs_dict







@require_torch
class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Tuple    =		(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
			_UpperCamelCase		:					Any    =		(
			    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase		:					Optional[Any]    =		False
			_UpperCamelCase		:					Optional[Any]    =		False
			_UpperCamelCase		:					int    =		False
			_UpperCamelCase		:					Any    =		False
			_UpperCamelCase		:					Optional[int]    =		False




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       SwiftFormerModelTester(self     )
						UpperCamelCase__					=       ConfigTester(
						    self      ,						config_class=snake_case      ,						has_text_modality=snake_case      ,						hidden_size=37      ,						num_attention_heads=12      ,						num_hidden_layers=12      ,						)




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						self.config_tester.run_common_tests()




			@unittest.skip(reason="SwiftFormer does not use inputs_embeds"     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__					=       model_class(snake_case     )
									UpperCamelCase__					=       model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(snake_case      ,						nn.Linear     )     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCamelCase__					=       ["pixel_values"]
									self.assertListEqual(arg_names[:1]      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case     )




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__					=       SwiftFormerModel.from_pretrained(snake_case     )
									self.assertIsNotNone(snake_case     )




			@unittest.skip(reason="SwiftFormer does not output attentions"     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     ):
									UpperCamelCase__					=       model_class(snake_case     )
									model.to(snake_case     )
									model.eval()

									with torch.no_grad():
												UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

									UpperCamelCase__					=       outputs.hidden_states

									UpperCamelCase__					=       8
									self.assertEqual(len(snake_case     )      ,						snake_case     )  # 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(snake_case     )     ):
												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__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__					=       True
									check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCamelCase__					=       True

									check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						def _config_zero_init(snake_case     ):
									UpperCamelCase__					=       copy.deepcopy(snake_case     )
									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(snake_case      ,						snake_case      ,						1E-1_0     )
												if isinstance(getattr(snake_case      ,						snake_case      ,						snake_case     )      ,						snake_case     ):
															UpperCamelCase__					=       _config_zero_init(getattr(snake_case      ,						snake_case     )     )
															setattr(snake_case      ,						snake_case      ,						snake_case     )
									return configs_no_init

						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						UpperCamelCase__					=       _config_zero_init(snake_case     )
						for model_class in self.all_model_classes:
									UpperCamelCase__					=       model_class(config=snake_case     )
									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 				snake_case__				(							self     ):

						'''simple docstring'''


						pass



def        UpperCamelCase_(				)->		Dict:
			UpperCamelCase__					=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
			return image







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






			"""simple docstring"""






			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"     ) if is_vision_available() else None







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"     ).to(snake_case     )

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_img()
						UpperCamelCase__					=       image_processor(images=snake_case      ,						return_tensors="pt"     ).to(snake_case     )

						# forward pass
						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						# verify the logits
						UpperCamelCase__					=       torch.Size((1, 1000)     )
						self.assertEqual(outputs.logits.shape      ,						snake_case     )

						UpperCamelCase__					=       torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]     ).to(snake_case     )
						self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,						snake_case      ,						atol=1E-4     )     )






def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number < 0 or shift_amount < 0:
						raise ValueError("both inputs must be positive integers"  )

			UpperCamelCase__					=       str(bin(_A  )  )
			binary_number += "0" * shift_amount
			return binary_number
def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number < 0 or shift_amount < 0:
						raise ValueError("both inputs must be positive integers"  )

			UpperCamelCase__					=       str(bin(_A  )  )[2:]
			if shift_amount >= len(_A  ):
						return "0b0"
			UpperCamelCase__					=       binary_number[: len(_A  ) - shift_amount]
			return "0b" + shifted_binary_number
def        UpperCamelCase_(				_A   :int				,     _A   :int  )->		str:
			if number >= 0:  # Get binary representation of positive number
						UpperCamelCase__					=       "0" + str(bin(_A  )  ).strip("-"  )[2:]
			else:  # Get binary (2's complement) representation of negative number
						UpperCamelCase__					=       len(bin(_A  )[3:]  )  # Find 2's complement of number
						UpperCamelCase__					=       bin(abs(_A  ) - (1 << binary_number_length)  )[3:]
						UpperCamelCase__					=       (
						    "1" + "0" * (binary_number_length - len(_A  )) + binary_number
						)

			if shift_amount >= len(_A  ):
						return "0b" + binary_number[0] * len(_A  )
			return (
			    "0b"
			    + binary_number[0] * shift_amount
			    + binary_number[: len(_A  ) - shift_amount]
			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()


