


"""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 :






		'''simple docstring'''

		def __init__(self				,       a_				,       a_=13				,       a_=3				,       a_=True				,       a_=True				,       a_=0.1				,       a_=0.1				,       a_=2_24				,       a_=10_00				,       a_=[3, 3, 6, 4]				,       a_=[48, 56, 1_12, 2_20]				,       ):





			'''simple docstring'''

			__snake_case	:						Any						=				parent
			__snake_case	:						Tuple						=				batch_size
			__snake_case	:						Dict						=				num_channels
			__snake_case	:						Dict						=				is_training
			__snake_case	:						List[Any]						=				use_labels
			__snake_case	:						Dict						=				hidden_dropout_prob
			__snake_case	:						Union[str, Any]						=				attention_probs_dropout_prob
			__snake_case	:						Optional[int]						=				num_labels
			__snake_case	:						str						=				image_size
			__snake_case	:						List[str]						=				layer_depths
			__snake_case	:						Dict						=				embed_dims



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			__snake_case	:						Optional[int]						=				None
			if self.use_labels:
				__snake_case	:						List[str]						=				ids_tensor([self.batch_size]				,       self.num_labels					)

			__snake_case	:						Dict						=				self.get_config()

			return config, pixel_values, labels



		def 				SCREAMING_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=a_				,       layer_scale_init_value=1E-5				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						str						=				SwiftFormerModel(config=a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						Union[str, Any]						=				model(a_					)
			self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.embed_dims[-1], 7, 7)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Dict						=				self.num_labels
			__snake_case	:						Tuple						=				SwiftFormerForImageClassification(a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						Any						=				model(a_				,       labels=a_					)
			self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)					)

			__snake_case	:						Optional[Any]						=				SwiftFormerForImageClassification(a_					)
			model.to(a_					)
			model.eval()

			__snake_case	:						Any						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)
			__snake_case	:						List[str]						=				model(a_					)
			self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			((__snake_case)	,							(__snake_case)	,							(__snake_case))	:						Optional[int]						=				self.prepare_config_and_inputs()
			__snake_case	:						Optional[Any]						=				{'''pixel_values''': pixel_values}
			return config, inputs_dict



@require_torch
class 			_UpperCAmelCase (							__snake_case,				__snake_case,				unittest.TestCase				):






		'''simple docstring'''

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

		lowerCamelCase__				=False
		lowerCamelCase__				=False
		lowerCamelCase__				=False
		lowerCamelCase__				=False
		lowerCamelCase__				=False



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				SwiftFormerModelTester(self					)
			__snake_case	:						Tuple						=				ConfigTester(
			    self				,       config_class=a_				,       has_text_modality=a_				,       hidden_size=37				,       num_attention_heads=12				,       num_hidden_layers=12				,       )



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.config_tester.run_common_tests()



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





			'''simple docstring'''

			pass



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						Optional[int]						=				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__snake_case	:						Dict						=				model_class(a_					)
				__snake_case	:						List[Any]						=				inspect.signature(model.forward					)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				__snake_case	:						List[str]						=				[*signature.parameters.keys()]

				__snake_case	:						List[str]						=				['''pixel_values''']
				self.assertListEqual(arg_names[:1]				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__snake_case	:						int						=				SwiftFormerModel.from_pretrained(a_					)
				self.assertIsNotNone(a_					)



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





			'''simple docstring'''

			pass



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			def check_hidden_states_output(a_				,       a_				,       a_					):
				__snake_case	:						List[str]						=				model_class(a_					)
				model.to(a_					)
				model.eval()

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

				__snake_case	:						Optional[Any]						=				outputs.hidden_states

				__snake_case	:						Union[str, Any]						=				8
				self.assertEqual(len(a_					)				,       a_					)  # 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(a_					)					):
					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),
					        ]					)				,       )

			__snake_case	,							__snake_case	:						Union[str, Any]						=				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__snake_case	:						Any						=				True
				check_hidden_states_output(a_				,       a_				,       a_					)

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

				check_hidden_states_output(a_				,       a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			def _config_zero_init(a_					):
				__snake_case	:						Optional[Any]						=				copy.deepcopy(a_					)
				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(a_				,       a_				,       1E-10					)
					if isinstance(getattr(a_				,       a_				,       a_					)				,       a_					):
						__snake_case	:						Union[str, Any]						=				_config_zero_init(getattr(a_				,       a_					)					)
						setattr(a_				,       a_				,       a_					)
				return configs_no_init

			__snake_case	,							__snake_case	:						List[str]						=				self.model_tester.prepare_config_and_inputs_for_common()

			__snake_case	:						Dict						=				_config_zero_init(a_					)
			for model_class in self.all_model_classes:
				__snake_case	:						Dict						=				model_class(config=a_					)
				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					):





			'''simple docstring'''

			pass






def 		lowercase							(     )     ->List[Any]:
	"""simple docstring"""





	__snake_case	:						int						=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
	return image



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






		'''simple docstring'''

		@cached_property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''					).to(a_					)

			__snake_case	:						Optional[int]						=				self.default_image_processor
			__snake_case	:						Dict						=				prepare_img()
			__snake_case	:						Tuple						=				image_processor(images=a_				,       return_tensors='''pt'''					).to(a_					)

			# forward pass
			with torch.no_grad():
				__snake_case	:						Any						=				model(**a_					)

			# verify the logits
			__snake_case	:						int						=				torch.Size((1, 10_00)					)
			self.assertEqual(outputs.logits.shape				,       a_					)

			__snake_case	:						List[Any]						=				torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]					).to(a_					)
			self.assertTrue(torch.allclose(outputs.logits[0, :3]				,       a_				,       atol=1E-4					)					)









"""simple docstring"""
import math






def 		lowercase							(     _snake_case						:      int     )     ->bool:
	"""simple docstring"""





	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5     ,   int(math.sqrt(_snake_case     ) + 1     )     ,   6     ):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True






def 		lowercase							(     _snake_case						:      float = 0.1     )     ->int:
	"""simple docstring"""





	__snake_case	:						Tuple						=				3
	__snake_case	:						Any						=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1     ,   (j + 2) * (j + 2)     ,   j + 1     ):
			primes += is_prime(_snake_case     )
		j += 2
	return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()





