

"""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			:    List[Any]       ,  _lowercase			:    int       ,  _lowercase			:    Dict=13       ,  _lowercase			:    List[Any]=3       ,  _lowercase			:    List[Any]=True       ,  _lowercase			:    List[str]=True       ,  _lowercase			:    Dict=0.1       ,  _lowercase			:    Any=0.1       ,  _lowercase			:    str=2_24       ,  _lowercase			:    Union[str, Any]=10_00       ,  _lowercase			:    Union[str, Any]=[3, 3, 6, 4]       ,  _lowercase			:    str=[48, 56, 1_12, 2_20]       ,  ):
		__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     a   (   self			:    Tuple		):
		__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     a   (   self			:    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     a   (   self			:    int       ,  _lowercase			:    Any       ,  _lowercase			:    str       ,  _lowercase			:    List[Any]		):
		__UpperCAmelCase							=      SwiftFormerModel(config=lowerCamelCase__		)
		model.to(lowerCamelCase__		)
		model.eval()
		__UpperCAmelCase							=      model(lowerCamelCase__		)
		self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.embed_dims[-1], 7, 7)		)



	def     a   (   self			:    str       ,  _lowercase			:    Optional[Any]       ,  _lowercase			:    Any       ,  _lowercase			:    int		):
		__UpperCAmelCase							=      self.num_labels
		__UpperCAmelCase							=      SwiftFormerForImageClassification(lowerCamelCase__		)
		model.to(lowerCamelCase__		)
		model.eval()
		__UpperCAmelCase							=      model(lowerCamelCase__       ,  labels=lowerCamelCase__		)
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)		)

		__UpperCAmelCase							=      SwiftFormerForImageClassification(lowerCamelCase__		)
		model.to(lowerCamelCase__		)
		model.eval()

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



	def     a   (   self			:    Tuple		):
		((__UpperCAmelCase)						,				(__UpperCAmelCase)						,				(__UpperCAmelCase))							=      self.prepare_config_and_inputs()
		__UpperCAmelCase							=      {'''pixel_values''': pixel_values}
		return config, inputs_dict






@require_torch
class 			_UpperCAmelCase					(				_lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase    ):
	a__						:      Dict						       = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	a__						:      List[str]						       = (
	    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	a__						:      Union[str, Any]						       = False
	a__						:      Union[str, Any]						       = False
	a__						:      Tuple						       = False
	a__						:      List[Any]						       = False
	a__						:      Any						       = False



	def     a   (   self			:    str		):
		__UpperCAmelCase							=      SwiftFormerModelTester(self		)
		__UpperCAmelCase							=      ConfigTester(
		    self       ,  config_class=lowerCamelCase__       ,  has_text_modality=lowerCamelCase__       ,  hidden_size=37       ,  num_attention_heads=12       ,  num_hidden_layers=12       ,  )



	def     a   (   self			:    Tuple		):
		self.config_tester.run_common_tests()



	@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''		)
	def     a   (   self			:    List[Any]		):
		pass



	def     a   (   self			:    Dict		):
		__UpperCAmelCase						,				__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs_for_common()

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



	def     a   (   self			:    Optional[int]		):
		__UpperCAmelCase						,				__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__UpperCAmelCase							=      model_class(lowerCamelCase__		)
			__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]       ,  lowerCamelCase__		)



	def     a   (   self			:    List[Any]		):
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCamelCase__		)



	def     a   (   self			:    Optional[int]		):
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__		)



	@slow
	def     a   (   self			:    Optional[Any]		):
		for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__UpperCAmelCase							=      SwiftFormerModel.from_pretrained(lowerCamelCase__		)
			self.assertIsNotNone(lowerCamelCase__		)



	@unittest.skip(reason='''SwiftFormer does not output attentions'''		)
	def     a   (   self			:    Optional[int]		):
		pass



	def     a   (   self			:    Dict		):
		def check_hidden_states_output(_lowercase			:    Tuple       ,  _lowercase			:    int       ,  _lowercase			:    int		):
			__UpperCAmelCase							=      model_class(lowerCamelCase__		)
			model.to(lowerCamelCase__		)
			model.eval()

			with torch.no_grad():
				__UpperCAmelCase							=      model(**self._prepare_for_class(lowerCamelCase__       ,  lowerCamelCase__		)		)

			__UpperCAmelCase							=      outputs.hidden_states

			__UpperCAmelCase							=      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							=      self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__UpperCAmelCase							=      True
			check_hidden_states_output(lowerCamelCase__       ,  lowerCamelCase__       ,  lowerCamelCase__		)

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

			check_hidden_states_output(lowerCamelCase__       ,  lowerCamelCase__       ,  lowerCamelCase__		)



	def     a   (   self			:    List[str]		):
		def _config_zero_init(_lowercase			:    Optional[Any]		):
			__UpperCAmelCase							=      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							=      _config_zero_init(getattr(lowerCamelCase__       ,  lowerCamelCase__		)		)
					setattr(lowerCamelCase__       ,  lowerCamelCase__       ,  lowerCamelCase__		)
			return configs_no_init

		__UpperCAmelCase						,				__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs_for_common()

		__UpperCAmelCase							=      _config_zero_init(lowerCamelCase__		)
		for model_class in self.all_model_classes:
			__UpperCAmelCase							=      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     a   (   self			:    Optional[int]		):
		pass






def 	lowercase__		(    ):
	__UpperCAmelCase							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
	return image




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



	@cached_property
	def     a   (   self			:    int		):
		return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''		) if is_vision_available() else None



	@slow
	def     a   (   self			:    List[Any]		):
		__UpperCAmelCase							=      SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''		).to(lowerCamelCase__		)

		__UpperCAmelCase							=      self.default_image_processor
		__UpperCAmelCase							=      prepare_img()
		__UpperCAmelCase							=      image_processor(images=lowerCamelCase__       ,  return_tensors='''pt'''		).to(lowerCamelCase__		)

		# forward pass
		with torch.no_grad():
			__UpperCAmelCase							=      model(**lowerCamelCase__		)

		# verify the logits
		__UpperCAmelCase							=      torch.Size((1, 10_00)		)
		self.assertEqual(outputs.logits.shape       ,  lowerCamelCase__		)

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






class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    n
							__lowerCamelCase		       =    [None] * self.n
							__lowerCamelCase		       =    0  # index of the first element
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    0


			def __len__(      self							)    ->      int:



							'''simple docstring'''
							return self.size


			def       lowercase_    (      self							)    ->      bool:



							'''simple docstring'''
							return self.size == 0


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							return False if self.is_empty() else self.array[self.front]


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Optional[Any]:



							'''simple docstring'''
							if self.size >= self.n:
											raise Exception('QUEUE IS FULL'							)

							__lowerCamelCase		       =    data
							__lowerCamelCase		       =    (self.rear + 1) % self.n
							self.size += 1
							return self






			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							if self.size == 0:
											raise Exception('UNDERFLOW'							)

							__lowerCamelCase		       =    self.array[self.front]
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    (self.front + 1) % self.n
							self.size -= 1
							return temp


