



import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
				from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_=13		,       lowerCamelCase_=32		,       lowerCamelCase_=2		,       lowerCamelCase_=3		,       lowerCamelCase_=16		,       lowerCamelCase_=[1, 2, 1]		,       lowerCamelCase_=[2, 2, 4]		,       lowerCamelCase_=2		,       lowerCamelCase_=2.0		,       lowerCamelCase_=True		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.1		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=False		,       lowerCamelCase_=True		,       lowerCamelCase_=0.02		,       lowerCamelCase_=1E-5		,       lowerCamelCase_=True		,       lowerCamelCase_=None		,       lowerCamelCase_=True		,       lowerCamelCase_=10		,       lowerCamelCase_=8		,       ):
						"""simple docstring"""

						a					     =   parent
						a					     =   batch_size
						a					     =   image_size
						a					     =   patch_size
						a					     =   num_channels
						a					     =   embed_dim
						a					     =   depths
						a					     =   num_heads
						a					     =   window_size
						a					     =   mlp_ratio
						a					     =   qkv_bias
						a					     =   hidden_dropout_prob
						a					     =   attention_probs_dropout_prob
						a					     =   drop_path_rate
						a					     =   hidden_act
						a					     =   use_absolute_embeddings
						a					     =   patch_norm
						a					     =   layer_norm_eps
						a					     =   initializer_range
						a					     =   is_training
						a					     =   scope
						a					     =   use_labels
						a					     =   type_sequence_label_size
						a					     =   encoder_stride




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

						a					     =   None
						if self.use_labels:
											a					     =   ids_tensor([self.batch_size]		,       self.type_sequence_label_size		)

						a					     =   self.get_config()

						return config, pixel_values, labels




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return SwinvaConfig(
						    image_size=self.image_size		,       patch_size=self.patch_size		,       num_channels=self.num_channels		,       embed_dim=self.embed_dim		,       depths=self.depths		,       num_heads=self.num_heads		,       window_size=self.window_size		,       mlp_ratio=self.mlp_ratio		,       qkv_bias=self.qkv_bias		,       hidden_dropout_prob=self.hidden_dropout_prob		,       attention_probs_dropout_prob=self.attention_probs_dropout_prob		,       drop_path_rate=self.drop_path_rate		,       hidden_act=self.hidden_act		,       use_absolute_embeddings=self.use_absolute_embeddings		,       path_norm=self.patch_norm		,       layer_norm_eps=self.layer_norm_eps		,       initializer_range=self.initializer_range		,       encoder_stride=self.encoder_stride		,       )




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   SwinvaModel(config=lowerCamelCase_		)
						model.to(lowerCamelCase_		)
						model.eval()
						a					     =   model(lowerCamelCase_		)

						a					     =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
						a					     =   int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

						self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, expected_seq_len, expected_dim)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   SwinvaForMaskedImageModeling(config=lowerCamelCase_		)
						model.to(lowerCamelCase_		)
						model.eval()
						a					     =   model(lowerCamelCase_		)
						self.parent.assertEqual(
						    result.logits.shape		,       (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

						# test greyscale images
						a					     =   1
						a					     =   SwinvaForMaskedImageModeling(lowerCamelCase_		)
						model.to(lowerCamelCase_		)
						model.eval()

						a					     =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						a					     =   model(lowerCamelCase_		)
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, 1, self.image_size, self.image_size)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.type_sequence_label_size
						a					     =   SwinvaForImageClassification(lowerCamelCase_		)
						model.to(lowerCamelCase_		)
						model.eval()
						a					     =   model(lowerCamelCase_		,       labels=lowerCamelCase_		)
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.type_sequence_label_size)		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




@require_torch
class 	_lowercase							(   lowerCAmelCase,      lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							(
	    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
	)
	__A							=							(
	    {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__A							=							False
	__A							=							False
	__A							=							False
	__A							=							False




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   SwinvaModelTester(self		)
						a					     =   ConfigTester(self		,       config_class=lowerCamelCase_		,       embed_dim=37		)




	def      UpperCamelCase_       (self		):
						"""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      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase_		)




	@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."		)
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	@unittest.skip(reason="Swinv2 does not use inputs_embeds"		)
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											a					     =   model_class(lowerCamelCase_		)
											self.assertIsInstance(model.get_input_embeddings()		,       (nn.Module)		)
											a					     =   model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowerCamelCase_		,       nn.Linear		)		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()

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

											a					     =   ["pixel_values"]
											self.assertListEqual(arg_names[:1]		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()
						a					     =   True

						for model_class in self.all_model_classes:
											a					     =   True
											a					     =   False
											a					     =   True
											a					     =   model_class(lowerCamelCase_		)
											model.to(lowerCamelCase_		)
											model.eval()
											with torch.no_grad():
																a					     =   model(**self._prepare_for_class(lowerCamelCase_		,       lowerCamelCase_		)		)
											a					     =   outputs.attentions
											a					     =   len(self.model_tester.depths		)
											self.assertEqual(len(lowerCamelCase_		)		,       lowerCamelCase_		)

											# check that output_attentions also work using config
											del inputs_dict["output_attentions"]
											a					     =   True
											a					     =   config.window_size**2
											a					     =   model_class(lowerCamelCase_		)
											model.to(lowerCamelCase_		)
											model.eval()
											with torch.no_grad():
																a					     =   model(**self._prepare_for_class(lowerCamelCase_		,       lowerCamelCase_		)		)
											a					     =   outputs.attentions
											self.assertEqual(len(lowerCamelCase_		)		,       lowerCamelCase_		)

											self.assertListEqual(
											    list(attentions[0].shape[-3:]		)		,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,       )
											a					     =   len(lowerCamelCase_		)

											# Check attention is always last and order is fine
											a					     =   True
											a					     =   True
											a					     =   model_class(lowerCamelCase_		)
											model.to(lowerCamelCase_		)
											model.eval()
											with torch.no_grad():
																a					     =   model(**self._prepare_for_class(lowerCamelCase_		,       lowerCamelCase_		)		)

											if hasattr(self.model_tester		,       "num_hidden_states_types"		):
																a					     =   self.model_tester.num_hidden_states_types
											else:
																# also another +1 for reshaped_hidden_states
																a					     =   2
											self.assertEqual(out_len + added_hidden_states		,       len(lowerCamelCase_		)		)

											a					     =   outputs.attentions

											self.assertEqual(len(lowerCamelCase_		)		,       lowerCamelCase_		)

											self.assertListEqual(
											    list(self_attentions[0].shape[-3:]		)		,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,       )




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   model_class(lowerCamelCase_		)
						model.to(lowerCamelCase_		)
						model.eval()

						with torch.no_grad():
											a					     =   model(**self._prepare_for_class(lowerCamelCase_		,       lowerCamelCase_		)		)

						a					     =   outputs.hidden_states

						a					     =   getattr(
						    self.model_tester		,       "expected_num_hidden_layers"		,       len(self.model_tester.depths		) + 1		)
						self.assertEqual(len(lowerCamelCase_		)		,       lowerCamelCase_		)

						# Swinv2 has a different seq_length
						a					     =   (
						    config.patch_size
						    if isinstance(config.patch_size		,       collections.abc.Iterable		)
						    else (config.patch_size, config.patch_size)
						)

						a					     =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]		)		,       [num_patches, self.model_tester.embed_dim]		,       )

						a					     =   outputs.reshaped_hidden_states
						self.assertEqual(len(lowerCamelCase_		)		,       lowerCamelCase_		)

						a     ,				a     ,				a     ,				a					     =   reshaped_hidden_states[0].shape
						a					     =   (
						    reshaped_hidden_states[0].view(lowerCamelCase_		,       lowerCamelCase_		,       height * width		).permute(0		,       2		,       1		)
						)
						self.assertListEqual(
						    list(reshaped_hidden_states.shape[-2:]		)		,       [num_patches, self.model_tester.embed_dim]		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()

						a					     =   (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size		,       collections.abc.Iterable		)
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)

						for model_class in self.all_model_classes:
											a					     =   True
											self.check_hidden_states_output(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

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

											self.check_hidden_states_output(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()
						a					     =   3

						a					     =   (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size		,       collections.abc.Iterable		)
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)
						a					     =   (
						    config.patch_size
						    if isinstance(config.patch_size		,       collections.abc.Iterable		)
						    else (config.patch_size, config.patch_size)
						)

						a					     =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
						a					     =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

						for model_class in self.all_model_classes:
											a					     =   True
											self.check_hidden_states_output(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       (padded_height, padded_width)		)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											a					     =   True
											self.check_hidden_states_output(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       (padded_height, padded_width)		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a					     =   SwinvaModel.from_pretrained(lowerCamelCase_		)
											self.assertIsNotNone(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()

						a					     =   _config_zero_init(lowerCamelCase_		)
						for model_class in self.all_model_classes:
											a					     =   model_class(config=lowerCamelCase_		)
											for name, param in model.named_parameters():
																if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																					self.assertIn(
																					    ((param.data.mean() * 1E9).round() / 1E9).item()		,       [0.0, 1.0]		,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''		,       )




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





	"""simple docstring"""




	@cached_property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return (
						    AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"		)
						    if is_vision_available()
						    else None
						)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"		).to(
						    lowerCamelCase_		)
						a					     =   self.default_image_processor

						a					     =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
						a					     =   image_processor(images=lowerCamelCase_		,       return_tensors="pt"		).to(lowerCamelCase_		)

						# forward pass
						with torch.no_grad():
											a					     =   model(**lowerCamelCase_		)

						# verify the logits
						a					     =   torch.Size((1, 1000)		)
						self.assertEqual(outputs.logits.shape		,       lowerCamelCase_		)
						a					     =   torch.tensor([-0.3947, -0.4306, 0.0026]		).to(lowerCamelCase_		)
						self.assertTrue(torch.allclose(outputs.logits[0, :3]		,       lowerCamelCase_		,       atol=1E-4		)		)





import cmath
import math






def     a(      A       :    float ,     A       :    float ,     A       :    float ,     A       :    float       )		->       complex:
					"""simple docstring"""





					a					     =   math.radians(A       )
					a					     =   math.radians(A       )

					# Convert voltage and current to rectangular form
					a					     =   cmath.rect(A ,     A       )
					a					     =   cmath.rect(A ,     A       )

					# Calculate apparent power
					return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()
