





'''simple docstring'''




import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
			from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=3      ,       UpperCamelCase_=32      ,       UpperCamelCase_=3      ,       UpperCamelCase_=10      ,       UpperCamelCase_=[8, 16, 32, 64]      ,       UpperCamelCase_=[1, 1, 2, 1]      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_="relu"      ,       UpperCamelCase_=3      ,       UpperCamelCase_=None      ,       UpperCamelCase_=["stage2", "stage3", "stage4"]      ,       UpperCamelCase_=[2, 3, 4]      ,       UpperCamelCase_=1      ,       )      ->		Any:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   image_size
		snake_case__	       =   num_channels
		snake_case__	       =   embeddings_size
		snake_case__	       =   hidden_sizes
		snake_case__	       =   depths
		snake_case__	       =   is_training
		snake_case__	       =   use_labels
		snake_case__	       =   hidden_act
		snake_case__	       =   num_labels
		snake_case__	       =   scope
		snake_case__	       =   len(UpperCamelCase_		)
		snake_case__	       =   out_features
		snake_case__	       =   out_indices
		snake_case__	       =   num_groups





	def   _snake_case   (		self		)      ->		Optional[Any]:
		snake_case__	       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

		snake_case__	       =   self.get_config()

		return config, pixel_values, labels





	def   _snake_case   (		self		)      ->		Optional[Any]:
		return BitConfig(
		    num_channels=self.num_channels      ,       embeddings_size=self.embeddings_size      ,       hidden_sizes=self.hidden_sizes      ,       depths=self.depths      ,       hidden_act=self.hidden_act      ,       num_labels=self.num_labels      ,       out_features=self.out_features      ,       out_indices=self.out_indices      ,       num_groups=self.num_groups      ,       )





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[Any]:
		snake_case__	       =   BitModel(config=UpperCamelCase_		)
		model.to(UpperCamelCase_		)
		model.eval()
		snake_case__	       =   model(UpperCamelCase_		)
		self.parent.assertEqual(
		    result.last_hidden_state.shape      ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,       )





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   self.num_labels
		snake_case__	       =   BitForImageClassification(UpperCamelCase_		)
		model.to(UpperCamelCase_		)
		model.eval()
		snake_case__	       =   model(UpperCamelCase_      ,       labels=UpperCamelCase_		)
		self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Tuple:
		snake_case__	       =   BitBackbone(config=UpperCamelCase_		)
		model.to(UpperCamelCase_		)
		model.eval()
		snake_case__	       =   model(UpperCamelCase_		)

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps		)      ,       len(config.out_features		)		)
		self.parent.assertListEqual(list(result.feature_maps[0].shape		)      ,       [self.batch_size, self.hidden_sizes[1], 4, 4]		)

		# verify channels
		self.parent.assertEqual(len(model.channels		)      ,       len(config.out_features		)		)
		self.parent.assertListEqual(model.channels      ,       config.hidden_sizes[1:]		)

		# verify backbone works with out_features=None
		snake_case__	       =   None
		snake_case__	       =   BitBackbone(config=UpperCamelCase_		)
		model.to(UpperCamelCase_		)
		model.eval()
		snake_case__	       =   model(UpperCamelCase_		)

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps		)      ,       1		)
		self.parent.assertListEqual(list(result.feature_maps[0].shape		)      ,       [self.batch_size, self.hidden_sizes[-1], 1, 1]		)

		# verify channels
		self.parent.assertEqual(len(model.channels		)      ,       1		)
		self.parent.assertListEqual(model.channels      ,       [config.hidden_sizes[-1]]		)





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs
		snake_case__	       =   {'pixel_values': pixel_values}
		return config, inputs_dict






@require_torch
class 				__snake_case     (					__magic_name__       ,       __magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
	__lowerCAmelCase       						=  (
	    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False





	def   _snake_case   (		self		)      ->		int:
		snake_case__	       =   BitModelTester(self		)
		snake_case__	       =   ConfigTester(self      ,       config_class=UpperCamelCase_      ,       has_text_modality=UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		int:
		self.create_and_test_config_common_properties()
		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   _snake_case   (		self		)      ->		Tuple:
		return





	@unittest.skip(reason='Bit does not output attentions'		)
	def   _snake_case   (		self		)      ->		Optional[int]:
		pass





	@unittest.skip(reason='Bit does not use inputs_embeds'		)
	def   _snake_case   (		self		)      ->		int:
		pass





	@unittest.skip(reason='Bit does not support input and output embeddings'		)
	def   _snake_case   (		self		)      ->		Optional[Any]:
		pass





	def   _snake_case   (		self		)      ->		str:
		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

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

			snake_case__	       =   ['pixel_values']
			self.assertListEqual(arg_names[:1]      ,       UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_backbone(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Optional[Any]:
		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			snake_case__	       =   model_class(config=UpperCamelCase_		)
			for name, module in model.named_modules():
				if isinstance(UpperCamelCase_      ,       (nn.BatchNormad, nn.GroupNorm)		):
					self.assertTrue(
					    torch.all(module.weight == 1		)      ,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      ,       )
					self.assertTrue(
					    torch.all(module.bias == 0		)      ,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      ,       )





	def   _snake_case   (		self		)      ->		Optional[int]:
		def check_hidden_states_output(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		):
			snake_case__	       =   model_class(UpperCamelCase_		)
			model.to(UpperCamelCase_		)
			model.eval()

			with torch.no_grad():
				snake_case__	       =   model(**self._prepare_for_class(UpperCamelCase_      ,       UpperCamelCase_		)		)

			snake_case__	       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

			snake_case__	       =   self.model_tester.num_stages
			self.assertEqual(len(UpperCamelCase_		)      ,       expected_num_stages + 1		)

			# Bit's feature maps are of shape (batch_size, num_channels, height, width)
			self.assertListEqual(
			    list(hidden_states[0].shape[-2:]		)      ,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]      ,       )

		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()
		snake_case__	       =   ['preactivation', 'bottleneck']
		for model_class in self.all_model_classes:
			for layer_type in layers_type:
				snake_case__	       =   layer_type
				snake_case__	       =   True
				check_hidden_states_output(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)

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

				check_hidden_states_output(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)





	@unittest.skip(reason='Bit does not use feedforward chunking'		)
	def   _snake_case   (		self		)      ->		List[str]:
		pass





	def   _snake_case   (		self		)      ->		Optional[Any]:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_		)





	@slow
	def   _snake_case   (		self		)      ->		Any:
		for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			snake_case__	       =   BitModel.from_pretrained(UpperCamelCase_		)
			self.assertIsNotNone(UpperCamelCase_		)





def 						__lowerCamelCase			(	)   ->Dict:
	snake_case__	       =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
	return image






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





	@cached_property
	def   _snake_case   (		self		)      ->		List[str]:
		return (
		    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		) if is_vision_available() else None
		)





	@slow
	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(UpperCamelCase_		)

		snake_case__	       =   self.default_image_processor
		snake_case__	       =   prepare_img()
		snake_case__	       =   image_processor(images=UpperCamelCase_      ,       return_tensors='pt'		).to(UpperCamelCase_		)

		# forward pass
		with torch.no_grad():
			snake_case__	       =   model(**UpperCamelCase_		)

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

		snake_case__	       =   torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]		).to(UpperCamelCase_		)

		self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,       UpperCamelCase_      ,       atol=1E-4		)		)






@require_torch
class 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  (BitBackbone,) if is_torch_available() else ()
	__lowerCAmelCase       						=  BitConfig

	__lowerCAmelCase       						=  False





	def   _snake_case   (		self		)      ->		Optional[Any]:
		snake_case__	       =   BitModelTester(self		)










'''simple docstring'''




import cmath
import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->complex:
	snake_case__	       =   math.radians(UpperCAmelCase_							)
	snake_case__	       =   math.radians(UpperCAmelCase_							)

	# Convert voltage and current to rectangular form
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)

	# Calculate apparent power
	return voltage_rect * current_rect


if __name__ == "__main__":
			import doctest

			doctest.testmod()



