'''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 					_lowercase						:
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=3       ,lowerCamelCase_=32       ,lowerCamelCase_=3       ,lowerCamelCase_=10       ,lowerCamelCase_=[8, 16, 32, 64]       ,lowerCamelCase_=[1, 1, 2, 1]       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_="relu"       ,lowerCamelCase_=3       ,lowerCamelCase_=None       ,lowerCamelCase_=["stage2", "stage3", "stage4"]       ,lowerCamelCase_=[2, 3, 4]       ,lowerCamelCase_=1       ,)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	parent
         UpperCAmelCase__				:					Any 					=	batch_size
         UpperCAmelCase__				:					Dict 					=	image_size
         UpperCAmelCase__				:					Any 					=	num_channels
         UpperCAmelCase__				:					str 					=	embeddings_size
         UpperCAmelCase__				:					Union[str, Any] 					=	hidden_sizes
         UpperCAmelCase__				:					int 					=	depths
         UpperCAmelCase__				:					Any 					=	is_training
         UpperCAmelCase__				:					int 					=	use_labels
         UpperCAmelCase__				:					Union[str, Any] 					=	hidden_act
         UpperCAmelCase__				:					Union[str, Any] 					=	num_labels
         UpperCAmelCase__				:					Tuple 					=	scope
         UpperCAmelCase__				:					Any 					=	len(lowerCamelCase_				)
         UpperCAmelCase__				:					List[str] 					=	out_features
         UpperCAmelCase__				:					str 					=	out_indices
         UpperCAmelCase__				:					Any 					=	num_groups




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

         UpperCAmelCase__				:					Dict 					=	self.get_config()

         return config, pixel_values, labels




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         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 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	BitModel(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					Tuple 					=	model(lowerCamelCase_				)
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.num_labels
         UpperCAmelCase__				:					int 					=	BitForImageClassification(lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					int 					=	model(lowerCamelCase_       ,labels=lowerCamelCase_				)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	BitBackbone(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					Optional[Any] 					=	model(lowerCamelCase_				)

         # 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
         UpperCAmelCase__				:					int 					=	None
         UpperCAmelCase__				:					Optional[Any] 					=	BitBackbone(config=lowerCamelCase_				)
         model.to(lowerCamelCase_				)
         model.eval()
         UpperCAmelCase__				:					List[str] 					=	model(lowerCamelCase_				)

         # 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 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.prepare_config_and_inputs()
         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					List[str] 					=	config_and_inputs
         UpperCAmelCase__				:					Tuple 					=	{'''pixel_values''': pixel_values}
         return config, inputs_dict




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


      UpperCAmelCase_   :   Union[str, Any]   				= (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
      UpperCAmelCase_   :   Tuple   				= (
          {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
          if is_torch_available()
          else {}
      )

      UpperCAmelCase_   :   int   				= False
      UpperCAmelCase_   :   Dict   				= False
      UpperCAmelCase_   :   List[str]   				= False
      UpperCAmelCase_   :   Optional[Any]   				= False
      UpperCAmelCase_   :   Union[str, Any]   				= False




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	BitModelTester(self				)
         UpperCAmelCase__				:					List[Any] 					=	ConfigTester(self       ,config_class=lowerCamelCase_       ,has_text_modality=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         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 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         return




      @unittest.skip(reason='''Bit does not output attentions'''				)
      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         pass




      @unittest.skip(reason='''Bit does not use inputs_embeds'''				)
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         pass




      @unittest.skip(reason='''Bit does not support input and output embeddings'''				)
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         pass




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					Dict 					=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase__				:					List[str] 					=	model_class(lowerCamelCase_				)
            UpperCAmelCase__				:					Union[str, Any] 					=	inspect.signature(model.forward				)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase__				:					Optional[int] 					=	[*signature.parameters.keys()]

            UpperCAmelCase__				:					Optional[Any] 					=	['''pixel_values''']
            self.assertListEqual(arg_names[:1]       ,lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					Tuple 					=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase__				:					Dict 					=	model_class(config=lowerCamelCase_				)
            for name, module in model.named_modules():
               if isinstance(lowerCamelCase_       ,(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 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         def check_hidden_states_output(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				):
            UpperCAmelCase__				:					str 					=	model_class(lowerCamelCase_				)
            model.to(lowerCamelCase_				)
            model.eval()

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

            UpperCAmelCase__				:					Union[str, Any] 					=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase__				:					List[str] 					=	self.model_tester.num_stages
            self.assertEqual(len(lowerCamelCase_				)       ,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]       ,)

         UpperCAmelCase__					,   UpperCAmelCase__				:					Any 					=	self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase__				:					Dict 					=	['''preactivation''', '''bottleneck''']
         for model_class in self.all_model_classes:
            for layer_type in layers_type:
               UpperCAmelCase__				:					Any 					=	layer_type
               UpperCAmelCase__				:					Union[str, Any] 					=	True
               check_hidden_states_output(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)

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

               check_hidden_states_output(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)




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



         '''simple docstring'''



         pass




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_				)




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase__				:					Dict 					=	BitModel.from_pretrained(lowerCamelCase_				)
            self.assertIsNotNone(lowerCamelCase_				)







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[int] 					=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
   return image


@require_torch
@require_vision
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      @cached_property
      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         return (
             BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				) if is_vision_available() else None
         )




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[int] 					=	self.default_image_processor
         UpperCAmelCase__				:					int 					=	prepare_img()
         UpperCAmelCase__				:					Optional[int] 					=	image_processor(images=lowerCamelCase_       ,return_tensors='''pt'''				).to(lowerCamelCase_				)

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

         # verify the logits
         UpperCAmelCase__				:					Optional[Any] 					=	torch.Size((1, 1000)				)
         self.assertEqual(outputs.logits.shape       ,lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	torch.tensor([[-0.6526, -0.5263, -1.4398]]				).to(lowerCamelCase_				)

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




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


      UpperCAmelCase_   :   str   				= (BitBackbone,) if is_torch_available() else ()
      UpperCAmelCase_   :   Union[str, Any]   				= BitConfig

      UpperCAmelCase_   :   Tuple   				= False




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	BitModelTester(self				)





'''simple docstring'''

import numpy as np







def __UpperCamelCase( _A       :					np.ndarray	,      _A       :					np.ndarray	,      _A       :					float = 1e-12	,      _A       :					int = 1_00	,      ):
   '''simple docstring'''
   assert np.shape(_A     )[0] == np.shape(_A     )[1]
   # Ensure proper dimensionality.
   assert np.shape(_A     )[0] == np.shape(_A     )[0]
   # Ensure inputs are either both complex or both real
   assert np.iscomplexobj(_A     ) == np.iscomplexobj(_A     )
   UpperCAmelCase__				:					int 					=	np.iscomplexobj(_A     )
   if is_complex:
      # Ensure complex input_matrix is Hermitian
      assert np.array_equal(_A	,      input_matrix.conj().T     )

   # Set convergence to False. Will define convergence when we exceed max_iterations
   # or when we have small changes from one iteration to next.

   UpperCAmelCase__				:					Any 					=	False
   UpperCAmelCase__				:					str 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1e12

   while not convergence:
      # Multiple matrix by the vector.
      UpperCAmelCase__				:					str 					=	np.dot(_A	,      _A     )
      # Normalize the resulting output vector.
      UpperCAmelCase__				:					int 					=	w / np.linalg.norm(_A     )
      # Find rayleigh quotient
      # (faster than usual b/c we know vector is normalized already)
      UpperCAmelCase__				:					int 					=	vector.conj().T if is_complex else vector.T
      UpperCAmelCase__				:					Any 					=	np.dot(_A	,      np.dot(_A	,      _A     )     )

      # Check convergence.
      UpperCAmelCase__				:					List[Any] 					=	np.abs(lambda_ - lambda_previous     ) / lambda_
      iterations += 1

      if error <= error_tol or iterations >= max_iterations:
         UpperCAmelCase__				:					Optional[Any] 					=	True

      UpperCAmelCase__				:					Optional[int] 					=	lambda_

   if is_complex:
      UpperCAmelCase__				:					Tuple 					=	np.real(lambda_     )

   return lambda_, vector







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[int] 					=	np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]     )
   UpperCAmelCase__				:					int 					=	np.array([41, 4, 20]     )
   UpperCAmelCase__				:					str 					=	real_input_matrix.astype(np.complexaaa     )
   UpperCAmelCase__				:					Union[str, Any] 					=	np.triu(1J * complex_input_matrix	,      1     )
   complex_input_matrix += imag_matrix
   complex_input_matrix += -1 * imag_matrix.T
   UpperCAmelCase__				:					Tuple 					=	np.array([41, 4, 20]     ).astype(np.complexaaa     )

   for problem_type in ["real", "complex"]:
      if problem_type == "real":
         UpperCAmelCase__				:					Optional[int] 					=	real_input_matrix
         UpperCAmelCase__				:					int 					=	real_vector
      elif problem_type == "complex":
         UpperCAmelCase__				:					int 					=	complex_input_matrix
         UpperCAmelCase__				:					Tuple 					=	complex_vector

      # Our implementation.
      UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	power_iteration(_A	,      _A     )

      # Numpy implementation.

      # Get eigenvalues and eigenvectors using built-in numpy
      # eigh (eigh used for symmetric or hermetian matrices).
      UpperCAmelCase__					,   UpperCAmelCase__				:					Tuple 					=	np.linalg.eigh(_A     )
      # Last eigenvalue is the maximum one.
      UpperCAmelCase__				:					Dict 					=	eigen_values[-1]
      # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
      UpperCAmelCase__				:					Union[str, Any] 					=	eigen_vectors[:, -1]

      # Check our implementation and numpy gives close answers.
      assert np.abs(eigen_value - eigen_value_max     ) <= 1e-6
      # Take absolute values element wise of each eigenvector.
      # as they are only unique to a minus sign.
      assert np.linalg.norm(np.abs(_A     ) - np.abs(_A     )     ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()




