
'''simple docstring'''





from typing import Any

import numpy as np
def   snake_case_       (							_a     :							np.ndarray			):
      assert np.shape(_a			)[0] == np.shape(_a			)[1]

      # If matrix not symmetric, exit right away.
      if np.allclose(_a ,					matrix.T			) is False:
            return False

      # Get eigenvalues and eignevectors for a symmetric matrix.
      snake_case_       ,	snake_case_						       =							np.linalg.eigh(_a			)

      # Check sign of all eigenvalues.
      # np.all returns a value of type np.bool_
      return bool(np.all(eigen_values > 0			)			)
def   snake_case_       (							_a     :							int			):
      snake_case_						       =							np.random.randn(_a ,					_a			)
      snake_case_						       =							np.dot(_a ,					random_matrix.T			)
      assert _is_matrix_spd(_a			)
      return spd_matrix
def   snake_case_       (							_a     :							np.ndarray ,					_a     :							np.ndarray ,					_a     :							int = 1_000 ,					_a     :							float = 1E-8 ,					):
      assert np.shape(_a			)[0] == np.shape(_a			)[1]
      assert np.shape(_a			)[0] == np.shape(_a			)[0]
      assert _is_matrix_spd(_a			)

      # Initialize solution guess, residual, search direction.
      snake_case_						       =							np.zeros((np.shape(_a			)[0], 1)			)
      snake_case_						       =							np.copy(_a			)
      snake_case_						       =							np.copy(_a			)

      # Set initial errors in solution guess and residual.
      snake_case_						       =							1E9
      snake_case_						       =							1E9
      snake_case_						       =							1E9

      # Set iteration counter to threshold number of iterations.
      snake_case_						       =							0

      while error > tol:
            # Save this value so we only calculate the matrix-vector product once.
            snake_case_						       =							np.dot(_a ,					_a			)

            # The main algorithm.

            # Update search direction magnitude.
            snake_case_						       =							np.dot(ra.T ,					_a			) / np.dot(pa.T ,					_a			)
            # Update solution guess.
            snake_case_						       =							xa + alpha * pa
            # Calculate new residual.
            snake_case_						       =							ra - alpha * w
            # Calculate new Krylov subspace scale.
            snake_case_						       =							np.dot(r.T ,					_a			) / np.dot(ra.T ,					_a			)
            # Calculate new A conjuage search direction.
            snake_case_						       =							r + beta * pa

            # Calculate errors.
            snake_case_						       =							np.linalg.norm(r - ra			)
            snake_case_						       =							np.linalg.norm(x - xa			)
            snake_case_						       =							np.maximum(_a ,					_a			)

            # Update variables.
            snake_case_						       =							np.copy(_a			)
            snake_case_						       =							np.copy(_a			)
            snake_case_						       =							np.copy(_a			)

            # Update number of iterations.
            iterations += 1
            if iterations > max_iterations:
                  break

      return x
def   snake_case_       (							):
      snake_case_						       =							3
      snake_case_						       =							_create_spd_matrix(_a			)
      snake_case_						       =							np.random.randn(_a ,					1			)
      snake_case_						       =							np.dot(_a ,					_a			)

      # Numpy solution.
      snake_case_						       =							np.linalg.solve(_a ,					_a			)

      # Our implementation.
      snake_case_						       =							conjugate_gradient(_a ,					_a			)

      # Ensure both solutions are close to x_true (and therefore one another).
      assert np.linalg.norm(x_numpy - x_true			) <= 1E-6
      assert np.linalg.norm(x_conjugate_gradient - x_true			) <= 1E-6


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     test_conjugate_gradient()



'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers.models.swin.modeling_tf_swin import (
         TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFSwinForImageClassification,
         TFSwinForMaskedImageModeling,
         TFSwinModel,
     )


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor



class  lowerCAmelCase__   :



  '''simple docstring'''






  def __init__(self  : Optional[int]	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Any=13	, lowerCAmelCase_  : int=32	, lowerCAmelCase_  : int=2	, lowerCAmelCase_  : List[str]=3	, lowerCAmelCase_  : Any=16	, lowerCAmelCase_  : Tuple=[1, 2, 1]	, lowerCAmelCase_  : List[str]=[2, 2, 4]	, lowerCAmelCase_  : Dict=2	, lowerCAmelCase_  : str=2.0	, lowerCAmelCase_  : int=True	, lowerCAmelCase_  : List[Any]=0.0	, lowerCAmelCase_  : List[Any]=0.0	, lowerCAmelCase_  : List[Any]=0.1	, lowerCAmelCase_  : Optional[int]="gelu"	, lowerCAmelCase_  : Dict=False	, lowerCAmelCase_  : str=True	, lowerCAmelCase_  : Optional[int]=0.02	, lowerCAmelCase_  : List[str]=1E-5	, lowerCAmelCase_  : Dict=True	, lowerCAmelCase_  : Tuple=None	, lowerCAmelCase_  : List[str]=True	, lowerCAmelCase_  : str=10	, lowerCAmelCase_  : Tuple=8	, )   ->       None:
        snake_case_						       =							parent
        snake_case_						       =							batch_size
        snake_case_						       =							image_size
        snake_case_						       =							patch_size
        snake_case_						       =							num_channels
        snake_case_						       =							embed_dim
        snake_case_						       =							depths
        snake_case_						       =							num_heads
        snake_case_						       =							window_size
        snake_case_						       =							mlp_ratio
        snake_case_						       =							qkv_bias
        snake_case_						       =							hidden_dropout_prob
        snake_case_						       =							attention_probs_dropout_prob
        snake_case_						       =							drop_path_rate
        snake_case_						       =							hidden_act
        snake_case_						       =							use_absolute_embeddings
        snake_case_						       =							patch_norm
        snake_case_						       =							layer_norm_eps
        snake_case_						       =							initializer_range
        snake_case_						       =							is_training
        snake_case_						       =							scope
        snake_case_						       =							use_labels
        snake_case_						       =							type_sequence_label_size
        snake_case_						       =							encoder_stride

  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Dict:
        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.type_sequence_label_size    )

        snake_case_						       =							self.get_config()

        return config, pixel_values, labels

  def 		_lowerCamelCase	(self  : str    )   ->       Tuple:
        return SwinConfig(
            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 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : int	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Optional[int]    )   ->       Any:
        snake_case_						       =							TFSwinModel(config=lowerCAmelCase_    )
        snake_case_						       =							model(lowerCAmelCase_    )

        snake_case_						       =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
        snake_case_						       =							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 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : Dict	, lowerCAmelCase_  : Optional[int]    )   ->       Optional[int]:
        snake_case_						       =							TFSwinForMaskedImageModeling(config=lowerCAmelCase_    )
        snake_case_						       =							model(lowerCAmelCase_    )
        self.parent.assertEqual(
            result.logits.shape	, (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

        # test greyscale images
        snake_case_						       =							1
        snake_case_						       =							TFSwinForMaskedImageModeling(lowerCAmelCase_    )

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

  def 		_lowerCamelCase	(self  : Dict	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Optional[Any]    )   ->       Tuple:
        snake_case_						       =							self.type_sequence_label_size
        snake_case_						       =							TFSwinForImageClassification(lowerCAmelCase_    )
        snake_case_						       =							model(lowerCAmelCase_	, labels=lowerCAmelCase_    )
        self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)    )

        # test greyscale images
        snake_case_						       =							1
        snake_case_						       =							TFSwinForImageClassification(lowerCAmelCase_    )
        snake_case_						       =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
        snake_case_						       =							model(lowerCAmelCase_    )
        self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)    )


  def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       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_tf
class  lowerCAmelCase__   (       UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase       ):



  '''simple docstring'''






  _UpperCamelCase						= (
      (
          TFSwinModel,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  _UpperCamelCase						= (
      {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
      if is_tf_available()
      else {}
  )

  _UpperCamelCase						= False
  _UpperCamelCase						= False
  _UpperCamelCase						= False
  _UpperCamelCase						= False

  def 		_lowerCamelCase	(self  : Any    )   ->       List[Any]:
        snake_case_						       =							TFSwinModelTester(self    )
        snake_case_						       =							ConfigTester(self	, config_class=lowerCAmelCase_	, embed_dim=37    )

  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Optional[Any]:
        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  : List[Any]    )   ->       List[str]:
        return

  def 		_lowerCamelCase	(self  : Any    )   ->       Dict:
        snake_case_						       =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : int    )   ->       Any:
        snake_case_						       =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : Tuple    )   ->       Optional[Any]:
        snake_case_						       =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_    )

  @unittest.skip(reason="Swin does not use inputs_embeds"    )
  def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       Union[str, Any]:
        pass

  def 		_lowerCamelCase	(self  : List[str]    )   ->       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(lowerCAmelCase_    )
              self.assertIsInstance(model.get_input_embeddings()	, tf.keras.layers.Layer    )
              snake_case_						       =							model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowerCAmelCase_	, tf.keras.layers.Dense    )    )

  def 		_lowerCamelCase	(self  : Dict    )   ->       Dict:
        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(lowerCAmelCase_    )
              snake_case_						       =							inspect.signature(model.call    )
              # 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]	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : Dict    )   ->       Any:
        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_						       =							True

        for model_class in self.all_model_classes:
              snake_case_						       =							True
              snake_case_						       =							False
              snake_case_						       =							True
              snake_case_						       =							model_class(lowerCAmelCase_    )
              snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
              snake_case_						       =							outputs.attentions
              snake_case_						       =							len(self.model_tester.depths    )
              self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              snake_case_						       =							True
              snake_case_						       =							config.window_size**2
              snake_case_						       =							model_class(lowerCAmelCase_    )
              snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
              snake_case_						       =							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]	, )
              snake_case_						       =							len(lowerCAmelCase_    )

              # Check attention is always last and order is fine
              snake_case_						       =							True
              snake_case_						       =							True
              snake_case_						       =							model_class(lowerCAmelCase_    )
              snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )

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

              snake_case_						       =							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 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : int	, lowerCAmelCase_  : Dict	, lowerCAmelCase_  : List[Any]    )   ->       Optional[int]:
        snake_case_						       =							model_class(lowerCAmelCase_    )
        snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
        snake_case_						       =							outputs.hidden_states

        snake_case_						       =							getattr(
            self.model_tester	, "expected_num_hidden_layers"	, len(self.model_tester.depths    ) + 1    )
        self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )

        # Swin has a different seq_length
        snake_case_						       =							to_atuple(config.patch_size    )

        snake_case_						       =							(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]	, )

        snake_case_						       =							outputs.reshaped_hidden_states
        self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )

        snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_						       =							reshaped_hidden_states[0].shape

        snake_case_						       =							tf.reshape(reshaped_hidden_states[0]	, (batch_size, num_channels, height * width)    )
        snake_case_						       =							tf.transpose(lowerCAmelCase_	, (0, 2, 1)    )

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

  def 		_lowerCamelCase	(self  : Dict    )   ->       str:
        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()

        snake_case_						       =							to_atuple(self.model_tester.image_size    )

        for model_class in self.all_model_classes:
              snake_case_						       =							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"]
              snake_case_						       =							True

              self.check_hidden_states_output(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : Tuple    )   ->       List[Any]:
        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_						       =							3

        snake_case_						       =							to_atuple(self.model_tester.image_size    )
        snake_case_						       =							to_atuple(config.patch_size    )

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

        for model_class in self.all_model_classes:
              snake_case_						       =							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"]
              snake_case_						       =							True
              self.check_hidden_states_output(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, (padded_height, padded_width)    )


  @slow
  def 		_lowerCamelCase	(self  : Any    )   ->       List[str]:
        for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case_						       =							TFSwinModel.from_pretrained(lowerCAmelCase_    )
              self.assertIsNotNone(lowerCAmelCase_    )



@require_vision
@require_tf
class  lowerCAmelCase__   (       unittest.TestCase       ):



  '''simple docstring'''






  @cached_property
  def 		_lowerCamelCase	(self  : Dict    )   ->       str:
        return (
            AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    )
            if is_vision_available()
            else None
        )


  @slow
  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       str:
        snake_case_						       =							TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    )
        snake_case_						       =							self.default_image_processor

        snake_case_						       =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
        snake_case_						       =							image_processor(images=lowerCAmelCase_	, return_tensors="tf"    )

        # forward pass
        snake_case_						       =							model(lowerCAmelCase_    )

        # verify the logits
        snake_case_						       =							tf.TensorShape((1, 10_00)    )
        self.assertEqual(outputs.logits.shape	, lowerCAmelCase_    )
        snake_case_						       =							tf.constant([-0.0_948, -0.6_454, -0.0_921]    )
        self.assertTrue(np.allclose(outputs.logits[0, :3]	, lowerCAmelCase_	, atol=1E-4    )    )

