

from typing import Any

import numpy as np



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple )     -> Optional[Any]:




				'''simple docstring'''





				assert np.shape(__UpperCamelCase )[0] == np.shape(__UpperCamelCase )[1]

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

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

				# Check sign of all eigenvalues.
				# np.all returns a value of type np.bool_
				return bool(np.all(eigen_values > 0 ) )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int )     -> Union[str, Any]:




				'''simple docstring'''





				A__            =     np.random.randn(__UpperCamelCase		,    __UpperCamelCase )
				A__            =     np.dot(__UpperCamelCase		,    random_matrix.T )
				assert _is_matrix_spd(__UpperCamelCase )
				return spd_matrix



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]		,    SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							Optional[int] = 1_0_0_0		,    SCREAMING_SNAKE_CASE_:							List[str] = 1e-8		,    )     -> List[Any]:




				'''simple docstring'''





				assert np.shape(__UpperCamelCase )[0] == np.shape(__UpperCamelCase )[1]
				assert np.shape(__UpperCamelCase )[0] == np.shape(__UpperCamelCase )[0]
				assert _is_matrix_spd(__UpperCamelCase )

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

				# Set initial errors in solution guess and residual.
				A__            =     1e9
				A__            =     1e9
				A__            =     1e9

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

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

								# The main algorithm.

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

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

								# Update variables.
								A__            =     np.copy(__UpperCamelCase )
								A__            =     np.copy(__UpperCamelCase )
								A__            =     np.copy(__UpperCamelCase )

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

				return x



def    lowerCAmelCase__							(		)     -> Optional[Any]:




				'''simple docstring'''





				A__            =     3
				A__            =     _create_spd_matrix(__UpperCamelCase )
				A__            =     np.random.randn(__UpperCamelCase		,    1 )
				A__            =     np.dot(__UpperCamelCase		,    __UpperCamelCase )

				# Numpy solution.
				A__            =     np.linalg.solve(__UpperCamelCase		,    __UpperCamelCase )

				# Our implementation.
				A__            =     conjugate_gradient(__UpperCamelCase		,    __UpperCamelCase )

				# 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()



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__						=	logging.get_logger(__name__)

lowerCAmelCase__						=	{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'swiftformer'


			def __init__(      self , lowercase=3 , lowercase=[3, 3, 6, 4] , lowercase=[48, 56, 112, 220] , lowercase=4 , lowercase=[True, True, True, True] , lowercase="gelu" , lowercase=3 , lowercase=2 , lowercase=1 , lowercase=0.0 , lowercase=True , lowercase=1e-5 , lowercase=1e-5 , **lowercase , )					->  Optional[int]:
							'''simple docstring'''






							super().__init__(**lowercase    )
							A__            =     num_channels
							A__            =     depths
							A__            =     embed_dims
							A__            =     mlp_ratio
							A__            =     downsamples
							A__            =     hidden_act
							A__            =     down_patch_size
							A__            =     down_stride
							A__            =     down_pad
							A__            =     drop_path_rate
							A__            =     use_layer_scale
							A__            =     layer_scale_init_value
							A__            =     batch_norm_eps

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    version.parse('1.11'     )


			@property
			def        UpperCamelCase						(      self    )					->  Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]    )


			@property
			def        UpperCamelCase						(      self    )					->  float:
							'''simple docstring'''






							return 1e-4

