
"""simple docstring"""

import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
					import onnxruntime as ort


_A						=			logging.get_logger(__name__)

_A						=			{
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}






class 	_lowercase       :




	def __init__(			self		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Union[str, Any]:
			logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
			lowerCamelCase					:      List[str]          =   model
			lowerCamelCase					:      Union[str, Any]          =   kwargs.get('model_save_dir'		,			UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   kwargs.get('latest_model_name'		,			UpperCAmelCase_ )




	def __call__(			self		,			**UpperCAmelCase_ )		->							int:
			lowerCamelCase					:      List[Any]          =   {k: np.array(UpperCAmelCase_ ) for k, v in kwargs.items()}
			return self.model.run(UpperCAmelCase_		,			UpperCAmelCase_ )




	@staticmethod
	def       _UpperCamelCase   (			UpperCAmelCase_		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None )		->							Dict:
			if provider is None:
					logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
					lowerCamelCase					:      List[str]          =   'CPUExecutionProvider'

			return ort.InferenceSession(UpperCAmelCase_		,			providers=[provider]		,			sess_options=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_ )		->							str:
			lowerCamelCase					:      Optional[Any]          =   file_name if file_name is not None else ONNX_WEIGHTS_NAME

			lowerCamelCase					:      Optional[Any]          =   self.model_save_dir.joinpath(self.latest_model_name )
			lowerCamelCase					:      Union[str, Any]          =   Path(UpperCAmelCase_ ).joinpath(UpperCAmelCase_ )
			try:
					shutil.copyfile(UpperCAmelCase_		,			UpperCAmelCase_ )
			except shutil.SameFileError:
					pass

			# copy external weights (for models >2GB)
			lowerCamelCase					:      List[str]          =   self.model_save_dir.joinpath(UpperCAmelCase_ )
			if src_path.exists():
					lowerCamelCase					:      str          =   Path(UpperCAmelCase_ ).joinpath(UpperCAmelCase_ )
					try:
							shutil.copyfile(UpperCAmelCase_		,			UpperCAmelCase_ )
					except shutil.SameFileError:
							pass




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			**UpperCAmelCase_		,			)		->							Any:
			if os.path.isfile(UpperCAmelCase_ ):
					logger.error(F"""Provided path ({save_directory}) should be a directory, not a file""" )
					return

			os.makedirs(UpperCAmelCase_		,			exist_ok=UpperCAmelCase_ )

			# saving model weights/files
			self._save_pretrained(UpperCAmelCase_		,			**UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							Any:
			lowerCamelCase					:      List[str]          =   file_name if file_name is not None else ONNX_WEIGHTS_NAME
			# load model from local directory
			if os.path.isdir(UpperCAmelCase_ ):
					lowerCamelCase					:      Optional[int]          =   OnnxRuntimeModel.load_model(
					    os.path.join(UpperCAmelCase_		,			UpperCAmelCase_ )		,			provider=UpperCAmelCase_		,			sess_options=UpperCAmelCase_ )
					lowerCamelCase					:      Optional[int]          =   Path(UpperCAmelCase_ )
			# load model from hub
			else:
					# download model
					lowerCamelCase					:      Optional[Any]          =   hf_hub_download(
					    repo_id=UpperCAmelCase_		,			filename=UpperCAmelCase_		,			use_auth_token=UpperCAmelCase_		,			revision=UpperCAmelCase_		,			cache_dir=UpperCAmelCase_		,			force_download=UpperCAmelCase_		,			)
					lowerCamelCase					:      Dict          =   Path(UpperCAmelCase_ ).parent
					lowerCamelCase					:      Any          =   Path(UpperCAmelCase_ ).name
					lowerCamelCase					:      str          =   OnnxRuntimeModel.load_model(UpperCAmelCase_		,			provider=UpperCAmelCase_		,			sess_options=UpperCAmelCase_ )
			return cls(model=UpperCAmelCase_		,			**UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							Optional[Any]:
			lowerCamelCase					:      Any          =   None
			if len(str(UpperCAmelCase_ ).split('@' ) ) == 2:
					lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   model_id.split('@' )

			return cls._from_pretrained(
			    model_id=UpperCAmelCase_		,			revision=UpperCAmelCase_		,			cache_dir=UpperCAmelCase_		,			force_download=UpperCAmelCase_		,			use_auth_token=UpperCAmelCase_		,			**UpperCAmelCase_		,			)








"""simple docstring"""

import numpy as np
def 		UpperCAmelCase (		a_,						a_,						a_ = 1E-12,						a_ = 100,						):





		'''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_      )
		lowerCamelCase					:      Optional[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.

		lowerCamelCase					:      Union[str, Any]          =   False
		lowerCamelCase					:      List[str]          =   0
		lowerCamelCase					:      Any          =   0
		lowerCamelCase					:      Dict          =   1E12

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

				# Check convergence.
				lowerCamelCase					:      Optional[int]          =   np.abs(lambda_ - lambda_previous      ) / lambda_
				iterations += 1

				if error <= error_tol or iterations >= max_iterations:
						lowerCamelCase					:      int          =   True

				lowerCamelCase					:      Optional[Any]          =   lambda_

		if is_complex:
				lowerCamelCase					:      Any          =   np.real(lambda_      )

		return lambda_, vector
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      str          =   np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
		lowerCamelCase					:      str          =   np.array([41, 4, 20]      )
		lowerCamelCase					:      Optional[Any]          =   real_input_matrix.astype(np.complexaaa      )
		lowerCamelCase					:      Dict          =   np.triu(1j * complex_input_matrix,						1      )
		complex_input_matrix += imag_matrix
		complex_input_matrix += -1 * imag_matrix.T
		lowerCamelCase					:      List[Any]          =   np.array([41, 4, 20]      ).astype(np.complexaaa      )

		for problem_type in ["real", "complex"]:
				if problem_type == "real":
						lowerCamelCase					:      str          =   real_input_matrix
						lowerCamelCase					:      Any          =   real_vector
				elif problem_type == "complex":
						lowerCamelCase					:      str          =   complex_input_matrix
						lowerCamelCase					:      Dict          =   complex_vector

				# Our implementation.
				lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   power_iteration(a_,						a_      )

				# Numpy implementation.

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






