

import mpmath  # for roots of unity
import numpy as np


class 			A						:







    """simple docstring"""





    def __init__(				self			:	Dict,lowercase_			:	List[Any]=None,lowercase_			:	List[str]=None		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       list(poly_a or [0]		)[:]
        A__   				=       list(poly_b or [0]		)[:]

        # Remove leading zero coefficients
        while self.polyA[-1] == 0:
            self.polyA.pop()
        A__   				=       len(self.polyA		)

        while self.polyB[-1] == 0:
            self.polyB.pop()
        A__   				=       len(self.polyB		)

        # Add 0 to make lengths equal a power of 2
        A__   				=       int(
            2 ** np.ceil(np.loga(len(self.polyA		) + len(self.polyB		) - 1		)		)		)

        while len(self.polyA		) < self.c_max_length:
            self.polyA.append(0		)
        while len(self.polyB		) < self.c_max_length:
            self.polyB.append(0		)
        # A complex root used for the fourier transform
        A__   				=       complex(mpmath.root(x=1,n=self.c_max_length,k=1		)		)

        # The product
        A__   				=       self.__multiply()


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	int		)->   Any:





        '''simple docstring'''







        A__   				=       [[x] for x in self.polyA] if which == 'A' else [[x] for x in self.polyB]
        # Corner case
        if len(lowercase_		) <= 1:
            return dft[0]
        #
        A__   				=       self.c_max_length // 2
        while next_ncol > 0:
            A__   				=       [[] for i in range(lowercase_		)]
            A__   				=       self.root**next_ncol

            # First half of next step
            A__   				=       1
            for j in range(self.c_max_length // (next_ncol * 2)		):
                for i in range(lowercase_		):
                    new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]		)
                current_root *= root
            # Second half of next step
            A__   				=       1
            for j in range(self.c_max_length // (next_ncol * 2)		):
                for i in range(lowercase_		):
                    new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]		)
                current_root *= root
            # Update
            A__   				=       new_dft
            A__   				=       next_ncol // 2
        return dft[0]


    def 						snake_case__					(				self			:	str		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       self.__dft('A'		)
        A__   				=       self.__dft('B'		)
        A__   				=       [[dft_a[i] * dft_b[i] for i in range(self.c_max_length		)]]
        del dft_a
        del dft_b

        # Corner Case
        if len(inverce_c[0]		) <= 1:
            return inverce_c[0]
        # Inverse DFT
        A__   				=       2
        while next_ncol <= self.c_max_length:
            A__   				=       [[] for i in range(lowercase_		)]
            A__   				=       self.root ** (next_ncol // 2)
            A__   				=       1
            # First half of next step
            for j in range(self.c_max_length // next_ncol		):
                for i in range(next_ncol // 2		):
                    # Even positions
                    new_inverse_c[i].append(
                        (
                            inverce_c[i][j]
                            + inverce_c[i][j + self.c_max_length // next_ncol]
                        )
                        / 2		)
                    # Odd positions
                    new_inverse_c[i + next_ncol // 2].append(
                        (
                            inverce_c[i][j]
                            - inverce_c[i][j + self.c_max_length // next_ncol]
                        )
                        / (2 * current_root)		)
                current_root *= root
            # Update
            A__   				=       new_inverse_c
            next_ncol *= 2
        # Unpack
        A__   				=       [round(x[0].real,8		) + round(x[0].imag,8		) * 1J for x in inverce_c]

        # Remove leading 0's
        while inverce_c[-1] == 0:
            inverce_c.pop()
        return inverce_c


    def __str__(				self			:	Optional[Any]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       'A = ' + ' + '.join(
            F'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]		)		)
        A__   				=       'B = ' + ' + '.join(
            F'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]		)		)
        A__   				=       'A*B = ' + ' + '.join(
            F'{coef}*x^{i}' for coef, i in enumerate(self.product		)		)

        return F'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
     import doctest

     doctest.testmod()




import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)


class 			A						(							nn.Module			):







    """simple docstring"""





    def __init__(				self			:	Union[str, Any]		)->   List[str]:





        '''simple docstring'''







        super().__init__()
        A__   				=       nn.Linear(3,4		)
        A__   				=       nn.BatchNormad(4		)
        A__   				=       nn.Linear(4,5		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Union[str, Any]		)->   Tuple:





        '''simple docstring'''







        return self.lineara(self.batchnorm(self.lineara(lowercase_		)		)		)




class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[str]		)->   Any:





        '''simple docstring'''







        A__   				=       ModelForTest()
        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,model.state_dict()		)
            A__   				=       os.path.join(lowercase_,'index.json'		)
            self.assertTrue(os.path.isfile(lowercase_		)		)
            # TODO: add tests on what is inside the index

            for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                A__   				=       os.path.join(lowercase_,F'{key}.dat'		)
                self.assertTrue(os.path.isfile(lowercase_		)		)
                # TODO: add tests on the fact weights are properly loaded


    def 						snake_case__					(				self			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       [torch.floataa, torch.floataa, torch.bfloataa]

        for dtype in dtypes:
            A__   				=       torch.randn(2,3,dtype=lowercase_		)
            with TemporaryDirectory() as tmp_dir:
                A__   				=       offload_weight(lowercase_,'weight',lowercase_,{}		)
                A__   				=       os.path.join(lowercase_,'weight.dat'		)
                self.assertTrue(os.path.isfile(lowercase_		)		)
                self.assertDictEqual(lowercase_,{'weight': {'shape': [2, 3], 'dtype': str(lowercase_		).split('.'		)[1]}}		)

                A__   				=       load_offloaded_weight(lowercase_,index['weight']		)
                self.assertTrue(torch.equal(lowercase_,lowercase_		)		)


    def 						snake_case__					(				self			:	Optional[int]		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       ModelForTest()
        A__   				=       model.state_dict()
        A__   				=       {k: v for k, v in state_dict.items() if 'linear2' not in k}
        A__   				=       {k: v for k, v in state_dict.items() if 'linear2' in k}

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)

        A__   				=       {k: v for k, v in state_dict.items() if 'weight' in k}
        A__   				=       {k: v for k, v in state_dict.items() if 'weight' not in k}

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            # Duplicates are removed
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)


    def 						snake_case__					(				self			:	int		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       {'a.1': 0, 'a.10': 1, 'a.2': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1': 0, 'a.2': 2}		)

        A__   				=       {'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1.a': 0, 'a.2.a': 2}		)

