

import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     Adafactor,
     AdamW,
     get_constant_schedule,
     get_constant_schedule_with_warmup,
     get_cosine_schedule_with_warmup,
     get_cosine_with_hard_restarts_schedule_with_warmup,
     get_inverse_sqrt_schedule,
     get_linear_schedule_with_warmup,
     get_polynomial_decay_schedule_with_warmup,
 )
def 		SCREAMING_SNAKE_CASE__					(				_UpperCAmelCase,   _UpperCAmelCase=10					)       ->			Tuple:



    '''simple docstring'''






    lowerCAmelCase					:      str           =   []
    for _ in range(lowercase_					):
        lrs.append(scheduler.get_lr()[0]					)
        scheduler.step()
    return lrs
def 		SCREAMING_SNAKE_CASE__					(				_UpperCAmelCase,   _UpperCAmelCase=10					)       ->			str:



    '''simple docstring'''






    lowerCAmelCase					:      Optional[int]           =   []
    for step in range(lowercase_					):
        lrs.append(scheduler.get_lr()[0]					)
        scheduler.step()
        if step == num_steps // 2:
            with tempfile.TemporaryDirectory() as tmpdirname:
                lowerCAmelCase					:      List[str]           =   os.path.join(lowercase_,   'schedule.bin'					)
                torch.save(scheduler.state_dict(),   lowercase_					)

                lowerCAmelCase					:      Optional[int]           =   torch.load(lowercase_					)
                scheduler.load_state_dict(lowercase_					)
    return lrs






@require_torch
class  __A					(				unittest.TestCase			):






    def 						lowercase__			(						self      :     Dict     ,					UpperCAmelCase_      :     Dict     ,					UpperCAmelCase_      :     Any     ,					UpperCAmelCase_      :     List[str]     ):
        self.assertEqual(len(__a     )     ,					len(__a     )     )
        for a, b in zip(__a     ,					__a     ):
            self.assertAlmostEqual(__a     ,					__a     ,					delta=__a     )






    def 						lowercase__			(						self      :     str     ):
        lowerCAmelCase					:      Optional[int]           =   torch.tensor([0.1, -0.2, -0.1]     ,					requires_grad=__a     )
        lowerCAmelCase					:      List[Any]           =   torch.tensor([0.4, 0.2, -0.5]     )
        lowerCAmelCase					:      Any           =   nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        lowerCAmelCase					:      Dict           =   AdamW(params=[w]     ,					lr=2E-1     ,					weight_decay=0.0     )
        for _ in range(100     ):
            lowerCAmelCase					:      List[Any]           =   criterion(__a     ,					__a     )
            loss.backward()
            optimizer.step()
            w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
            w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()     ,					[0.4, 0.2, -0.5]     ,					tol=1E-2     )






    def 						lowercase__			(						self      :     List[str]     ):
        lowerCAmelCase					:      List[Any]           =   torch.tensor([0.1, -0.2, -0.1]     ,					requires_grad=__a     )
        lowerCAmelCase					:      Any           =   torch.tensor([0.4, 0.2, -0.5]     )
        lowerCAmelCase					:      List[str]           =   nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        lowerCAmelCase					:      Dict           =   Adafactor(
            params=[w]     ,					lr=1E-2     ,					eps=(1E-30, 1E-3)     ,					clip_threshold=1.0     ,					decay_rate=-0.8     ,					betaa=__a     ,					weight_decay=0.0     ,					relative_step=__a     ,					scale_parameter=__a     ,					warmup_init=__a     ,					)
        for _ in range(1000     ):
            lowerCAmelCase					:      Optional[Any]           =   criterion(__a     ,					__a     )
            loss.backward()
            optimizer.step()
            w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
            w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()     ,					[0.4, 0.2, -0.5]     ,					tol=1E-2     )






@require_torch
class  __A					(				unittest.TestCase			):
    lowerCAmelCase_				:		Optional[Any]							=				nn.Linear(50    ,    50			) if is_torch_available() else None
    lowerCAmelCase_				:		List[Any]							=				AdamW(m.parameters()    ,    lr=1_0.0			) if is_torch_available() else None
    lowerCAmelCase_				:		List[str]							=				10






    def 						lowercase__			(						self      :     List[Any]     ,					UpperCAmelCase_      :     Tuple     ,					UpperCAmelCase_      :     List[Any]     ,					UpperCAmelCase_      :     Dict     ,					UpperCAmelCase_      :     str=None     ):
        self.assertEqual(len(__a     )     ,					len(__a     )     )
        for a, b in zip(__a     ,					__a     ):
            self.assertAlmostEqual(__a     ,					__a     ,					delta=__a     ,					msg=__a     )






    def 						lowercase__			(						self      :     Optional[Any]     ):
        lowerCAmelCase					:      Union[str, Any]           =   {"num_warmup_steps": 2, "num_training_steps": 10}
        # schedulers doct format
        # function: (sched_args_dict, expected_learning_rates)
        lowerCAmelCase					:      Optional[Any]           =   {
            get_constant_schedule: ({}, [10.0] * self.num_steps),
            get_constant_schedule_with_warmup: (
                {"num_warmup_steps": 4},
                [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
            ),
            get_linear_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
            ),
            get_cosine_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
            ),
            get_cosine_with_hard_restarts_schedule_with_warmup: (
                {**common_kwargs, "num_cycles": 2},
                [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
            ),
            get_polynomial_decay_schedule_with_warmup: (
                {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
                [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
            ),
            get_inverse_sqrt_schedule: (
                {"num_warmup_steps": 2},
                [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
            ),
        }

        for scheduler_func, data in scheds.items():
            lowerCAmelCase					:      List[str]           =   data

            lowerCAmelCase					:      List[str]           =   scheduler_func(self.optimizer     ,					**__a     )
            self.assertEqual(len([scheduler.get_lr()[0]]     )     ,					1     )
            lowerCAmelCase					:      Optional[int]           =   unwrap_schedule(__a     ,					self.num_steps     )
            self.assertListAlmostEqual(
                __a     ,					__a     ,					tol=1E-2     ,					msg=f"failed for {scheduler_func} in normal scheduler"     ,					)

            lowerCAmelCase					:      List[str]           =   scheduler_func(self.optimizer     ,					**__a     )
            if scheduler_func.__name__ != "get_constant_schedule":
                LambdaScheduleWrapper.wrap_scheduler(__a     )  # wrap to test picklability of the schedule
            lowerCAmelCase					:      int           =   unwrap_and_save_reload_schedule(__a     ,					self.num_steps     )
            self.assertListEqual(__a     ,					__a     ,					msg=f"failed for {scheduler_func} in save and reload"     )






class  __A					:






    def __init__(						self      :     List[Any]     ,					UpperCAmelCase_      :     Optional[int]     ):
        lowerCAmelCase					:      Any           =   fn






    def __call__(						self      :     Tuple     ,					*UpperCAmelCase_      :     Optional[Any]     ,					**UpperCAmelCase_      :     str     ):
        return self.fn(*__a     ,					**__a     )






    @classmethod
    def 						lowercase__			(						self      :     Any     ,					UpperCAmelCase_      :     Any     ):
        lowerCAmelCase					:      Any           =   list(map(self     ,					scheduler.lr_lambdas     )     )












"""simple docstring"""


import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_ = 1e-12		,lowercase_ = 100		,)       ->		tuple[float, np.ndarray]:




      """simple docstring"""

      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[1]
      # Ensure proper dimensionality.
      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[0]
      # Ensure inputs are either both complex or both real
      assert np.iscomplexobj(lowercase_      ) == np.iscomplexobj(lowercase_      )
      _UpperCamelCase   :						Dict    =							np.iscomplexobj(lowercase_      )
      if is_complex:
            # Ensure complex input_matrix is Hermitian
            assert np.array_equal(lowercase_		,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   :						List[Any]    =							False
      _UpperCamelCase   :						Optional[int]    =							0
      _UpperCamelCase   :						int    =							0
      _UpperCamelCase   :						Optional[int]    =							1e12

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

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

            if error <= error_tol or iterations >= max_iterations:
                  _UpperCamelCase   :						List[Any]    =							True

            _UpperCamelCase   :						Tuple    =							lambda_

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

      return lambda_, vector




def        lowercase__     (       )       ->		None:




      """simple docstring"""

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

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

            # Our implementation.
            _UpperCamelCase,	_UpperCamelCase   :						int    =							power_iteration(lowercase_		,lowercase_      )

            # Numpy implementation.

            # Get eigenvalues and eigenvectors using built-in numpy
            # eigh (eigh used for symmetric or hermetian matrices).
            _UpperCamelCase,	_UpperCamelCase   :						str    =							np.linalg.eigh(lowercase_      )
            # Last eigenvalue is the maximum one.
            _UpperCamelCase   :						Any    =							eigen_values[-1]
            # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
            _UpperCamelCase   :						Tuple    =							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(lowercase_      ) - np.abs(lowercase_      )      ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()
