






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    (			__lowercase :      int      ,					__lowercase :      Tuple=1_0     )    ->   Any:





    """simple docstring"""

    __A       =						[]
    for _ in range(__lowercase     ):
        lrs.append(scheduler.get_lr()[0]     )
        scheduler.step()
    return lrs



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any      ,					__lowercase :      Any=1_0     )    ->   Union[str, Any]:





    """simple docstring"""

    __A       =						[]
    for step in range(__lowercase     ):
        lrs.append(scheduler.get_lr()[0]     )
        scheduler.step()
        if step == num_steps // 2:
            with tempfile.TemporaryDirectory() as tmpdirname:
                __A       =						os.path.join(__lowercase      ,					"""schedule.bin"""     )
                torch.save(scheduler.state_dict()      ,					__lowercase     )

                __A       =						torch.load(__lowercase     )
                scheduler.load_state_dict(__lowercase     )
    return lrs


@require_torch
class    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Union[str, Any]					):


         """simple docstring"""



         self.assertEqual(len(UpperCamelCase_					)			,						len(UpperCamelCase_					)					)
         for a, b in zip(UpperCamelCase_			,						UpperCamelCase_					):
             self.assertAlmostEqual(UpperCamelCase_			,						UpperCamelCase_			,						delta=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         __A       =						torch.tensor([0.1, -0.2, -0.1]			,						requires_grad=UpperCamelCase_					)
         __A       =						torch.tensor([0.4, 0.2, -0.5]					)
         __A       =						nn.MSELoss()
         # No warmup, constant schedule, no gradient clipping
         __A       =						AdamW(params=[w]			,						lr=2e-1			,						weight_decay=0.0					)
         for _ in range(100					):
             __A       =						criterion(UpperCamelCase_			,						UpperCamelCase_					)
             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 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						torch.tensor([0.1, -0.2, -0.1]			,						requires_grad=UpperCamelCase_					)
         __A       =						torch.tensor([0.4, 0.2, -0.5]					)
         __A       =						nn.MSELoss()
         # No warmup, constant schedule, no gradient clipping
         __A       =						Adafactor(
             params=[w]			,						lr=1e-2			,						eps=(1e-30, 1e-3)			,						clip_threshold=1.0			,						decay_rate=-0.8			,						betaa=UpperCamelCase_			,						weight_decay=0.0			,						relative_step=UpperCamelCase_			,						scale_parameter=UpperCamelCase_			,						warmup_init=UpperCamelCase_			,						)
         for _ in range(1_000					):
             __A       =						criterion(UpperCamelCase_			,						UpperCamelCase_					)
             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    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					nn.Linear(50      ,   50	) if is_torch_available() else None
     SCREAMING_SNAKE_CASE     =					AdamW(m.parameters()      ,   lr=10.0	) if is_torch_available() else None
     SCREAMING_SNAKE_CASE     =					10




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      Any=None					):


         """simple docstring"""



         self.assertEqual(len(UpperCamelCase_					)			,						len(UpperCamelCase_					)					)
         for a, b in zip(UpperCamelCase_			,						UpperCamelCase_					):
             self.assertAlmostEqual(UpperCamelCase_			,						UpperCamelCase_			,						delta=UpperCamelCase_			,						msg=UpperCamelCase_					)





     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						{"""num_warmup_steps""": 2, """num_training_steps""": 10}
         # schedulers doct format
         # function: (sched_args_dict, expected_learning_rates)
         __A       =						{
             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, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
             ),
             get_inverse_sqrt_schedule: (
                 {"""num_warmup_steps""": 2},
                 [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
             ),
         }

         for scheduler_func, data in scheds.items():
             __A     ,						__A       =						data

             __A       =						scheduler_func(self.optimizer			,						**UpperCamelCase_					)
             self.assertEqual(len([scheduler.get_lr()[0]]					)			,						1					)
             __A       =						unwrap_schedule(UpperCamelCase_			,						self.num_steps					)
             self.assertListAlmostEqual(
                 UpperCamelCase_			,						UpperCamelCase_			,						tol=1e-2			,						msg=F"failed for {scheduler_func} in normal scheduler"			,						)

             __A       =						scheduler_func(self.optimizer			,						**UpperCamelCase_					)
             if scheduler_func.__name__ != "get_constant_schedule":
                 LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase_					)  # wrap to test picklability of the schedule
             __A       =						unwrap_and_save_reload_schedule(UpperCamelCase_			,						self.num_steps					)
             self.assertListEqual(UpperCamelCase_			,						UpperCamelCase_			,						msg=F"failed for {scheduler_func} in save and reload"					)


class    __lowercase   :







     '''simple docstring'''




     def __init__(					self							:      Optional[int]			,						UpperCamelCase_							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						fn




     def __call__(					self							:      Tuple			,						*UpperCamelCase_							:      Dict			,						**UpperCamelCase_							:      int					):


         """simple docstring"""



         return self.fn(*UpperCamelCase_			,						**UpperCamelCase_					)





     @classmethod
     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						list(map(self			,						scheduler.lr_lambdas					)					)












from __future__ import annotations

from typing import Any


class    __lowercase   :







     '''simple docstring'''




     def __init__(					self							:      Dict			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      float = 0					):


         """simple docstring"""



         __A     ,						__A       =						row, column
         __A       =						[[default_value for c in range(UpperCamelCase_					)] for r in range(UpperCamelCase_					)]




     def __str__(					self							:      Any					):


         """simple docstring"""



         __A       =						F"Matrix consist of {self.row} rows and {self.column} columns\n"

         # Make string identifier
         __A       =						0
         for row_vector in self.array:
             for obj in row_vector:
                 __A       =						max(UpperCamelCase_			,						len(str(UpperCamelCase_					)					)					)
         __A       =						F"%{max_element_length}s"

         # Make string and return
         def single_line(UpperCamelCase_							:      list[float]					) -> str:
             nonlocal string_format_identifier
             __A       =						"""["""
             line += ", ".join(string_format_identifier % (obj,) for obj in row_vector					)
             line += "]"
             return line

         s += "\n".join(single_line(UpperCamelCase_					) for row_vector in self.array					)
         return s




     def __repr__(					self							:      Optional[Any]					):


         """simple docstring"""



         return str(self					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      tuple[int, int]					):


         """simple docstring"""



         if not (isinstance(UpperCamelCase_			,						(list, tuple)					) and len(UpperCamelCase_					) == 2):
             return False
         elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
             return False
         else:
             return True




     def __getitem__(					self							:      Optional[int]			,						UpperCamelCase_							:      tuple[int, int]					):


         """simple docstring"""



         assert self.validate_indicies(UpperCamelCase_					)
         return self.array[loc[0]][loc[1]]




     def __setitem__(					self							:      str			,						UpperCamelCase_							:      tuple[int, int]			,						UpperCamelCase_							:      float					):


         """simple docstring"""



         assert self.validate_indicies(UpperCamelCase_					)
         __A       =						value




     def __add__(					self							:      Optional[int]			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         assert isinstance(UpperCamelCase_			,						UpperCamelCase_					)
         assert self.row == another.row and self.column == another.column

         # Add
         __A       =						Matrix(self.row			,						self.column					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						self[r, c] + another[r, c]
         return result




     def __neg__(					self							:      Any					):


         """simple docstring"""



         __A       =						Matrix(self.row			,						self.column					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						-self[r, c]
         return result




     def __sub__(					self							:      List[Any]			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         return self + (-another)




     def __mul__(					self							:      Union[str, Any]			,						UpperCamelCase_							:      int | float | Matrix					):


         """simple docstring"""



         if isinstance(UpperCamelCase_			,						(int, float)					):  # Scalar multiplication
             __A       =						Matrix(self.row			,						self.column					)
             for r in range(self.row					):
                 for c in range(self.column					):
                     __A       =						self[r, c] * another
             return result
         elif isinstance(UpperCamelCase_			,						UpperCamelCase_					):  # Matrix multiplication
             assert self.column == another.row
             __A       =						Matrix(self.row			,						another.column					)
             for r in range(self.row					):
                 for c in range(another.column					):
                     for i in range(self.column					):
                         result[r, c] += self[r, i] * another[i, c]
             return result
         else:
             __A       =						F"Unsupported type given for another ({type(UpperCamelCase_					)})"
             raise TypeError(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						Matrix(self.column			,						self.row					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						self[r, c]
         return result





     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Matrix			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         assert isinstance(UpperCamelCase_			,						UpperCamelCase_					) and isinstance(UpperCamelCase_			,						UpperCamelCase_					)
         assert self.row == self.column == u.row == v.row  # u, v should be column vector
         assert u.column == v.column == 1  # u, v should be column vector

         # Calculate
         __A       =						v.transpose()
         __A       =						(v_t * self * u)[0, 0] + 1
         if numerator_factor == 0:
             return None  # It's not invertable
         return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



  def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





      """simple docstring"""

      __A       =						Matrix(3      ,					3      ,					0     )
      for i in range(3     ):
          __A       =						1
      print(f"a^(-1) is {ainv}"     )
      # u, v
      __A       =						Matrix(3      ,					1      ,					0     )
      __A     ,						__A     ,						__A       =						1, 2, -3
      __A       =						Matrix(3      ,					1      ,					0     )
      __A     ,						__A     ,						__A       =						4, -2, 5
      print(f"u is {u}"     )
      print(f"v is {v}"     )
      print(f"uv^T is {u * v.transpose()}"     )
      # Sherman Morrison
      print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowercase      ,					__lowercase     )}"     )



  def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





      """simple docstring"""

      import doctest

      doctest.testmod()






  testa()




