




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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=10				)			->Union[str, Any]:






		"""simple docstring"""


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

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=10				)			->Optional[int]:






		"""simple docstring"""


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

								__lowercase				:		Optional[int]												=				torch.load(lowerCAmelCase_				)
								scheduler.load_state_dict(lowerCAmelCase_				)
		return lrs




@require_torch
class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	List[Any]    ,   lowercase__					:	Any    ,   lowercase__					:	int    ,   lowercase__					:	Any						):
						self.assertEqual(len(lowercase__						)    ,   len(lowercase__						)						)
						for a, b in zip(lowercase__    ,   lowercase__						):
								self.assertAlmostEqual(lowercase__    ,   lowercase__    ,   delta=lowercase__						)



				def       snake_case    (     self					:	Optional[Any]						):
						__lowercase				:		Union[str, Any]												=				torch.tensor([0.1, -0.2, -0.1]    ,   requires_grad=lowercase__						)
						__lowercase				:		List[str]												=				torch.tensor([0.4, 0.2, -0.5]						)
						__lowercase				:		Optional[Any]												=				nn.MSELoss()
						# No warmup, constant schedule, no gradient clipping
						__lowercase				:		List[Any]												=				AdamW(params=[w]    ,   lr=2e-1    ,   weight_decay=0.0						)
						for _ in range(1_0_0						):
								__lowercase				:		str												=				criterion(lowercase__    ,   lowercase__						)
								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       snake_case    (     self					:	str						):
						__lowercase				:		Tuple												=				torch.tensor([0.1, -0.2, -0.1]    ,   requires_grad=lowercase__						)
						__lowercase				:		Optional[Any]												=				torch.tensor([0.4, 0.2, -0.5]						)
						__lowercase				:		Optional[int]												=				nn.MSELoss()
						# No warmup, constant schedule, no gradient clipping
						__lowercase				:		int												=				Adafactor(
						    params=[w]    ,   lr=1e-2    ,   eps=(1e-30, 1e-3)    ,   clip_threshold=1.0    ,   decay_rate=-0.8    ,   betaa=lowercase__    ,   weight_decay=0.0    ,   relative_step=lowercase__    ,   scale_parameter=lowercase__    ,   warmup_init=lowercase__    ,   )
						for _ in range(1_0_0_0						):
								__lowercase				:		Optional[int]												=				criterion(lowercase__    ,   lowercase__						)
								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       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     Dict       =      nn.Linear(50  ,			50    ) if is_torch_available() else None
				__UpperCAmelCase :     Optional[Any]       =      AdamW(m.parameters()  ,			lr=10.0    ) if is_torch_available() else None
				__UpperCAmelCase :     Union[str, Any]       =      10



				def       snake_case    (     self					:	List[Any]    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	Dict    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Dict=None						):
						self.assertEqual(len(lowercase__						)    ,   len(lowercase__						)						)
						for a, b in zip(lowercase__    ,   lowercase__						):
								self.assertAlmostEqual(lowercase__    ,   lowercase__    ,   delta=lowercase__    ,   msg=lowercase__						)



				def       snake_case    (     self					:	Optional[int]						):
						__lowercase				:		str												=				{'''num_warmup_steps''': 2, '''num_training_steps''': 1_0}
						# schedulers doct format
						# function: (sched_args_dict, expected_learning_rates)
						__lowercase				:		Optional[int]												=				{
						    get_constant_schedule: ({}, [1_0.0] * self.num_steps),
						    get_constant_schedule_with_warmup: (
						        {'''num_warmup_steps''': 4},
						        [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
						    ),
						    get_linear_schedule_with_warmup: (
						        {**common_kwargs},
						        [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
						    ),
						    get_cosine_schedule_with_warmup: (
						        {**common_kwargs},
						        [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
						    ),
						    get_cosine_with_hard_restarts_schedule_with_warmup: (
						        {**common_kwargs, '''num_cycles''': 2},
						        [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
						    ),
						    get_polynomial_decay_schedule_with_warmup: (
						        {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
						        [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
						    ),
						    get_inverse_sqrt_schedule: (
						        {'''num_warmup_steps''': 2},
						        [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
						    ),
						}

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

								__lowercase				:		str												=				scheduler_func(self.optimizer    ,   **lowercase__						)
								self.assertEqual(len([scheduler.get_lr()[0]]						)    ,   1						)
								__lowercase				:		Union[str, Any]												=				unwrap_schedule(lowercase__    ,   self.num_steps						)
								self.assertListAlmostEqual(
								    lowercase__    ,   lowercase__    ,   tol=1e-2    ,   msg=f'failed for {scheduler_func} in normal scheduler'    ,   )

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





class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	Union[str, Any]    ,   lowercase__					:	Optional[int]						):
						__lowercase				:		str												=				fn



				def __call__(     self					:	List[Any]    ,   *lowercase__					:	List[str]    ,   **lowercase__					:	Optional[Any]						):
						return self.fn(*lowercase__    ,   **lowercase__						)



				@classmethod
				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	Any						):
						__lowercase				:		Union[str, Any]												=				list(map(self    ,   scheduler.lr_lambdas						)						)






"""simple docstring"""







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  )			->list[float]:






		"""simple docstring"""


		__lowercase						,__lowercase				:		Dict												=				coefficient_matrix.shape
		__lowercase						,__lowercase				:		Tuple												=				constant_matrix.shape

		if rowsa != colsa:
				__lowercase				:		str												=				F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if colsa != 1:
				__lowercase				:		Dict												=				F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if rowsa != rowsa:
				__lowercase				:		Optional[Any]												=				(
				    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
				    F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if len(_lowerCamelCase				) != rowsa:
				__lowercase				:		Dict												=				(
				    "Number of initial values must be equal to number of rows in coefficient "
				    F'matrix but received {len(_lowerCamelCase				)} and {rowsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if iterations <= 0:
				raise ValueError("Iterations must be at least 1"				)

		__lowercase				:		NDArray[floataa]												=				np.concatenate(
		    (coefficient_matrix, constant_matrix),  axis=1				)

		__lowercase						,__lowercase				:		str												=				table.shape

		strictly_diagonally_dominant(_lowerCamelCase				)

		# Iterates the whole matrix for given number of times
		for _ in range(_lowerCamelCase				):
				__lowercase				:		List[Any]												=				[]
				for row in range(_lowerCamelCase				):
						__lowercase				:		Optional[Any]												=				0
						for col in range(_lowerCamelCase				):
								if col == row:
										__lowercase				:		Tuple												=				table[row][col]
								elif col == cols - 1:
										__lowercase				:		Tuple												=				table[row][col]
								else:
										temp += (-1) * table[row][col] * init_val[col]
						__lowercase				:		List[Any]												=				(temp + val) / denom
						new_val.append(_lowerCamelCase				)
				__lowercase				:		Dict												=				new_val

		return [float(_lowerCamelCase				) for i in new_val]

def 						snake_case__       (					_lowerCamelCase				)			->bool:






		"""simple docstring"""


		__lowercase						,__lowercase				:		int												=				table.shape

		__lowercase				:		Optional[int]												=				True

		for i in range(0,  _lowerCamelCase				):
				__lowercase				:		Optional[int]												=				0
				for j in range(0,  cols - 1				):
						if i == j:
								continue
						else:
								total += table[i][j]

				if table[i][i] <= total:
						raise ValueError("Coefficient matrix is not strictly diagonally dominant"				)

		return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
						import doctest

						doctest.testmod()
