

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 a(lowercase__      ,			lowercase__=10						):





	'''simple docstring'''
	snake_case_   		=			[]
	for _ in range(lowercase__						):
		lrs.append(scheduler.get_lr()[0]						)
		scheduler.step()
	return lrs





def a(lowercase__      ,			lowercase__=10						):





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

				snake_case_   		=			torch.load(lowercase__						)
				scheduler.load_state_dict(lowercase__						)
	return lrs


@require_torch
class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			torch.tensor([0.1, -0.2, -0.1]			,					requires_grad=__UpperCamelCase				)
				snake_case_   		=			torch.tensor([0.4, 0.2, -0.5]				)
				snake_case_   		=			nn.MSELoss()
				# No warmup, constant schedule, no gradient clipping
				snake_case_   		=			AdamW(params=[w]			,					lr=2E-1			,					weight_decay=0.0				)
				for _ in range(1_00				):
					snake_case_   		=			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				):

				"""simple docstring"""


				snake_case_   		=			torch.tensor([0.1, -0.2, -0.1]			,					requires_grad=__UpperCamelCase				)
				snake_case_   		=			torch.tensor([0.4, 0.2, -0.5]				)
				snake_case_   		=			nn.MSELoss()
				# No warmup, constant schedule, no gradient clipping
				snake_case_   		=			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(10_00				):
					snake_case_   		=			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 SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			__A    	=	nn.Linear(5_0				,    5_0				) if is_torch_available() else None
			__A    	=	AdamW(m.parameters()				,    lr=10.0				) if is_torch_available() else None
			__A    	=	1_0


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase=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				):

				"""simple docstring"""


				snake_case_   		=			{'num_warmup_steps': 2, 'num_training_steps': 10}
				# schedulers doct format
				# function: (sched_args_dict, expected_learning_rates)
				snake_case_   		=			{
				    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():
					snake_case_		,							snake_case_   		=			data

					snake_case_   		=			scheduler_func(self.optimizer			,					**__UpperCamelCase				)
					self.assertEqual(len([scheduler.get_lr()[0]]				)			,					1				)
					snake_case_   		=			unwrap_schedule(__UpperCamelCase			,					self.num_steps				)
					self.assertListAlmostEqual(
					    __UpperCamelCase			,					__UpperCamelCase			,					tol=1E-2			,					msg=f"""failed for {scheduler_func} in normal scheduler"""			,					)

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







class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			fn


			def __call__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


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







			@classmethod
			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


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



from itertools import count





def a(lowercase__ = 50						):





	'''simple docstring'''
	snake_case_   		=			[1] * min_block_length

	for n in count(lowercase__						):
		fill_count_functions.append(1						)

		for block_length in range(lowercase__      ,			n + 1						):
			for block_start in range(n - block_length						):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1000000:
			break

	return n


if __name__ == "__main__":
		print(f"""{solution() = }""")
