
'''simple docstring'''
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      UpperCamelCase_				(      A__  ,      A__=10						):
	a_      				=						[]
	for _ in range(A__						):
		lrs.append(scheduler.get_lr()[0]						)
		scheduler.step()
	return lrs






def      UpperCamelCase_				(      A__  ,      A__=10						):
	a_      				=						[]
	for step in range(A__						):
		lrs.append(scheduler.get_lr()[0]						)
		scheduler.step()
		if step == num_steps // 2:
			with tempfile.TemporaryDirectory() as tmpdirname:
				a_      				=						os.path.join(A__  ,      """schedule.bin"""						)
				torch.save(scheduler.state_dict()  ,      A__						)

				a_      				=						torch.load(A__						)
				scheduler.load_state_dict(A__						)
	return lrs







@require_torch
class   a_ (      unittest.TestCase					):




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




			def 					lowerCAmelCase__					(       self	):
				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(1_00	):
					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	):
				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(10_00	):
					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   a_ (      unittest.TestCase					):
			lowerCamelCase__	:			List[str]       =    nn.Linear(50		,  50					) if is_torch_available() else None
			lowerCamelCase__	:			int       =    AdamW(m.parameters()		,  lr=1_0.0					) if is_torch_available() else None
			lowerCamelCase__	:			Dict       =    10




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase=None	):
				self.assertEqual(len(UpperCAmelCase	)					,   len(UpperCAmelCase	)	)
				for a, b in zip(UpperCAmelCase					,   UpperCAmelCase	):
					self.assertAlmostEqual(UpperCAmelCase					,   UpperCAmelCase					,   delta=UpperCAmelCase					,   msg=UpperCAmelCase	)






			def 					lowerCAmelCase__					(       self	):
				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, 7.6_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():
					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   a_ :




			def __init__(       self					,   UpperCAmelCase	):
				a_      				=						fn




			def __call__(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				return self.fn(*UpperCAmelCase					,   **UpperCAmelCase	)






			@classmethod
			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						list(map(self					,   scheduler.lr_lambdas	)	)


'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowercase__											=subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
lowercase__											=(
    subprocess.check_output(F"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode('utf-8').split()
)

lowercase__											='|'.join(sys.argv[1:])
lowercase__											=re.compile(rF"""^({joined_dirs}).*?\.py$""")

lowercase__											=[x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
