






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_				:  Union[str, Any]				,				lowerCAmelCase_				:  Optional[Any]=10					):
							__lowercase					:							List[Any]			=   []
							for _ in range(lowerCAmelCase_					):
														lrs.append(scheduler.get_lr()[0]					)
														scheduler.step()
							return lrs
def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  int=10					):
							__lowercase					:							List[str]			=   []
							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					:							Dict			=   torch.load(lowerCAmelCase_					)
																												scheduler.load_state_dict(lowerCAmelCase_					)
							return lrs

@require_torch
class 						lowerCAmelCase   (       unittest.TestCase	):

					'''simple docstring'''

					def 					lowerCAmelCase (     self :		List[str]						,   __a :		Union[str, Any]						,   __a :		Optional[Any]						,   __a :		Any							)	->     Tuple:


												"""simple docstring"""

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



					def 					lowerCAmelCase (     self :		Any							)	->     List[str]:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=__a							)
												__lowercase					:							List[str]			=   torch.tensor([0.4, 0.2, -0.5]							)
												__lowercase					:							Union[str, Any]			=   nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												__lowercase					:							Optional[Any]			=   AdamW(params=[w]						,   lr=2E-1						,   weight_decay=0.0							)
												for _ in range(100							):
																			__lowercase					:							str			=   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 					lowerCAmelCase (     self :		Any							)	->     Optional[int]:


												"""simple docstring"""

												__lowercase					:							Any			=   torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=__a							)
												__lowercase					:							str			=   torch.tensor([0.4, 0.2, -0.5]							)
												__lowercase					:							str			=   nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												__lowercase					:							Optional[Any]			=   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							):
																			__lowercase					:							int			=   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 						lowerCAmelCase   (       unittest.TestCase	):

					'''simple docstring'''

					_A  :	List[str]     =					nn.Linear(50					,				50	) if is_torch_available() else None
					_A  :	str     =					AdamW(m.parameters()					,				lr=1_0.0	) if is_torch_available() else None
					_A  :	str     =					10



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		Dict						,   __a :		Union[str, Any]						,   __a :		List[str]						,   __a :		int=None							)	->     Union[str, Any]:


												"""simple docstring"""

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



					def 					lowerCAmelCase (     self :		int							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   {"""num_warmup_steps""": 2, """num_training_steps""": 10}
												# schedulers doct format
												# function: (sched_args_dict, expected_learning_rates)
												__lowercase					:							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, 7.656, 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():
																			__lowercase	,	__lowercase					:							int			=   data

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

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







class 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		Any						,   __a :		Dict							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Dict			=   fn



					def __call__(     self :		Optional[Any]						,   *__a :		int						,   **__a :		List[str]							)	->     Optional[Any]:


												"""simple docstring"""

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



					@classmethod
					def 					lowerCAmelCase (     self :		Any						,   __a :		Optional[Any]							)	->     int:


												"""simple docstring"""

												__lowercase					:							int			=   list(map(self						,   scheduler.lr_lambdas							)							)







lowerCamelCase       :       Dict        =				'''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
	from .utils import rich