



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      lowerCAmelCase__		(   lowerCamelCase_		:		Tuple	,lowerCamelCase_		:		Optional[int]=10):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					[]
				for _ in range(lowerCamelCase_):
								lrs.append(scheduler.get_lr()[0])
								scheduler.step()
				return lrs






def      lowerCAmelCase__		(   lowerCamelCase_		:		str	,lowerCamelCase_		:		Any=10):





				'''simple docstring'''

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

																lowerCAmelCase__					:    Dict												=					torch.load(lowerCamelCase_)
																scheduler.load_state_dict(lowerCamelCase_)
				return lrs



@require_torch
class 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					torch.tensor([0.1, -0.2, -0.1]		,requires_grad=__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					torch.tensor([0.4, 0.2, -0.5]			)
						lowerCAmelCase__					:    Dict												=					nn.MSELoss()
						# No warmup, constant schedule, no gradient clipping
						lowerCAmelCase__					:    str												=					AdamW(params=[w]		,lr=2e-1		,weight_decay=0.0			)
						for _ in range(1_00			):
										lowerCAmelCase__					:    Optional[Any]												=					criterion(__lowerCamelCase		,__lowerCamelCase			)
										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			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					torch.tensor([0.1, -0.2, -0.1]		,requires_grad=__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					torch.tensor([0.4, 0.2, -0.5]			)
						lowerCAmelCase__					:    int												=					nn.MSELoss()
						# No warmup, constant schedule, no gradient clipping
						lowerCAmelCase__					:    Any												=					Adafactor(
						    params=[w]		,lr=1e-2		,eps=(1e-30, 1e-3)		,clip_threshold=1.0		,decay_rate=-0.8		,betaa=__lowerCamelCase		,weight_decay=0.0		,relative_step=__lowerCamelCase		,scale_parameter=__lowerCamelCase		,warmup_init=__lowerCamelCase		,)
						for _ in range(10_00			):
										lowerCAmelCase__					:    Optional[int]												=					criterion(__lowerCamelCase		,__lowerCamelCase			)
										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'''



		snake_case_     =nn.Linear(50				,    50) if is_torch_available() else None
		snake_case_     =AdamW(m.parameters()				,    lr=10.0) if is_torch_available() else None
		snake_case_     =10



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase=None			)					->      Union[str, Any]:

						"""simple docstring"""






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



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

						"""simple docstring"""






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

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

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



class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					fn



		def __call__(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      Dict:

						"""simple docstring"""






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



		@classmethod
		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					list(map(self		,scheduler.lr_lambdas			)			)






import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		List[Any]=1024	,lowerCamelCase_		:		int=1024	,lowerCamelCase_		:		Dict=False	,**lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					AutoTokenizer.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[Any]												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''train'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    int												=					tok.pad_token_id

				def get_lens(lowerCamelCase_		:		Tuple):
								lowerCAmelCase__					:    Tuple												=					tqdm(
								    DataLoader(lowerCamelCase_	,batch_size=512	,num_workers=8	,shuffle=lowerCamelCase_	,collate_fn=ds.collate_fn)	,desc=str(ds.len_file)	,)
								lowerCAmelCase__					:    Tuple												=					[]
								for batch in dl:
												lowerCAmelCase__					:    Dict												=					batch['''input_ids'''].ne(lowerCamelCase_).sum(1).tolist()
												lowerCAmelCase__					:    Dict												=					batch['''labels'''].ne(lowerCamelCase_).sum(1).tolist()
												if consider_target:
																for src, tgt in zip(lowerCamelCase_	,lowerCamelCase_):
																				max_lens.append(max(lowerCamelCase_	,lowerCamelCase_))
												else:
																max_lens.extend(lowerCamelCase_)
								return max_lens

				lowerCAmelCase__					:    str												=					get_lens(lowerCamelCase_)
				lowerCAmelCase__					:    Tuple												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''val'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    Optional[int]												=					get_lens(lowerCamelCase_)
				pickle_save(lowerCamelCase_	,train_ds.len_file)
				pickle_save(lowerCamelCase_	,val_ds.len_file)


if __name__ == "__main__":
							fire.Fire(save_len_file)

