
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					(    lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Optional[Any]=1_0  ):
 __a			:  List[str]		    =  []
 for _ in range(lowerCAmelCase__  ):
  lrs.append(scheduler.get_lr()[0]  )
  scheduler.step()
 return lrs




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Any=1_0  ):
 __a			:  Optional[Any]		    =  []
 for step in range(lowerCAmelCase__  ):
  lrs.append(scheduler.get_lr()[0]  )
  scheduler.step()
  if step == num_steps // 2:
   with tempfile.TemporaryDirectory() as tmpdirname:
    __a			:  Tuple		    =  os.path.join(lowerCAmelCase__  ,       '''schedule.bin'''  )
    torch.save(scheduler.state_dict()  ,       lowerCAmelCase__  )

    __a			:  Tuple		    =  torch.load(lowerCAmelCase__  )
    scheduler.load_state_dict(lowerCAmelCase__  )
 return lrs



@require_torch
class 		UpperCamelCase__  (	unittest.TestCase   ):
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: List[Any] ,    snake_case_			: Any ,    snake_case_			: Any       ):
    self.assertEqual(len(snake_case_       ) ,    len(snake_case_       )       )
    for a, b in zip(snake_case_ ,    snake_case_       ):
     self.assertAlmostEqual(snake_case_ ,    snake_case_ ,    delta=snake_case_       )
   def 				lowerCAmelCase      (self			: int       ):
    __a			:  Tuple		    =  torch.tensor([0.1, -0.2, -0.1] ,    requires_grad=snake_case_       )
    __a			:  Any		    =  torch.tensor([0.4, 0.2, -0.5]       )
    __a			:  Any		    =  nn.MSELoss()
    # No warmup, constant schedule, no gradient clipping
    __a			:  Any		    =  AdamW(params=[w] ,    lr=2E-1 ,    weight_decay=0.0       )
    for _ in range(1_0_0       ):
     __a			:  List[str]		    =  criterion(snake_case_ ,    snake_case_       )
     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			: Tuple       ):
    __a			:  Any		    =  torch.tensor([0.1, -0.2, -0.1] ,    requires_grad=snake_case_       )
    __a			:  str		    =  torch.tensor([0.4, 0.2, -0.5]       )
    __a			:  List[str]		    =  nn.MSELoss()
    # No warmup, constant schedule, no gradient clipping
    __a			:  str		    =  Adafactor(
        params=[w] ,    lr=1E-2 ,    eps=(1E-30, 1E-3) ,    clip_threshold=1.0 ,    decay_rate=-0.8 ,    betaa=snake_case_ ,    weight_decay=0.0 ,    relative_step=snake_case_ ,    scale_parameter=snake_case_ ,    warmup_init=snake_case_ ,    )
    for _ in range(1_0_0_0       ):
     __a			:  Any		    =  criterion(snake_case_ ,    snake_case_       )
     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 		UpperCamelCase__  (	unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				nn.Linear(50   ,50   ) if is_torch_available() else None
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				AdamW(m.parameters()   ,lr=10.0   ) if is_torch_available() else None
   _SCREAMING_SNAKE_CASE				:							Any     	=				10
   def 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: str ,    snake_case_			: int ,    snake_case_			: int ,    snake_case_			: str=None       ):
    self.assertEqual(len(snake_case_       ) ,    len(snake_case_       )       )
    for a, b in zip(snake_case_ ,    snake_case_       ):
     self.assertAlmostEqual(snake_case_ ,    snake_case_ ,    delta=snake_case_ ,    msg=snake_case_       )







   def 				lowerCAmelCase      (self			: List[str]       ):
    __a			:  List[Any]		    =  {'''num_warmup_steps''': 2, '''num_training_steps''': 1_0}
    # schedulers doct format
    # function: (sched_args_dict, expected_learning_rates)
    __a			:  Optional[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():
     __a    ,	__a			:  List[str]		    =  data

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

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





class 		UpperCamelCase__  :
   def __init__(self			: str ,    snake_case_			: Dict       ):
    __a			:  int		    =  fn
   def __call__(self			: str ,    *snake_case_			: Union[str, Any] ,    **snake_case_			: Tuple       ):
    return self.fn(*snake_case_ ,    **snake_case_       )







   @classmethod
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: Dict       ):
    __a			:  List[Any]		    =  list(map(self ,    scheduler.lr_lambdas       )       )


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (	unittest.TestCase   ):
   @slow
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  List[str]		    =  AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,    return_dict=snake_case_       ).to(snake_case_       )
    __a			:  List[Any]		    =  AutoTokenizer.from_pretrained('''google/mt5-small'''       )

    __a			:  Optional[int]		    =  tokenizer('''Hello there''' ,    return_tensors='''pt'''       ).input_ids
    __a			:  Dict		    =  tokenizer('''Hi I am''' ,    return_tensors='''pt'''       ).input_ids

    __a			:  Optional[Any]		    =  model(input_ids.to(snake_case_       ) ,    labels=labels.to(snake_case_       )       ).loss
    __a			:  Tuple		    =  -(labels.shape[-1] * loss.item())

    __a			:  Dict		    =  -84.9127
    self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )
