

"""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   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	str=10)     ->						int:
   '''simple docstring'''


   __lowercase								=						[]
   for _ in range(UpperCamelCase_):
      lrs.append(scheduler.get_lr()[0])
      scheduler.step()
   return lrs




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	List[str]=10)     ->						str:
   '''simple docstring'''


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

            __lowercase								=						torch.load(UpperCamelCase_)
            scheduler.load_state_dict(UpperCamelCase_)
   return lrs




@require_torch
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 def  _lowercase			(      self							:      Tuple, UpperCAmelCase__							:      Optional[int], UpperCAmelCase__							:      int, UpperCAmelCase__							:      str     ):
    self.assertEqual(len(UpperCAmelCase__     ), len(UpperCAmelCase__     )     )
    for a, b in zip(UpperCAmelCase__, UpperCAmelCase__     ):
       self.assertAlmostEqual(UpperCAmelCase__, UpperCAmelCase__, delta=UpperCAmelCase__     )


 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						torch.tensor([0.1, -0.2, -0.1], requires_grad=UpperCAmelCase__     )
    __lowercase								=						torch.tensor([0.4, 0.2, -0.5]     )
    __lowercase								=						nn.MSELoss()
    # No warmup, constant schedule, no gradient clipping
    __lowercase								=						AdamW(params=[w], lr=2E-1, weight_decay=0.0     )
    for _ in range(1_0_0     ):
       __lowercase								=						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  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						torch.tensor([0.1, -0.2, -0.1], requires_grad=UpperCAmelCase__     )
    __lowercase								=						torch.tensor([0.4, 0.2, -0.5]     )
    __lowercase								=						nn.MSELoss()
    # No warmup, constant schedule, no gradient clipping
    __lowercase								=						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(1_0_0_0     ):
       __lowercase								=						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  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : Union[str, Any]       		=     nn.Linear(5_0					,5_0       ) if is_torch_available() else None
 __UpperCAmelCase     : Optional[Any]       		=     AdamW(m.parameters()					,lr=10.0       ) if is_torch_available() else None
 __UpperCAmelCase     : Optional[Any]       		=     1_0


 def  _lowercase			(      self							:      Union[str, Any], UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      Optional[Any], UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      Tuple=None     ):
    self.assertEqual(len(UpperCAmelCase__     ), len(UpperCAmelCase__     )     )
    for a, b in zip(UpperCAmelCase__, UpperCAmelCase__     ):
       self.assertAlmostEqual(UpperCAmelCase__, UpperCAmelCase__, delta=UpperCAmelCase__, msg=UpperCAmelCase__     )



 def  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						{"num_warmup_steps": 2, "num_training_steps": 1_0}
    # schedulers doct format
    # function: (sched_args_dict, expected_learning_rates)
    __lowercase								=						{
        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								=						data

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

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







class  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      List[Any], UpperCAmelCase__							:      Tuple     ):
    __lowercase								=						fn


 def __call__(      self							:      Any, *UpperCAmelCase__							:      List[str], **UpperCAmelCase__							:      Any     ):
    return self.fn(*UpperCAmelCase__, **UpperCAmelCase__     )



 @classmethod
 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      int     ):
    __lowercase								=						list(map(self, scheduler.lr_lambdas     )     )







"""simple docstring"""





_a							=							[
    'DownloadConfig',
    'DownloadManager',
    'DownloadMode',
    'StreamingDownloadManager',
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager




