





'''simple docstring'''




import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowercase__       : List[str]         =       {
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

lowercase__       : Optional[Any]         =       logging.WARNING






def 			_lowerCAmelCase     (    )	->	Tuple:
     __A		:					List[str]						=			os.getenv('DATASETS_VERBOSITY'					,				lowercase_     )
     if env_level_str:
          if env_level_str in log_levels:
               return log_levels[env_level_str]
          else:
               logging.getLogger().warning(
                   f'Unknown option DATASETS_VERBOSITY={env_level_str}, '
                   f'has to be one of: { ", ".join(log_levels.keys()     ) }'     )
     return _default_log_level






def 			_lowerCAmelCase     (    )	->	str:
     return __name__.split('.'     )[0]






def 			_lowerCAmelCase     (    )	->	logging.Logger:
     return logging.getLogger(_get_library_name()     )






def 			_lowerCAmelCase     (    )	->	None:
     __A		:					List[Any]						=			_get_library_root_logger()
     library_root_logger.setLevel(_get_default_logging_level()     )






def 			_lowerCAmelCase     (    )	->	None:
     __A		:					List[str]						=			_get_library_root_logger()
     library_root_logger.setLevel(logging.NOTSET     )






def 			_lowerCAmelCase     (    __snake_case    :					str = None     )	->	logging.Logger:

     if name is None:
          __A		:					Any						=			_get_library_name()
     return logging.getLogger(lowercase_     )






def 			_lowerCAmelCase     (    )	->	int:
     return _get_library_root_logger().getEffectiveLevel()






def 			_lowerCAmelCase     (    __snake_case    :					Tuple     )	->	None:
     _get_library_root_logger().setLevel(lowercase_     )






def 			_lowerCAmelCase     (    )	->	List[Any]:
     return set_verbosity(lowercase_     )






def 			_lowerCAmelCase     (    )	->	List[Any]:
     return set_verbosity(lowercase_     )






def 			_lowerCAmelCase     (    )	->	int:
     return set_verbosity(lowercase_     )






def 			_lowerCAmelCase     (    )	->	Optional[int]:
     return set_verbosity(lowercase_     )






def 			_lowerCAmelCase     (    )	->	None:
     __A		:					int						=			False






def 			_lowerCAmelCase     (    )	->	None:
     __A		:					Tuple						=			True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      *_UpperCAmelCase		,      **_UpperCAmelCase):  # pylint: disable=unused-argument

          '''simple docstring'''






          __A		:					Dict						=			args[0] if args else None


     def __iter__(		self):

          '''simple docstring'''






          return iter(self._iterator)


     def __getattr__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          def empty_fn(*_UpperCAmelCase		,      **_UpperCAmelCase):  # pylint: disable=unused-argument
               return

          return empty_fn


     def __enter__(		self):

          '''simple docstring'''






          return self


     def __exit__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          return




lowercase__       : Optional[Any]         =       True


class 					SCREAMING_SNAKE_CASE       :


     def __call__(		self		,      *_UpperCAmelCase		,      _UpperCAmelCase=False		,      **_UpperCAmelCase):

          '''simple docstring'''






          if _tqdm_active and not disable:
               return tqdm_lib.tqdm(*__a		,      **__a)
          else:
               return EmptyTqdm(*__a		,      **__a)


     def 				SCREAMING_SNAKE_CASE    (		self		,      *_UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			None
          if _tqdm_active:
               return tqdm_lib.tqdm.set_lock(*__a		,      **__a)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          if _tqdm_active:
               return tqdm_lib.tqdm.get_lock()




lowercase__       : Any         =       _tqdm_cls()






def 			_lowerCAmelCase     (    )	->	bool:
     global _tqdm_active
     return bool(_tqdm_active     )






def 			_lowerCAmelCase     (    )	->	str:
     global _tqdm_active
     __A		:					int						=			True







def 			_lowerCAmelCase     (    )	->	Tuple:
     global _tqdm_active
     __A		:					List[Any]						=			False






"""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        lowercase__     (       lowercase_		,lowercase_=10      )       ->		Tuple:




      """simple docstring"""

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




def        lowercase__     (       lowercase_		,lowercase_=10      )       ->		str:




      """simple docstring"""

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

                        _UpperCamelCase   :						Optional[int]    =							torch.load(lowercase_      )
                        scheduler.load_state_dict(lowercase_      )
      return lrs



@require_torch
class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def  __SCREAMING_SNAKE_CASE					(		self :							Dict       , __a :							Dict       , __a :							Any       , __a :							List[str]						)		->					List[str]:
        self.assertEqual(len(__a						)       , len(__a						)						)
        for a, b in zip(__a       , __a						):
              self.assertAlmostEqual(__a       , __a       , delta=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					str:
        _UpperCamelCase   :						Optional[int]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						Any    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							AdamW(params=[w]       , lr=2e-1       , weight_decay=0.0						)
        for _ in range(100						):
              _UpperCamelCase   :						List[Any]    =							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  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Tuple:
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						Any    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						List[str]    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							Adafactor(
            params=[w]       , lr=1e-2       , eps=(1e-3_0, 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						):
              _UpperCamelCase   :						Optional[Any]    =							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   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       nn.Linear(50		,	50 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[Any]						  =       AdamW(m.parameters()		,	lr=10.0 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[str]						  =       10

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							Tuple       , __a :							List[Any]       , __a :							Dict       , __a :							str=None						)		->					List[Any]:
        self.assertEqual(len(__a						)       , len(__a						)						)
        for a, b in zip(__a       , __a						):
              self.assertAlmostEqual(__a       , __a       , delta=__a       , msg=__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					List[Any]:
        _UpperCamelCase   :						Union[str, Any]    =							{"num_warmup_steps": 2, "num_training_steps": 10}
        # schedulers doct format
        # function: (sched_args_dict, expected_learning_rates)
        _UpperCamelCase   :						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, 7.6_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():
              _UpperCamelCase,	_UpperCamelCase   :						List[str]    =							data

              _UpperCamelCase   :						List[str]    =							scheduler_func(self.optimizer       , **__a						)
              self.assertEqual(len([scheduler.get_lr()[0]]						)       , 1						)
              _UpperCamelCase   :						Optional[int]    =							unwrap_schedule(__a       , self.num_steps						)
              self.assertListAlmostEqual(
                  __a       , __a       , tol=1e-2       , msg=F'''failed for {scheduler_func} in normal scheduler'''       , )

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





class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							List[Any]       , __a :							Optional[int]						)		->					Tuple:
        _UpperCamelCase   :						Any    =							fn

  def __call__(		self :							Tuple       , *__a :							Optional[Any]       , **__a :							str						)		->					Any:
        return self.fn(*__a       , **__a						)


  @classmethod
  def  __SCREAMING_SNAKE_CASE					(		self :							Any       , __a :							Any						)		->					Tuple:
        _UpperCamelCase   :						Any    =							list(map(self       , scheduler.lr_lambdas						)						)
