



'''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_     (							_lowerCAmelCase       ,_lowerCAmelCase=10       )      ->      Any:
  __lowerCamelCase       :       Optional[Any]           = []
  for _ in range(_lowerCAmelCase       ):
    lrs.append(scheduler.get_lr()[0]       )
    scheduler.step()
  return lrs

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=10       )      ->      Union[str, Any]:
  __lowerCamelCase       :       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:
        __lowerCamelCase       :       Optional[int]           = os.path.join(_lowerCAmelCase       ,'schedule.bin'       )
        torch.save(scheduler.state_dict()       ,_lowerCAmelCase       )

        __lowerCamelCase       :       Optional[Any]           = torch.load(_lowerCAmelCase       )
        scheduler.load_state_dict(_lowerCAmelCase       )
  return lrs






@require_torch
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	str			,	_a      :	Tuple			,	_a      :	Tuple			,	_a      :	Any   )					->					Tuple:
    self.assertEqual(len(_a   )			,	len(_a   )   )
    for a, b in zip(_a			,	_a   ):
      self.assertAlmostEqual(_a			,	_a			,	delta=_a   )




  def 						_lowercase     ( self      :	int   )					->					int:
    __lowerCamelCase       :       str           = torch.tensor([0.1, -0.2, -0.1]			,	requires_grad=_a   )
    __lowerCamelCase       :       Union[str, Any]           = torch.tensor([0.4, 0.2, -0.5]   )
    __lowerCamelCase       :       Optional[int]           = nn.MSELoss()
    # No warmup, constant schedule, no gradient clipping
    __lowerCamelCase       :       Optional[Any]           = AdamW(params=[w]			,	lr=2e-1			,	weight_decay=0.0   )
    for _ in range(100   ):
      __lowerCamelCase       :       Dict           = 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 						_lowercase     ( self      :	Optional[int]   )					->					Any:
    __lowerCamelCase       :       Dict           = torch.tensor([0.1, -0.2, -0.1]			,	requires_grad=_a   )
    __lowerCamelCase       :       Dict           = torch.tensor([0.4, 0.2, -0.5]   )
    __lowerCamelCase       :       Any           = nn.MSELoss()
    # No warmup, constant schedule, no gradient clipping
    __lowerCamelCase       :       Union[str, 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   ):
      __lowerCamelCase       :       Union[str, 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 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




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




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	Any			,	_a      :	Optional[Any]			,	_a      :	Tuple			,	_a      :	Union[str, Any]=None   )					->					Optional[Any]:
    self.assertEqual(len(_a   )			,	len(_a   )   )
    for a, b in zip(_a			,	_a   ):
      self.assertAlmostEqual(_a			,	_a			,	delta=_a			,	msg=_a   )




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					List[Any]:
    __lowerCamelCase       :       List[str]           = {'num_warmup_steps': 2, 'num_training_steps': 10}
    # schedulers doct format
    # function: (sched_args_dict, expected_learning_rates)
    __lowerCamelCase       :       Tuple           = {
        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       :       Any           = data

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

      __lowerCamelCase       :       int           = scheduler_func(self.optimizer			,	**_a   )
      if scheduler_func.__name__ != "get_constant_schedule":
        LambdaScheduleWrapper.wrap_scheduler(_a   )  # wrap to test picklability of the schedule
      __lowerCamelCase       :       List[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      :	Optional[Any]			,	_a      :	Dict   )					->					Optional[Any]:
    __lowerCamelCase       :       Optional[Any]           = fn




  def __call__( self      :	Optional[int]			,	*_a      :	int			,	**_a      :	int   )					->					Dict:
    return self.fn(*_a			,	**_a   )




  @classmethod
  def 						_lowercase     ( self      :	int			,	_a      :	Tuple   )					->					Union[str, Any]:
    __lowerCamelCase       :       int           = list(map(self			,	scheduler.lr_lambdas   )   )











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase											=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BioGptForCausalLM',
       'BioGptForTokenClassification',
       'BioGptForSequenceClassification',
       'BioGptModel',
       'BioGptPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






