

'''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 	UpperCamelCase   (       lowercase_						:    List[str]     ,					lowercase_						:    Union[str, Any]=1_0	)		->	List[Any]:




  '''simple docstring'''



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




def 	UpperCamelCase   (       lowercase_						:    List[Any]     ,					lowercase_						:    str=1_0	)		->	Dict:




  '''simple docstring'''



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

        lowercase									=torch.load(lowercase_	)
        scheduler.load_state_dict(lowercase_	)
  return lrs

@require_torch
class 						__magic_name__   (							unittest.TestCase ):







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_   ):
   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 						_A(       self   ):
   lowercase									=torch.tensor([0.1, -0.2, -0.1]  , requires_grad=snake_case_   )
   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_00   ):
     lowercase									=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 						_A(       self   ):
   lowercase									=torch.tensor([0.1, -0.2, -0.1]  , requires_grad=snake_case_   )
   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=snake_case_  , weight_decay=0.0  , relative_step=snake_case_  , scale_parameter=snake_case_  , warmup_init=snake_case_  , )
   for _ in range(10_00   ):
     lowercase									=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 						__magic_name__   (							unittest.TestCase ):
 UpperCamelCase__     =       nn.Linear(50 ,				50 ) if is_torch_available() else None
 UpperCamelCase__     =       AdamW(m.parameters() ,				lr=1_0.0 ) if is_torch_available() else None
 UpperCamelCase__     =       10







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_=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 						_A(       self   ):
   lowercase									={'''num_warmup_steps''': 2, '''num_training_steps''': 10}
   # 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, 7.6_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():
     lowercase							, lowercase									=data

     lowercase									=scheduler_func(self.optimizer  , **snake_case_   )
     self.assertEqual(len([scheduler.get_lr()[0]]   )  , 1   )
     lowercase									=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'  , )

     lowercase									=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
     lowercase									=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 						__magic_name__   :







 def __init__(       self  , snake_case_   ):
   lowercase									=fn







 def __call__(       self  , *snake_case_  , **snake_case_   ):
   return self.fn(*snake_case_  , **snake_case_   )







 @classmethod
 def 						_A(       self  , snake_case_   ):
   lowercase									=list(map(self  , scheduler.lr_lambdas   )   )




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__							:						Tuple							     =   {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Tuple							     =   ["""RobertaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Optional[int]							     =   [
       """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RobertaForCausalLM""",
       """RobertaForMaskedLM""",
       """RobertaForMultipleChoice""",
       """RobertaForQuestionAnswering""",
       """RobertaForSequenceClassification""",
       """RobertaForTokenClassification""",
       """RobertaModel""",
       """RobertaPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						List[str]							     =   [
       """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFRobertaForCausalLM""",
       """TFRobertaForMaskedLM""",
       """TFRobertaForMultipleChoice""",
       """TFRobertaForQuestionAnswering""",
       """TFRobertaForSequenceClassification""",
       """TFRobertaForTokenClassification""",
       """TFRobertaMainLayer""",
       """TFRobertaModel""",
       """TFRobertaPreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						str							     =   [
       """FlaxRobertaForCausalLM""",
       """FlaxRobertaForMaskedLM""",
       """FlaxRobertaForMultipleChoice""",
       """FlaxRobertaForQuestionAnswering""",
       """FlaxRobertaForSequenceClassification""",
       """FlaxRobertaForTokenClassification""",
       """FlaxRobertaModel""",
       """FlaxRobertaPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
   from .tokenization_roberta import RobertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roberta_fast import RobertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta import (
          ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaForCausalLM,
          RobertaForMaskedLM,
          RobertaForMultipleChoice,
          RobertaForQuestionAnswering,
          RobertaForSequenceClassification,
          RobertaForTokenClassification,
          RobertaModel,
          RobertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta import (
          TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaForCausalLM,
          TFRobertaForMaskedLM,
          TFRobertaForMultipleChoice,
          TFRobertaForQuestionAnswering,
          TFRobertaForSequenceClassification,
          TFRobertaForTokenClassification,
          TFRobertaMainLayer,
          TFRobertaModel,
          TFRobertaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta import (
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaModel,
          FlaxRobertaPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase__							:						List[Any]							     =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

