



'''simple docstring'''





import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
           DistilBertForMaskedLM,
           DistilBertForMultipleChoice,
           DistilBertForQuestionAnswering,
           DistilBertForSequenceClassification,
           DistilBertForTokenClassification,
           DistilBertModel,
       )







class     UpperCAmelCase_       (       lowercase_	):
    """simple docstring"""







    def __init__(    self					:     Union[str, Any] ,     snake_case_					:     Tuple ,     snake_case_					:     Dict=13 ,     snake_case_					:     Any=7 ,     snake_case_					:     List[Any]=True ,     snake_case_					:     List[Any]=True ,     snake_case_					:     Optional[Any]=False ,     snake_case_					:     List[str]=True ,     snake_case_					:     List[str]=99 ,     snake_case_					:     str=32 ,     snake_case_					:     Tuple=5 ,     snake_case_					:     List[str]=4 ,     snake_case_					:     Optional[Any]=37 ,     snake_case_					:     Dict="gelu" ,     snake_case_					:     Dict=0.1 ,     snake_case_					:     List[Any]=0.1 ,     snake_case_					:     Tuple=512 ,     snake_case_					:     int=16 ,     snake_case_					:     Optional[Any]=2 ,     snake_case_					:     Any=0.02 ,     snake_case_					:     Any=3 ,     snake_case_					:     Tuple=4 ,     snake_case_					:     Optional[Any]=None ,     ):
          snake_case__  :    List[str]    						=      parent
          snake_case__  :    Union[str, Any]    						=      batch_size
          snake_case__  :    Dict    						=      seq_length
          snake_case__  :    Optional[Any]    						=      is_training
          snake_case__  :    int    						=      use_input_mask
          snake_case__  :    Optional[int]    						=      use_token_type_ids
          snake_case__  :    int    						=      use_labels
          snake_case__  :    Dict    						=      vocab_size
          snake_case__  :    Optional[Any]    						=      hidden_size
          snake_case__  :    List[Any]    						=      num_hidden_layers
          snake_case__  :    Union[str, Any]    						=      num_attention_heads
          snake_case__  :    List[str]    						=      intermediate_size
          snake_case__  :    List[str]    						=      hidden_act
          snake_case__  :    Dict    						=      hidden_dropout_prob
          snake_case__  :    Any    						=      attention_probs_dropout_prob
          snake_case__  :    List[Any]    						=      max_position_embeddings
          snake_case__  :    Optional[int]    						=      type_vocab_size
          snake_case__  :    int    						=      type_sequence_label_size
          snake_case__  :    int    						=      initializer_range
          snake_case__  :    Dict    						=      num_labels
          snake_case__  :    Union[str, Any]    						=      num_choices
          snake_case__  :    Any    						=      scope




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    List[str]    						=      ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

          snake_case__  :    Optional[int]    						=      None
          if self.use_input_mask:
                snake_case__  :    Any    						=      random_attention_mask([self.batch_size, self.seq_length]						)

          snake_case__  :    List[str]    						=      None
          snake_case__  :    Union[str, Any]    						=      None
          snake_case__  :    Optional[int]    						=      None
          if self.use_labels:
                snake_case__  :    Dict    						=      ids_tensor([self.batch_size] ,     self.type_sequence_label_size						)
                snake_case__  :    List[Any]    						=      ids_tensor([self.batch_size, self.seq_length] ,     self.num_labels						)
                snake_case__  :    List[Any]    						=      ids_tensor([self.batch_size] ,     self.num_choices						)

          snake_case__  :    Optional[int]    						=      self.get_config()

          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




    def lowerCamelCase       (    self					:     Dict						):
          return DistilBertConfig(
              vocab_size=self.vocab_size ,     dim=self.hidden_size ,     n_layers=self.num_hidden_layers ,     n_heads=self.num_attention_heads ,     hidden_dim=self.intermediate_size ,     hidden_act=self.hidden_act ,     dropout=self.hidden_dropout_prob ,     attention_dropout=self.attention_probs_dropout_prob ,     max_position_embeddings=self.max_position_embeddings ,     initializer_range=self.initializer_range ,     )




    def lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     List[Any] ,     snake_case_					:     Dict ,     snake_case_					:     List[str] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Union[str, Any]						):
          snake_case__  :    Tuple    						=      DistilBertModel(config=SCREAMING_SNAKE_CASE_						)
          model.to(SCREAMING_SNAKE_CASE_						)
          model.eval()
          snake_case__  :    Optional[int]    						=      model(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_						)
          snake_case__  :    Dict    						=      model(SCREAMING_SNAKE_CASE_						)
          self.parent.assertEqual(result.last_hidden_state.shape ,     (self.batch_size, self.seq_length, self.hidden_size)						)




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     str ,     snake_case_					:     Optional[int] ,     snake_case_					:     Dict ,     snake_case_					:     str ,     snake_case_					:     int						):
          snake_case__  :    Optional[int]    						=      DistilBertForMaskedLM(config=SCREAMING_SNAKE_CASE_						)
          model.to(SCREAMING_SNAKE_CASE_						)
          model.eval()
          snake_case__  :    int    						=      model(SCREAMING_SNAKE_CASE_ ,     attention_mask=SCREAMING_SNAKE_CASE_ ,     labels=SCREAMING_SNAKE_CASE_						)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.seq_length, self.vocab_size)						)




    def lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     str ,     snake_case_					:     Any ,     snake_case_					:     Tuple ,     snake_case_					:     List[Any] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     Optional[Any]						):
          snake_case__  :    Any    						=      DistilBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_						)
          model.to(SCREAMING_SNAKE_CASE_						)
          model.eval()
          snake_case__  :    Any    						=      model(
              SCREAMING_SNAKE_CASE_ ,     attention_mask=SCREAMING_SNAKE_CASE_ ,     start_positions=SCREAMING_SNAKE_CASE_ ,     end_positions=SCREAMING_SNAKE_CASE_						)
          self.parent.assertEqual(result.start_logits.shape ,     (self.batch_size, self.seq_length)						)
          self.parent.assertEqual(result.end_logits.shape ,     (self.batch_size, self.seq_length)						)




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     Any ,     snake_case_					:     Dict ,     snake_case_					:     Tuple ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     int ,     snake_case_					:     Optional[Any]						):
          snake_case__  :    Optional[int]    						=      self.num_labels
          snake_case__  :    str    						=      DistilBertForSequenceClassification(SCREAMING_SNAKE_CASE_						)
          model.to(SCREAMING_SNAKE_CASE_						)
          model.eval()
          snake_case__  :    List[str]    						=      model(SCREAMING_SNAKE_CASE_ ,     attention_mask=SCREAMING_SNAKE_CASE_ ,     labels=SCREAMING_SNAKE_CASE_						)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_labels)						)




    def lowerCamelCase       (    self					:     List[str] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     Tuple ,     snake_case_					:     Any ,     snake_case_					:     List[Any] ,     snake_case_					:     Tuple ,     snake_case_					:     Optional[int]						):
          snake_case__  :    Union[str, Any]    						=      self.num_labels
          snake_case__  :    Union[str, Any]    						=      DistilBertForTokenClassification(config=SCREAMING_SNAKE_CASE_						)
          model.to(SCREAMING_SNAKE_CASE_						)
          model.eval()

          snake_case__  :    int    						=      model(SCREAMING_SNAKE_CASE_ ,     attention_mask=SCREAMING_SNAKE_CASE_ ,     labels=SCREAMING_SNAKE_CASE_						)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.seq_length, self.num_labels)						)




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     Any ,     snake_case_					:     Dict ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Optional[int] ,     snake_case_					:     List[str] ,     snake_case_					:     Union[str, Any]						):
          snake_case__  :    Tuple    						=      self.num_choices
          snake_case__  :    Union[str, Any]    						=      DistilBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_						)
          model.to(SCREAMING_SNAKE_CASE_						)
          model.eval()
          snake_case__  :    List[Any]    						=      input_ids.unsqueeze(1						).expand(-1 ,     self.num_choices ,     -1						).contiguous()
          snake_case__  :    int    						=      input_mask.unsqueeze(1						).expand(-1 ,     self.num_choices ,     -1						).contiguous()
          snake_case__  :    List[Any]    						=      model(
              SCREAMING_SNAKE_CASE_ ,     attention_mask=SCREAMING_SNAKE_CASE_ ,     labels=SCREAMING_SNAKE_CASE_ ,     )
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_choices)						)




    def lowerCamelCase       (    self					:     str						):
          snake_case__  :    Any    						=      self.prepare_config_and_inputs()
          (snake_case__)  :    str    						=      config_and_inputs
          snake_case__  :    List[str]    						=      {'input_ids': input_ids, 'attention_mask': input_mask}
          return config, inputs_dict







@require_torch
class     UpperCAmelCase_       (       lowercase_		,		lowercase_		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		(
        (
            DistilBertModel,
            DistilBertForMaskedLM,
            DistilBertForMultipleChoice,
            DistilBertForQuestionAnswering,
            DistilBertForSequenceClassification,
            DistilBertForTokenClassification,
        )
        if is_torch_available()
        else None
    )
    lowercase					=		(
        {
            "feature-extraction": DistilBertModel,
            "fill-mask": DistilBertForMaskedLM,
            "question-answering": DistilBertForQuestionAnswering,
            "text-classification": DistilBertForSequenceClassification,
            "token-classification": DistilBertForTokenClassification,
            "zero-shot": DistilBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowercase					=		True
    lowercase					=		True
    lowercase					=		True
    lowercase					=		True




    def lowerCamelCase       (    self					:     Optional[Any]						):
          snake_case__  :    Optional[int]    						=      DistilBertModelTester(self						)
          snake_case__  :    int    						=      ConfigTester(self ,     config_class=SCREAMING_SNAKE_CASE_ ,     dim=37						)




    def lowerCamelCase       (    self					:     Optional[Any]						):
          self.config_tester.run_common_tests()




    def lowerCamelCase       (    self					:     Union[str, Any]						):
          snake_case__  :    int    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_model(*SCREAMING_SNAKE_CASE_						)




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    str    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_masked_lm(*SCREAMING_SNAKE_CASE_						)




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    Optional[int]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_question_answering(*SCREAMING_SNAKE_CASE_						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    Optional[Any]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_sequence_classification(*SCREAMING_SNAKE_CASE_						)




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    str    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_token_classification(*SCREAMING_SNAKE_CASE_						)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    List[str]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_distilbert_for_multiple_choice(*SCREAMING_SNAKE_CASE_						)




    @slow
    def lowerCamelCase       (    self					:     Union[str, Any]						):
          for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__  :    Tuple    						=      DistilBertModel.from_pretrained(SCREAMING_SNAKE_CASE_						)
                self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)




    @slow
    @require_torch_gpu
    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    Dict    						=      self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                # BertForMultipleChoice behaves incorrectly in JIT environments.
                if model_class == DistilBertForMultipleChoice:
                      return

                snake_case__  :    List[str]    						=      True
                snake_case__  :    str    						=      model_class(config=SCREAMING_SNAKE_CASE_						)

                snake_case__  :    List[Any]    						=      self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_						)
                snake_case__  :    Optional[Any]    						=      torch.jit.trace(
                    SCREAMING_SNAKE_CASE_ ,     (inputs_dict["""input_ids"""].to("""cpu"""						), inputs_dict["""attention_mask"""].to("""cpu"""						))						)

                with tempfile.TemporaryDirectory() as tmp:
                      torch.jit.save(SCREAMING_SNAKE_CASE_ ,     os.path.join(SCREAMING_SNAKE_CASE_ ,     """traced_model.pt"""						)						)
                      snake_case__  :    Union[str, Any]    						=      torch.jit.load(os.path.join(SCREAMING_SNAKE_CASE_ ,     """traced_model.pt"""						) ,     map_location=SCREAMING_SNAKE_CASE_						)
                      loaded(inputs_dict["""input_ids"""].to(SCREAMING_SNAKE_CASE_						) ,     inputs_dict["""attention_mask"""].to(SCREAMING_SNAKE_CASE_						)						)







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







    @slow
    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    Tuple    						=      DistilBertModel.from_pretrained("""distilbert-base-uncased"""						)
          snake_case__  :    Dict    						=      torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]						)
          snake_case__  :    Optional[int]    						=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
          with torch.no_grad():
                snake_case__  :    List[str]    						=      model(SCREAMING_SNAKE_CASE_ ,     attention_mask=SCREAMING_SNAKE_CASE_						)[0]
          snake_case__  :    Optional[int]    						=      torch.Size((1, 11, 768)						)
          self.assertEqual(output.shape ,     SCREAMING_SNAKE_CASE_						)
          snake_case__  :    int    						=      torch.tensor(
              [[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]						)

          self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,     SCREAMING_SNAKE_CASE_ ,     atol=1E-4						)						)





"""simple docstring"""







import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowercase__		:				Optional[Any]          =	logging.get_logger(__name__)







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """linear"""
      _SCREAMING_SNAKE_CASE  				=     """cosine"""
      _SCREAMING_SNAKE_CASE  				=     """cosine_with_restarts"""
      _SCREAMING_SNAKE_CASE  				=     """polynomial"""
      _SCREAMING_SNAKE_CASE  				=     """constant"""
      _SCREAMING_SNAKE_CASE  				=     """constant_with_warmup"""
      _SCREAMING_SNAKE_CASE  				=     """piecewise_constant"""




def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int = -1						)						->   Tuple:

  """simple docstring"""




  return LambdaLR(lowerCAmelCase__    ,   lambda lowerCAmelCase__						: 1    ,   last_epoch=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int = -1						)						->   str:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				int						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1.0    ,   lowerCAmelCase__						)						)
    return 1.0

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				int = -1						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				str								= {}
  lowerCAmelCase_   :				Optional[Any]								= step_rules.split(','						)
  for rule_str in rule_list[:-1]:
    lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= rule_str.split(':'						)
    lowerCAmelCase_   :				List[str]								= int(lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[int]								= float(lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[Any]								= value
  lowerCAmelCase_   :				str								= float(rule_list[-1]						)

  def create_rules_function(lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				int						):
    def rule_func(lowerCAmelCase__      :				int						) -> float:
      lowerCAmelCase_   :				str								= sorted(rules_dict.keys()						)
      for i, sorted_step in enumerate(lowerCAmelCase__						):
        if steps < sorted_step:
          return rules_dict[sorted_steps[i]]
      return last_lr_multiple

    return rule_func

  lowerCAmelCase_   :				Dict								= create_rules_function(lowerCAmelCase__    ,   lowerCAmelCase__						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Union[str, Any]=-1						)						->   Any:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				int						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    return max(
        0.0    ,   float(num_training_steps - current_step						) / float(max(1    ,   num_training_steps - num_warmup_steps						)						)						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				float = 0.5    ,   lowerCAmelCase__      :				int = -1						)						->   Union[str, Any]:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				Any						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    lowerCAmelCase_   :				List[Any]								= float(current_step - num_warmup_steps						) / float(max(1    ,   num_training_steps - num_warmup_steps						)						)
    return max(0.0    ,   0.5 * (1.0 + math.cos(math.pi * float(lowerCAmelCase__						) * 2.0 * progress						))						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int = 1    ,   lowerCAmelCase__      :				int = -1						)						->   int:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				str						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    lowerCAmelCase_   :				Dict								= float(current_step - num_warmup_steps						) / float(max(1    ,   num_training_steps - num_warmup_steps						)						)
    if progress >= 1.0:
      return 0.0
    return max(0.0    ,   0.5 * (1.0 + math.cos(math.pi * ((float(lowerCAmelCase__						) * progress) % 1.0)						))						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Tuple=1e-7    ,   lowerCAmelCase__      :				Union[str, Any]=1.0    ,   lowerCAmelCase__      :				int=-1						)						->   Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= optimizer.defaults['lr']
  if not (lr_init > lr_end):
    raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"						)

  def lr_lambda(lowerCAmelCase__      :				int						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    elif current_step > num_training_steps:
      return lr_end / lr_init  # as LambdaLR multiplies by lr_init
    else:
      lowerCAmelCase_   :				Any								= lr_init - lr_end
      lowerCAmelCase_   :				int								= num_training_steps - num_warmup_steps
      lowerCAmelCase_   :				Dict								= 1 - (current_step - num_warmup_steps) / decay_steps
      lowerCAmelCase_   :				Dict								= lr_range * pct_remaining**power + lr_end
      return decay / lr_init  # as LambdaLR multiplies by lr_init

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)


lowercase__		:				Any          =	{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, SchedulerType]    ,   lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				Optional[str] = None    ,   lowerCAmelCase__      :				Optional[int] = None    ,   lowerCAmelCase__      :				Optional[int] = None    ,   lowerCAmelCase__      :				int = 1    ,   lowerCAmelCase__      :				float = 1.0    ,   lowerCAmelCase__      :				int = -1    ,   )						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				List[str]								= SchedulerType(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[Any]								= TYPE_TO_SCHEDULER_FUNCTION[name]
  if name == SchedulerType.CONSTANT:
    return schedule_func(lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

  if name == SchedulerType.PIECEWISE_CONSTANT:
    return schedule_func(lowerCAmelCase__    ,   step_rules=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

  # All other schedulers require `num_warmup_steps`
  if num_warmup_steps is None:
    raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument."						)

  if name == SchedulerType.CONSTANT_WITH_WARMUP:
    return schedule_func(lowerCAmelCase__    ,   num_warmup_steps=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

  # All other schedulers require `num_training_steps`
  if num_training_steps is None:
    raise ValueError(f"{name} requires `num_training_steps`, please provide that argument."						)

  if name == SchedulerType.COSINE_WITH_RESTARTS:
    return schedule_func(
        lowerCAmelCase__    ,   num_warmup_steps=lowerCAmelCase__    ,   num_training_steps=lowerCAmelCase__    ,   num_cycles=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__    ,   )

  if name == SchedulerType.POLYNOMIAL:
    return schedule_func(
        lowerCAmelCase__    ,   num_warmup_steps=lowerCAmelCase__    ,   num_training_steps=lowerCAmelCase__    ,   power=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__    ,   )

  return schedule_func(
      lowerCAmelCase__    ,   num_warmup_steps=lowerCAmelCase__    ,   num_training_steps=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)





