




'''simple docstring'''
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
 import torch


class        _UpperCAmelCase						:




    def __init__(			self  :       str  ,     a  :       Union[str, Any]  ,     a  :       Optional[Any]=1_4  ,     a  :       Any=7  ,     a  :       Union[str, Any]=True  ,     a  :       Optional[int]=True  ,     a  :       Optional[Any]=False  ,     a  :       int=True  ,     a  :       Any=9_9  ,     a  :       Dict=3_2  ,     a  :       str=4  ,     a  :       Any=4  ,     a  :       Optional[Any]=4  ,     a  :       List[Any]=3_7  ,     a  :       Optional[int]="gelu"  ,     a  :       Dict=0.1  ,     a  :       Optional[int]=0.1  ,     a  :       Any=5_1_2  ,     a  :       int=0.02  ,     ):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     parent
       lowercase_	:    str           =     batch_size
       lowercase_	:    Optional[Any]           =     seq_length
       lowercase_	:    int           =     is_training
       lowercase_	:    List[Any]           =     use_input_mask
       lowercase_	:    Union[str, Any]           =     use_token_type_ids
       lowercase_	:    Union[str, Any]           =     use_labels
       lowercase_	:    Any           =     vocab_size
       lowercase_	:    Dict           =     hidden_size
       lowercase_	:    str           =     rotary_dim
       lowercase_	:    Any           =     num_hidden_layers
       lowercase_	:    Optional[Any]           =     num_attention_heads
       lowercase_	:    List[str]           =     intermediate_size
       lowercase_	:    Optional[int]           =     hidden_act
       lowercase_	:    List[Any]           =     hidden_dropout_prob
       lowercase_	:    List[Any]           =     attention_probs_dropout_prob
       lowercase_	:    Any           =     max_position_embeddings
       lowercase_	:    List[str]           =     initializer_range
       lowercase_	:    List[Any]           =     None
       lowercase_	:    Tuple           =     vocab_size - 1
       lowercase_	:    int           =     vocab_size - 1
       lowercase_	:    Dict           =     vocab_size - 1




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    str           =     ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size			)

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

       lowercase_	:    Optional[Any]           =     GPTJConfig(
           vocab_size=self.vocab_size  ,     n_embd=self.hidden_size  ,     n_layer=self.num_hidden_layers  ,     n_head=self.num_attention_heads  ,     n_positions=self.max_position_embeddings  ,     use_cache=A_  ,     bos_token_id=self.bos_token_id  ,     eos_token_id=self.eos_token_id  ,     pad_token_id=self.pad_token_id  ,     rotary_dim=self.rotary_dim  ,     )

       return (config, input_ids, input_mask)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    int           =     self.prepare_config_and_inputs()
       lowercase_    ,		lowercase_    ,		lowercase_	:    Tuple           =     config_and_inputs
       lowercase_	:    Tuple           =     {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict




    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       Any  ,     a  :       List[str]  ,     a  :       List[str]  ,     a  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     2_0
       lowercase_	:    Optional[Any]           =     model_class_name(A_			)

       lowercase_	:    Dict           =     model.init_cache(input_ids.shape[0]  ,     A_			)
       lowercase_	:    Any           =     jnp.ones((input_ids.shape[0], max_decoder_length)  ,     dtype="i4"			)

       lowercase_	:    str           =     jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1			)[None, :]  ,     (input_ids.shape[0], input_ids.shape[-1] - 1)			)
       lowercase_	:    int           =     model(
           input_ids[:, :-1]  ,     attention_mask=A_  ,     past_key_values=A_  ,     position_ids=A_  ,     )

       lowercase_	:    Any           =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,     dtype="i4"			)
       lowercase_	:    Optional[Any]           =     model(
           input_ids[:, -1:]  ,     attention_mask=A_  ,     past_key_values=outputs_cache.past_key_values  ,     position_ids=A_  ,     )

       lowercase_	:    Optional[Any]           =     model(A_			)

       lowercase_	:    Optional[int]           =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
       self.parent.assertTrue(diff < 1e-3  ,     msg=f"""Max diff is {diff}"""			)





    def 				lowerCAmelCase__				(			self  :       str  ,     a  :       Optional[int]  ,     a  :       Optional[int]  ,     a  :       Tuple  ,     a  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    str           =     2_0
       lowercase_	:    List[str]           =     model_class_name(A_			)

       lowercase_	:    int           =     jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])			)]  ,     axis=-1  ,     )

       lowercase_	:    List[str]           =     model.init_cache(input_ids.shape[0]  ,     A_			)
       lowercase_	:    List[Any]           =     jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1			)[None, :]  ,     (input_ids.shape[0], input_ids.shape[-1] - 1)			)

       lowercase_	:    List[Any]           =     model(
           input_ids[:, :-1]  ,     attention_mask=A_  ,     past_key_values=A_  ,     position_ids=A_  ,     )
       lowercase_	:    int           =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,     dtype="i4"			)
       lowercase_	:    List[str]           =     model(
           input_ids[:, -1:]  ,     past_key_values=outputs_cache.past_key_values  ,     attention_mask=A_  ,     position_ids=A_  ,     )

       lowercase_	:    List[str]           =     model(A_  ,     attention_mask=A_			)

       lowercase_	:    int           =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
       self.parent.assertTrue(diff < 1e-3  ,     msg=f"""Max diff is {diff}"""			)


@require_flax
class        _UpperCAmelCase						(		_lowercase				,					_lowercase				,					unittest.TestCase			):
    __lowerCamelCase:   List[Any]		       =    (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    __lowerCamelCase:   Optional[Any]		       =    (FlaxGPTJForCausalLM,) if is_flax_available() else ()




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     FlaxGPTJModelTester(self			)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       for model_class_name in self.all_model_classes:
          lowercase_    ,		lowercase_    ,		lowercase_	:    List[str]           =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(A_  ,     A_  ,     A_  ,     A_			)




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       for model_class_name in self.all_model_classes:
          lowercase_    ,		lowercase_    ,		lowercase_	:    Any           =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              A_  ,     A_  ,     A_  ,     A_			)




    @tooslow
    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    int           =     GPTaTokenizer.from_pretrained("gpt2"  ,     pad_token="<|endoftext|>"  ,     padding_side="left"			)
       lowercase_	:    str           =     tokenizer(["Hello this is a long string", "Hey"]  ,     return_tensors="np"  ,     padding=A_  ,     truncation=A_			)

       lowercase_	:    str           =     FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"			)
       lowercase_	:    str           =     False
       lowercase_	:    Any           =     model.config.eos_token_id

       lowercase_	:    Dict           =     jax.jit(model.generate			)

       lowercase_	:    str           =     jit_generate(
           inputs["input_ids"]  ,     attention_mask=inputs["attention_mask"]  ,     pad_token_id=tokenizer.pad_token_id			).sequences

       lowercase_	:    str           =     tokenizer.batch_decode(A_  ,     skip_special_tokens=A_			)

       lowercase_	:    List[str]           =     [
           "Hello this is a long string of text.\n\nI\'m trying to get the text of the",
           "Hey, I\'m a little late to the party. I\'m going to",
       ]

       self.assertListEqual(A_  ,     A_			)




    @is_pt_flax_cross_test
    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Dict           =     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__			):
             # prepare inputs
             lowercase_	:    Dict           =     self._prepare_for_class(A_  ,     A_			)
             lowercase_	:    Dict           =     {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowercase_	:    List[Any]           =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowercase_	:    List[Any]           =     getattr(A_  ,     A_			)

             lowercase_    ,		lowercase_	:    Any           =     pt_inputs["input_ids"].shape
             lowercase_	:    List[Any]           =     np.random.randint(0  ,     seq_length - 1  ,     size=(batch_size,)			)
             for batch_idx, start_index in enumerate(A_			):
                lowercase_	:    Union[str, Any]           =     0
                lowercase_	:    Dict           =     1
                lowercase_	:    Dict           =     0
                lowercase_	:    Optional[Any]           =     1
             lowercase_	:    Optional[int]           =     pt_model_class(A_			).eval()
             lowercase_	:    List[Any]           =     model_class(A_  ,     dtype=jnp.floataa			)

             lowercase_	:    Any           =     convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,     A_			)
             lowercase_	:    Union[str, Any]           =     fx_state

             with torch.no_grad():
                lowercase_	:    int           =     pt_model(**A_			).to_tuple()

             lowercase_	:    Union[str, Any]           =     fx_model(**A_			).to_tuple()
             self.assertEqual(len(A_			)  ,     len(A_			)  ,     "Output lengths differ between Flax and PyTorch"			)
             for fx_output, pt_output in zip(A_  ,     A_			):
                self.assert_almost_equals(fx_output[:, -1]  ,     pt_output[:, -1].numpy()  ,     4e-2			)

             with tempfile.TemporaryDirectory() as tmpdirname:
                pt_model.save_pretrained(A_			)
                lowercase_	:    Optional[Any]           =     model_class.from_pretrained(A_  ,     from_pt=A_			)

             lowercase_	:    List[Any]           =     fx_model_loaded(**A_			).to_tuple()
             self.assertEqual(
                 len(A_			)  ,     len(A_			)  ,     "Output lengths differ between Flax and PyTorch"			)
             for fx_output_loaded, pt_output in zip(A_  ,     A_			):
                self.assert_almost_equals(fx_output_loaded[:, -1]  ,     pt_output[:, -1].numpy()  ,     4e-2			)




    @is_pt_flax_cross_test
    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Tuple           =     self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__			):
             # prepare inputs
             lowercase_	:    Union[str, Any]           =     self._prepare_for_class(A_  ,     A_			)
             lowercase_	:    Tuple           =     {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowercase_	:    Dict           =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowercase_	:    Tuple           =     getattr(A_  ,     A_			)

             lowercase_	:    Optional[int]           =     pt_model_class(A_			).eval()
             lowercase_	:    str           =     model_class(A_  ,     dtype=jnp.floataa			)

             lowercase_	:    Union[str, Any]           =     load_flax_weights_in_pytorch_model(A_  ,     fx_model.params			)
             lowercase_    ,		lowercase_	:    Tuple           =     pt_inputs["input_ids"].shape
             lowercase_	:    Tuple           =     np.random.randint(0  ,     seq_length - 1  ,     size=(batch_size,)			)
             for batch_idx, start_index in enumerate(A_			):
                lowercase_	:    Any           =     0
                lowercase_	:    Optional[Any]           =     1
                lowercase_	:    Union[str, Any]           =     0
                lowercase_	:    List[Any]           =     1

             # make sure weights are tied in PyTorch
             pt_model.tie_weights()

             with torch.no_grad():
                lowercase_	:    Dict           =     pt_model(**A_			).to_tuple()

             lowercase_	:    Union[str, Any]           =     fx_model(**A_			).to_tuple()
             self.assertEqual(len(A_			)  ,     len(A_			)  ,     "Output lengths differ between Flax and PyTorch"			)
             for fx_output, pt_output in zip(A_  ,     A_			):
                self.assert_almost_equals(fx_output[:, -1]  ,     pt_output[:, -1].numpy()  ,     4e-2			)

             with tempfile.TemporaryDirectory() as tmpdirname:
                fx_model.save_pretrained(A_			)
                lowercase_	:    Any           =     pt_model_class.from_pretrained(A_  ,     from_flax=A_			)

             with torch.no_grad():
                lowercase_	:    int           =     pt_model_loaded(**A_			).to_tuple()

             self.assertEqual(
                 len(A_			)  ,     len(A_			)  ,     "Output lengths differ between Flax and PyTorch"			)
             for fx_output, pt_output in zip(A_  ,     A_			):
                self.assert_almost_equals(fx_output[:, -1]  ,     pt_output[:, -1].numpy()  ,     4e-2			)





    @tooslow
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       for model_class_name in self.all_model_classes:
          lowercase_	:    Any           =     model_class_name.from_pretrained("EleutherAI/gpt-j-6B"			)
          lowercase_	:    Optional[int]           =     model(np.ones((1, 1)			)			)
          self.assertIsNotNone(A_			)












'''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


UpperCamelCase__ =			logging.get_logger(__name__)


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   Tuple		       =    'linear'
    __lowerCamelCase:   Any		       =    'cosine'
    __lowerCamelCase:   Optional[Any]		       =    'cosine_with_restarts'
    __lowerCamelCase:   Tuple		       =    'polynomial'
    __lowerCamelCase:   int		       =    'constant'
    __lowerCamelCase:   Optional[Any]		       =    'constant_with_warmup'
    __lowerCamelCase:   List[str]		       =    'piecewise_constant'




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




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

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     {}
   lowercase_	:    Dict           =     step_rules.split(","							)
   for rule_str in rule_list[:-1]:
      lowercase_    ,		lowercase_	:    Any           =     rule_str.split(":"							)
      lowercase_	:    List[Any]           =     int(_UpperCamelCase							)
      lowercase_	:    int           =     float(_UpperCamelCase							)
      lowercase_	:    Optional[int]           =     value
   lowercase_	:    Union[str, Any]           =     float(rule_list[-1]							)

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

      return rule_func

   lowercase_	:    Optional[int]           =     create_rules_function(_UpperCamelCase						,		_UpperCamelCase							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=-1							):


   """simple docstring"""




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

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 0.5						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    List[str]           =     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(_UpperCamelCase							) * 2.0 * progress							))							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 1						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    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(_UpperCamelCase							) * progress) % 1.0)							))							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=1e-7						,		_UpperCamelCase=1.0						,		_UpperCamelCase=-1							):


   """simple docstring"""




   lowercase_	:    Dict           =     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(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         lowercase_	:    int           =     lr_init - lr_end
         lowercase_	:    Optional[int]           =     num_training_steps - num_warmup_steps
         lowercase_	:    Optional[Any]           =     1 - (current_step - num_warmup_steps) / decay_steps
         lowercase_	:    List[Any]           =     lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)


UpperCamelCase__ =			{
    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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = 1						,		_UpperCamelCase = 1.0						,		_UpperCamelCase = -1						,		):


   """simple docstring"""




   lowercase_	:    Any           =     SchedulerType(_UpperCamelCase							)
   lowercase_	:    List[Any]           =     TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

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

   # 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(_UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

   # 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(
          _UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		num_training_steps=_UpperCamelCase						,		num_cycles=_UpperCamelCase						,		last_epoch=_UpperCamelCase						,		)

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

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




