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 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[Any]	      = '''linear'''
   lowerCAmelCase   :     Union[str, Any]	      = '''cosine'''
   lowerCAmelCase   :     Optional[int]	      = '''cosine_with_restarts'''
   lowerCAmelCase   :     str	      = '''polynomial'''
   lowerCAmelCase   :     Optional[int]	      = '''constant'''
   lowerCAmelCase   :     Optional[Any]	      = '''constant_with_warmup'''
   lowerCAmelCase   :     Any	      = '''piecewise_constant'''




def    lowerCAmelCase_  (		lowercase:				Optimizer	,       lowercase:				int = -1		)     ->					int:







 '''simple docstring'''
 return LambdaLR(lowercase	,       lambda lowercase		: 1	,       last_epoch=lowercase		)




def    lowerCAmelCase_  (		lowercase:				Optimizer	,       lowercase:				int	,       lowercase:				int = -1		)     ->					Union[str, Any]:







 '''simple docstring'''

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

 return LambdaLR(lowercase	,       lowercase	,       last_epoch=lowercase		)




def    lowerCAmelCase_  (		lowercase:				Optimizer	,       lowercase:				str	,       lowercase:				int = -1		)     ->					Union[str, Any]:







 '''simple docstring'''
 _UpperCamelCase:  str    						=			{}
 _UpperCamelCase:  Union[str, Any]    						=			step_rules.split(''','''		)
 for rule_str in rule_list[:-1]:
  _UpperCamelCase						,				_UpperCamelCase:  Dict    						=			rule_str.split(''':'''		)
  _UpperCamelCase:  Any    						=			int(lowercase		)
  _UpperCamelCase:  Optional[Any]    						=			float(lowercase		)
  _UpperCamelCase:  Union[str, Any]    						=			value
 _UpperCamelCase:  str    						=			float(rule_list[-1]		)

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

  return rule_func

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

 return LambdaLR(lowercase	,       lowercase	,       last_epoch=lowercase		)




def    lowerCAmelCase_  (		lowercase:				List[Any]	,       lowercase:				List[Any]	,       lowercase:				List[str]	,       lowercase:				Union[str, Any]=-1		)     ->					int:







 '''simple docstring'''

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

 return LambdaLR(lowercase	,       lowercase	,       lowercase		)




def    lowerCAmelCase_  (		lowercase:				Optimizer	,       lowercase:				int	,       lowercase:				int	,       lowercase:				float = 0.5	,       lowercase:				int = -1		)     ->					Tuple:







 '''simple docstring'''

 def lr_lambda(lowercase:				int		):
  if current_step < num_warmup_steps:
   return float(lowercase		) / float(max(1	,       lowercase		)		)
  _UpperCamelCase:  Optional[int]    						=			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(lowercase		) * 2.0 * progress		))		)

 return LambdaLR(lowercase	,       lowercase	,       lowercase		)




def    lowerCAmelCase_  (		lowercase:				Optimizer	,       lowercase:				int	,       lowercase:				int	,       lowercase:				int = 1	,       lowercase:				int = -1		)     ->					str:







 '''simple docstring'''

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

 return LambdaLR(lowercase	,       lowercase	,       lowercase		)




def    lowerCAmelCase_  (		lowercase:				List[Any]	,       lowercase:				Optional[Any]	,       lowercase:				Any	,       lowercase:				Tuple=1E-7	,       lowercase:				str=1.0	,       lowercase:				Dict=-1		)     ->					str:







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

 return LambdaLR(lowercase	,       lowercase	,       lowercase		)


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    lowerCAmelCase_  (		lowercase:				Union[str, SchedulerType]	,       lowercase:				Optimizer	,       lowercase:				Optional[str] = None	,       lowercase:				Optional[int] = None	,       lowercase:				Optional[int] = None	,       lowercase:				int = 1	,       lowercase:				float = 1.0	,       lowercase:				int = -1	,       )     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			SchedulerType(lowercase		)
 _UpperCamelCase:  Optional[Any]    						=			TYPE_TO_SCHEDULER_FUNCTION[name]
 if name == SchedulerType.CONSTANT:
  return schedule_func(lowercase	,       last_epoch=lowercase		)

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

 # 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(lowercase	,       num_warmup_steps=lowercase	,       last_epoch=lowercase		)

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

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

 return schedule_func(
     lowercase	,       num_warmup_steps=lowercase	,       num_training_steps=lowercase	,       last_epoch=lowercase		)
import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def __init__(						self       :	Any		,	_lowercase       :	Optional[int]		,	_lowercase       :	Tuple=13		,	_lowercase       :	str=7		,	_lowercase       :	List[Any]=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	str=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	Dict=99		,	_lowercase       :	List[Any]=32		,	_lowercase       :	List[str]=5		,	_lowercase       :	str=4		,	_lowercase       :	int=37		,	_lowercase       :	List[str]="gelu"		,	_lowercase       :	Any=0.1		,	_lowercase       :	Optional[int]=0.1		,	_lowercase       :	Dict=512		,	_lowercase       :	int=16		,	_lowercase       :	Optional[Any]=2		,	_lowercase       :	Dict=0.02		,	_lowercase       :	List[Any]=4		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			parent
    _UpperCamelCase:  int    						=			batch_size
    _UpperCamelCase:  List[str]    						=			seq_length
    _UpperCamelCase:  Optional[int]    						=			is_training
    _UpperCamelCase:  Optional[Any]    						=			use_attention_mask
    _UpperCamelCase:  Any    						=			use_token_type_ids
    _UpperCamelCase:  List[str]    						=			use_labels
    _UpperCamelCase:  Optional[int]    						=			vocab_size
    _UpperCamelCase:  List[str]    						=			hidden_size
    _UpperCamelCase:  Union[str, Any]    						=			num_hidden_layers
    _UpperCamelCase:  Any    						=			num_attention_heads
    _UpperCamelCase:  List[str]    						=			intermediate_size
    _UpperCamelCase:  Union[str, Any]    						=			hidden_act
    _UpperCamelCase:  Dict    						=			hidden_dropout_prob
    _UpperCamelCase:  List[str]    						=			attention_probs_dropout_prob
    _UpperCamelCase:  str    						=			max_position_embeddings
    _UpperCamelCase:  Dict    						=			type_vocab_size
    _UpperCamelCase:  Tuple    						=			type_sequence_label_size
    _UpperCamelCase:  List[Any]    						=			initializer_range
    _UpperCamelCase:  str    						=			num_choices



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



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

    _UpperCamelCase:  Any    						=			None
    if self.use_attention_mask:
     _UpperCamelCase:  List[str]    						=			random_attention_mask([self.batch_size, self.seq_length]				)

    _UpperCamelCase:  Optional[Any]    						=			None
    if self.use_token_type_ids:
     _UpperCamelCase:  Optional[Any]    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size				)

    _UpperCamelCase:  Tuple    						=			AlbertConfig(
        vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	is_decoder=_lowercase		,	initializer_range=self.initializer_range		,	)

    return config, input_ids, token_type_ids, attention_mask



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  int    						=			config_and_inputs
    _UpperCamelCase:  int    						=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict




@require_flax
class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Any	      = (
       (
           FlaxAlbertModel,
           FlaxAlbertForPreTraining,
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )



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



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			FlaxAlbertModelTester(self				)



   @slow
   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    for model_class_name in self.all_model_classes:
     _UpperCamelCase:  int    						=			model_class_name.from_pretrained('''albert-base-v2'''				)
     _UpperCamelCase:  List[Any]    						=			model(np.ones((1, 1)				)				)
     self.assertIsNotNone(_lowercase				)




@require_flax
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   @slow
   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			FlaxAlbertModel.from_pretrained('''albert-base-v2'''				)
    _UpperCamelCase:  List[Any]    						=			np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]				)
    _UpperCamelCase:  Optional[int]    						=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
    _UpperCamelCase:  Optional[int]    						=			model(_lowercase		,	attention_mask=_lowercase				)[0]
    _UpperCamelCase:  Tuple    						=			(1, 11, 768)
    self.assertEqual(output.shape		,	_lowercase				)
    _UpperCamelCase:  Tuple    						=			np.array(
        [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]				)

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