


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


lowerCamelCase											=       logging.get_logger(__name__)




class A  (   __A     ):
      UpperCamelCase__							:  Tuple      ="linear"
      UpperCamelCase__							:  Union[str, Any]      ="cosine"
      UpperCamelCase__							:  Any      ="cosine_with_restarts"
      UpperCamelCase__							:  int      ="polynomial"
      UpperCamelCase__							:  Union[str, Any]      ="constant"
      UpperCamelCase__							:  Tuple      ="constant_with_warmup"
      UpperCamelCase__							:  str      ="piecewise_constant"




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      int = -1     ):
       '''simple docstring'''





       return LambdaLR(_lowerCamelCase		,			lambda SCREAMING_SNAKE_CASE__     : 1		,			last_epoch=_lowerCamelCase     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      int = -1     ):
       '''simple docstring'''





       def lr_lambda(SCREAMING_SNAKE_CASE__					:      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 	a_      (    SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      int = -1     ):
       '''simple docstring'''





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

       def create_rules_function(SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      Tuple     ):
              def rule_func(SCREAMING_SNAKE_CASE__					:      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       :		Optional[int]		=create_rules_function(_lowerCamelCase		,			_lowerCamelCase     )

       return LambdaLR(_lowerCamelCase		,			_lowerCamelCase		,			last_epoch=_lowerCamelCase     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      Dict		,			SCREAMING_SNAKE_CASE__					:      Any=-1     ):
       '''simple docstring'''





       def lr_lambda(SCREAMING_SNAKE_CASE__					:      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 	a_      (    SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      float = 0.5		,			SCREAMING_SNAKE_CASE__					:      int = -1     ):
       '''simple docstring'''





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

       return LambdaLR(_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      int = 1		,			SCREAMING_SNAKE_CASE__					:      int = -1     ):
       '''simple docstring'''





       def lr_lambda(SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
              if current_step < num_warmup_steps:
                     return float(_lowerCamelCase     ) / float(max(1		,			_lowerCamelCase     )     )
              _lowerCamelCase       :		Tuple		=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 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Union[str, Any]=1e-7		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]=1.0		,			SCREAMING_SNAKE_CASE__					:      int=-1     ):
       '''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(SCREAMING_SNAKE_CASE__					:      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       :		List[Any]		=num_training_steps - num_warmup_steps
                     _lowerCamelCase       :		Any		=1 - (current_step - num_warmup_steps) / decay_steps
                     _lowerCamelCase       :		List[str]		=lr_range * pct_remaining**power + lr_end
                     return decay / lr_init  # as LambdaLR multiplies by lr_init

       return LambdaLR(_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase     )


lowerCamelCase											=       {
    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 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, SchedulerType]		,			SCREAMING_SNAKE_CASE__					:      Optimizer		,			SCREAMING_SNAKE_CASE__					:      Optional[str] = None		,			SCREAMING_SNAKE_CASE__					:      Optional[int] = None		,			SCREAMING_SNAKE_CASE__					:      Optional[int] = None		,			SCREAMING_SNAKE_CASE__					:      int = 1		,			SCREAMING_SNAKE_CASE__					:      float = 1.0		,			SCREAMING_SNAKE_CASE__					:      int = -1		,			):
       '''simple docstring'''





       _lowerCamelCase       :		List[str]		=SchedulerType(_lowerCamelCase     )
       _lowerCamelCase       :		int		=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     )



from typing import TYPE_CHECKING

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


UpperCamelCase  							=						{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						['''ElectraTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ElectraForCausalLM''',
          '''ElectraForMaskedLM''',
          '''ElectraForMultipleChoice''',
          '''ElectraForPreTraining''',
          '''ElectraForQuestionAnswering''',
          '''ElectraForSequenceClassification''',
          '''ElectraForTokenClassification''',
          '''ElectraModel''',
          '''ElectraPreTrainedModel''',
          '''load_tf_weights_in_electra''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFElectraForMaskedLM''',
          '''TFElectraForMultipleChoice''',
          '''TFElectraForPreTraining''',
          '''TFElectraForQuestionAnswering''',
          '''TFElectraForSequenceClassification''',
          '''TFElectraForTokenClassification''',
          '''TFElectraModel''',
          '''TFElectraPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''FlaxElectraForCausalLM''',
          '''FlaxElectraForMaskedLM''',
          '''FlaxElectraForMultipleChoice''',
          '''FlaxElectraForPreTraining''',
          '''FlaxElectraForQuestionAnswering''',
          '''FlaxElectraForSequenceClassification''',
          '''FlaxElectraForTokenClassification''',
          '''FlaxElectraModel''',
          '''FlaxElectraPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
      from .tokenization_electra import ElectraTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_electra_fast import ElectraTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_electra import (
                ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                ElectraForCausalLM,
                ElectraForMaskedLM,
                ElectraForMultipleChoice,
                ElectraForPreTraining,
                ElectraForQuestionAnswering,
                ElectraForSequenceClassification,
                ElectraForTokenClassification,
                ElectraModel,
                ElectraPreTrainedModel,
                load_tf_weights_in_electra,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_electra import (
                TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFElectraForMaskedLM,
                TFElectraForMultipleChoice,
                TFElectraForPreTraining,
                TFElectraForQuestionAnswering,
                TFElectraForSequenceClassification,
                TFElectraForTokenClassification,
                TFElectraModel,
                TFElectraPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_electra import (
                FlaxElectraForCausalLM,
                FlaxElectraForMaskedLM,
                FlaxElectraForMultipleChoice,
                FlaxElectraForPreTraining,
                FlaxElectraForQuestionAnswering,
                FlaxElectraForSequenceClassification,
                FlaxElectraForTokenClassification,
                FlaxElectraModel,
                FlaxElectraPreTrainedModel,
            )

else:
      import sys

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



