

'''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


__lowerCamelCase										=			logging.get_logger(__name__)


class     A__					(					SCREAMING_SNAKE_CASE__	):
    lowercase		    =						"linear"
    lowercase		    =						"cosine"
    lowercase		    =						"cosine_with_restarts"
    lowercase		    =						"polynomial"
    lowercase		    =						"constant"
    lowercase		    =						"constant_with_warmup"
    lowercase		    =						"piecewise_constant"

def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__ = -1 )	->	str:
       return LambdaLR(__UpperCamelCase,				lambda UpperCAmelCase__ : 1,				last_epoch=__UpperCamelCase )
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ = -1 )	->	str:

       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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ = -1 )	->	int:
       A_		    =					{}
       A_		    =					step_rules.split(""",""" )
       for rule_str in rule_list[:-1]:
              A_    ,	A_		    =					rule_str.split(""":""" )
              A_		    =					int(__UpperCamelCase )
              A_		    =					float(__UpperCamelCase )
              A_		    =					value
       A_		    =					float(rule_list[-1] )

       def create_rules_function(UpperCAmelCase__,				UpperCAmelCase__ ):
              def rule_func(UpperCAmelCase__ ) -> float:
                     A_		    =					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

       A_		    =					create_rules_function(__UpperCamelCase,				__UpperCamelCase )

       return LambdaLR(__UpperCamelCase,				__UpperCamelCase,				last_epoch=__UpperCamelCase )
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__=-1 )	->	Dict:

       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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ = 0.5,				UpperCAmelCase__ = -1 )	->	Tuple:

       def lr_lambda(UpperCAmelCase__ ):
              if current_step < num_warmup_steps:
                     return float(__UpperCamelCase ) / float(max(1,				__UpperCamelCase ) )
              A_		    =					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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ = 1,				UpperCAmelCase__ = -1 )	->	Optional[int]:

       def lr_lambda(UpperCAmelCase__ ):
              if current_step < num_warmup_steps:
                     return float(__UpperCamelCase ) / float(max(1,				__UpperCamelCase ) )
              A_		    =					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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__=1e-7,				UpperCAmelCase__=1.0,				UpperCAmelCase__=-1 )	->	Union[str, Any]:
       A_		    =					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:
                     A_		    =					lr_init - lr_end
                     A_		    =					num_training_steps - num_warmup_steps
                     A_		    =					1 - (current_step - num_warmup_steps) / decay_steps
                     A_		    =					lr_range * pct_remaining**power + lr_end
                     return decay / lr_init  # as LambdaLR multiplies by lr_init

       return LambdaLR(__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase )


__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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ = None,				UpperCAmelCase__ = None,				UpperCAmelCase__ = None,				UpperCAmelCase__ = 1,				UpperCAmelCase__ = 1.0,				UpperCAmelCase__ = -1,				)	->	List[str]:
       A_		    =					SchedulerType(__UpperCamelCase )
       A_		    =					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 )

from ...processing_utils import ProcessorMixin


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''image_processor''', '''feature_extractor''']
  lowerCamelCase__   =  '''TvltImageProcessor'''
  lowerCamelCase__   =  '''TvltFeatureExtractor'''







  def __init__(							self       :				List[str]     ,   __magic_name__       :				Any     ,   __magic_name__       :				Any      )     ->		int:
        super().__init__(image_processor=__magic_name__     ,   feature_extractor=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     image_processor
        SCREAMING_SNAKE_CASE_      =     feature_extractor







  def __call__(							self       :				List[str]     ,   __magic_name__       :				Optional[Any]=None     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				int=None     ,   __magic_name__       :				str=None     ,   __magic_name__       :				Any=False     ,   __magic_name__       :				int=False     ,   *__magic_name__       :				int     ,   **__magic_name__       :				Any     ,   )     ->		List[Any]:

        if images is None and audio is None:
              raise ValueError("You need to specify either an `images` or `audio` input to process."      )

        SCREAMING_SNAKE_CASE_      =     None
        if images is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   mask_pixel=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if images_mixed is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   is_mixed=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if audio is not None:
              SCREAMING_SNAKE_CASE_      =     self.feature_extractor(
                  __magic_name__     ,   *__magic_name__     ,   sampling_rate=__magic_name__     ,   mask_audio=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     {}
        if audio is not None:
              output_dict.update(__magic_name__      )
        if images is not None:
              output_dict.update(__magic_name__      )
        if images_mixed_dict is not None:
              output_dict.update(__magic_name__      )
        return output_dict







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.image_processor.model_input_names
        SCREAMING_SNAKE_CASE_      =     self.feature_extractor.model_input_names
        return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names      )      )


