





'''simple docstring'''

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


a        =						logging.get_logger(__name__)







class __a (   _snake_case			):
   __UpperCamelCase   : Any	      =	['input_values', 'padding_mask']





   def __init__(					self				:			str				,lowerCamelCase				:			int = 1				,lowerCamelCase				:			int = 2_4000				,lowerCamelCase				:			float = 0.0				,lowerCamelCase				:			float = None				,lowerCamelCase				:			float = None				,**lowerCamelCase				:			Union[str, Any]				,):

      '''simple docstring'''

      super().__init__(feature_size=lowerCamelCase				,sampling_rate=lowerCamelCase				,padding_value=lowerCamelCase				,**lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       chunk_length_s
      __SCREAMING_SNAKE_CASE      							=       overlap





   @property
   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      if self.chunk_length_s is None:
         return None
      else:
         return int(self.chunk_length_s * self.sampling_rate    )





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      if self.chunk_length_s is None or self.overlap is None:
         return None
      else:
         return max(1				,int((1.0 - self.overlap) * self.chunk_length    )    )





   def __call__(					self				:			Tuple				,lowerCamelCase				:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,lowerCamelCase				:			Optional[Union[bool, str, PaddingStrategy]] = None				,lowerCamelCase				:			Optional[bool] = False				,lowerCamelCase				:			Optional[int] = None				,lowerCamelCase				:			Optional[Union[str, TensorType]] = None				,lowerCamelCase				:			Optional[int] = None				,):

      '''simple docstring'''

      if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
            raise ValueError(
                f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
                f""" {self.sampling_rate} and not {sampling_rate}."""    )
      else:
         logger.warning(
             """It is strongly recommended to pass the `sampling_rate` argument to this function. """
             """Failing to do so can result in silent errors that might be hard to debug."""    )

      if padding and truncation:
         raise ValueError("""Both padding and truncation were set. Make sure you only set one."""    )
      elif padding is None:
         # by default let's pad the inputs
         __SCREAMING_SNAKE_CASE      							=       True

      __SCREAMING_SNAKE_CASE      							=       bool(
          isinstance(lowerCamelCase				,(list, tuple)    ) and (isinstance(raw_audio[0]				,(np.ndarray, tuple, list)    ))    )

      if is_batched:
         __SCREAMING_SNAKE_CASE      							=       [np.asarray(lowerCamelCase				,dtype=np.floataa    ).T for audio in raw_audio]
      elif not is_batched and not isinstance(lowerCamelCase				,np.ndarray    ):
         __SCREAMING_SNAKE_CASE      							=       np.asarray(lowerCamelCase				,dtype=np.floataa    )
      elif isinstance(lowerCamelCase				,np.ndarray    ) and raw_audio.dtype is np.dtype(np.floataa    ):
         __SCREAMING_SNAKE_CASE      							=       raw_audio.astype(np.floataa    )

      # always return batch
      if not is_batched:
         __SCREAMING_SNAKE_CASE      							=       [np.asarray(lowerCamelCase    ).T]

      # verify inputs are valid
      for idx, example in enumerate(lowerCamelCase    ):
         if example.ndim > 2:
            raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}"""    )
         if self.feature_size == 1 and example.ndim != 1:
            raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels"""    )
         if self.feature_size == 2 and example.shape[-1] != 2:
            raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels"""    )

      __SCREAMING_SNAKE_CASE      							=       None
      __SCREAMING_SNAKE_CASE      							=       BatchFeature({"""input_values""": raw_audio}    )
      if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
         if truncation:
            __SCREAMING_SNAKE_CASE      							=       min(array.shape[0] for array in raw_audio    )
            __SCREAMING_SNAKE_CASE      							=       int(np.floor(max_length / self.chunk_stride    )    )
            __SCREAMING_SNAKE_CASE      							=       (nb_step - 1) * self.chunk_stride + self.chunk_length
         elif padding:
            __SCREAMING_SNAKE_CASE      							=       max(array.shape[0] for array in raw_audio    )
            __SCREAMING_SNAKE_CASE      							=       int(np.ceil(max_length / self.chunk_stride    )    )
            __SCREAMING_SNAKE_CASE      							=       (nb_step - 1) * self.chunk_stride + self.chunk_length
            __SCREAMING_SNAKE_CASE      							=       """max_length"""
         else:
            __SCREAMING_SNAKE_CASE      							=       input_values

        # normal padding on batch
      if padded_inputs is None:
         __SCREAMING_SNAKE_CASE      							=       self.pad(
             lowerCamelCase				,max_length=lowerCamelCase				,truncation=lowerCamelCase				,padding=lowerCamelCase				,return_attention_mask=lowerCamelCase				,)
         if padding:
            __SCREAMING_SNAKE_CASE      							=       padded_inputs.pop("""attention_mask"""    )

      __SCREAMING_SNAKE_CASE      							=       []
      for example in padded_inputs.pop("""input_values"""    ):
         if self.feature_size == 1:
            __SCREAMING_SNAKE_CASE      							=       example[..., None]
         input_values.append(example.T    )

      __SCREAMING_SNAKE_CASE      							=       input_values
      if return_tensors is not None:
         __SCREAMING_SNAKE_CASE      							=       padded_inputs.convert_to_tensors(lowerCamelCase    )

      return padded_inputs












'''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


a        =						logging.get_logger(__name__)







class __a (   _snake_case			):
   __UpperCamelCase   : int	      =	'linear'
   __UpperCamelCase   : Tuple	      =	'cosine'
   __UpperCamelCase   : Tuple	      =	'cosine_with_restarts'
   __UpperCamelCase   : List[Any]	      =	'polynomial'
   __UpperCamelCase   : Optional[Any]	      =	'constant'
   __UpperCamelCase   : Optional[int]	      =	'constant_with_warmup'
   __UpperCamelCase   : List[Any]	      =	'piecewise_constant'





def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







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

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					List[Any]:

   '''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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       {}
   __SCREAMING_SNAKE_CASE      							=       step_rules.split(""","""      )
   for rule_str in rule_list[:-1]:
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       rule_str.split(""":"""      )
      __SCREAMING_SNAKE_CASE      							=       int(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       float(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       value
   __SCREAMING_SNAKE_CASE      							=       float(rule_list[-1]      )

   def create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      ):
      def rule_func(__UpperCAmelCase      ) -> float:
         __SCREAMING_SNAKE_CASE      							=       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

   __SCREAMING_SNAKE_CASE      							=       create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=-1      )						->					int:

   '''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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0.5							,	__UpperCAmelCase = -1      )						->					Dict:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_SNAKE_CASE      							=       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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 1							,	__UpperCAmelCase = -1      )						->					Tuple:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_SNAKE_CASE      							=       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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=1e-7							,	__UpperCAmelCase=1.0							,	__UpperCAmelCase=-1      )						->					Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       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:
         __SCREAMING_SNAKE_CASE      							=       lr_init - lr_end
         __SCREAMING_SNAKE_CASE      							=       num_training_steps - num_warmup_steps
         __SCREAMING_SNAKE_CASE      							=       1 - (current_step - num_warmup_steps) / decay_steps
         __SCREAMING_SNAKE_CASE      							=       lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )


a        =						{
    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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = None							,	__UpperCAmelCase = None							,	__UpperCAmelCase = None							,	__UpperCAmelCase = 1							,	__UpperCAmelCase = 1.0							,	__UpperCAmelCase = -1							,	)						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       SchedulerType(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       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      )





