






"""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_	:    int            =      logging.get_logger(__name__)





class   UpperCamelCase_ (       a_  ):
  _A       :					Tuple				=	'linear'
  _A       :					Tuple				=	'cosine'
  _A       :					Union[str, Any]				=	'cosine_with_restarts'
  _A       :					Optional[Any]				=	'polynomial'
  _A       :					List[Any]				=	'constant'
  _A       :					List[str]				=	'constant_with_warmup'
  _A       :					List[str]				=	'piecewise_constant'






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase = -1		):







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






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase = -1		):







       '''simple docstring'''

       def lr_lambda(lowerCAmelCase		):
              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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase = -1		):







       '''simple docstring'''
       UpperCAmelCase	    =			{}
       UpperCAmelCase	    =			step_rules.split(""","""		)
       for rule_str in rule_list[:-1]:
              UpperCAmelCase    ,       UpperCAmelCase	    =			rule_str.split(""":"""		)
              UpperCAmelCase	    =			int(lowerCAmelCase		)
              UpperCAmelCase	    =			float(lowerCAmelCase		)
              UpperCAmelCase	    =			value
       UpperCAmelCase	    =			float(rule_list[-1]		)

       def create_rules_function(lowerCAmelCase	,      lowerCAmelCase		):
              def rule_func(lowerCAmelCase		) -> float:
                     UpperCAmelCase	    =			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

       UpperCAmelCase	    =			create_rules_function(lowerCAmelCase	,      lowerCAmelCase		)

       return LambdaLR(lowerCAmelCase	,      lowerCAmelCase	,      last_epoch=lowerCAmelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=-1		):







       '''simple docstring'''

       def lr_lambda(lowerCAmelCase		):
              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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase = 0.5	,      lowerCAmelCase = -1		):







       '''simple docstring'''

       def lr_lambda(lowerCAmelCase		):
              if current_step < num_warmup_steps:
                     return float(lowerCAmelCase		) / float(max(1	,      lowerCAmelCase		)		)
              UpperCAmelCase	    =			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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase = 1	,      lowerCAmelCase = -1		):







       '''simple docstring'''

       def lr_lambda(lowerCAmelCase		):
              if current_step < num_warmup_steps:
                     return float(lowerCAmelCase		) / float(max(1	,      lowerCAmelCase		)		)
              UpperCAmelCase	    =			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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=1e-7	,      lowerCAmelCase=1.0	,      lowerCAmelCase=-1		):







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

       return LambdaLR(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)


lowerCAmelCase_	:    Union[str, Any]            =      {
    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       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase = None	,      lowerCAmelCase = None	,      lowerCAmelCase = None	,      lowerCAmelCase = 1	,      lowerCAmelCase = 1.0	,      lowerCAmelCase = -1	,      ):







       '''simple docstring'''
       UpperCAmelCase	    =			SchedulerType(lowerCAmelCase		)
       UpperCAmelCase	    =			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		)








"""simple docstring"""







import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_	:    List[Any]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Optional[Any]            =      {
    '''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
    '''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Dict				=	'encodec'



  def __init__(      self ,		snake_case__=[1.5, 3.0, 6.0, 12.0, 24.0] ,		snake_case__=2_40_00 ,		snake_case__=1 ,		snake_case__=False ,		snake_case__=None ,		snake_case__=None ,		snake_case__=1_28 ,		snake_case__=32 ,		snake_case__=1 ,		snake_case__=[8, 5, 4, 2] ,		snake_case__="weight_norm" ,		snake_case__=7 ,		snake_case__=7 ,		snake_case__=3 ,		snake_case__=2 ,		snake_case__=True ,		snake_case__="reflect" ,		snake_case__=2 ,		snake_case__=2 ,		snake_case__=1.0 ,		snake_case__=10_24 ,		snake_case__=None ,		snake_case__=True ,		**snake_case__ ,		)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			target_bandwidths
         UpperCAmelCase	    =			sampling_rate
         UpperCAmelCase	    =			audio_channels
         UpperCAmelCase	    =			normalize
         UpperCAmelCase	    =			chunk_length_s
         UpperCAmelCase	    =			overlap
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_filters
         UpperCAmelCase	    =			num_residual_layers
         UpperCAmelCase	    =			upsampling_ratios
         UpperCAmelCase	    =			norm_type
         UpperCAmelCase	    =			kernel_size
         UpperCAmelCase	    =			last_kernel_size
         UpperCAmelCase	    =			residual_kernel_size
         UpperCAmelCase	    =			dilation_growth_rate
         UpperCAmelCase	    =			use_causal_conv
         UpperCAmelCase	    =			pad_mode
         UpperCAmelCase	    =			compress
         UpperCAmelCase	    =			num_lstm_layers
         UpperCAmelCase	    =			trim_right_ratio
         UpperCAmelCase	    =			codebook_size
         UpperCAmelCase	    =			codebook_dim if codebook_dim is not None else hidden_size
         UpperCAmelCase	    =			use_conv_shortcut

         if self.norm_type not in ["weight_norm", "time_group_norm"]:
                raise ValueError(
                    f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''				)

         super().__init__(**snake_case__				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """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				)			->			Optional[int]:



         """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				)				)



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			np.prod(self.upsampling_ratios				)
         return math.ceil(self.sampling_rate / hop_length				)



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)
