




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(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''linear'''
    lowerCAmelCase        =				'''cosine'''
    lowerCAmelCase        =				'''cosine_with_restarts'''
    lowerCAmelCase        =				'''polynomial'''
    lowerCAmelCase        =				'''constant'''
    lowerCAmelCase        =				'''constant_with_warmup'''
    lowerCAmelCase        =				'''piecewise_constant'''







def lowerCamelCase__       (      _lowercase			,						_lowercase = -1				):

     '''simple docstring'''


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







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase = -1				):

     '''simple docstring'''


     def lr_lambda(_lowercase				):
          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			,						_lowercase			,						_lowercase = -1				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      {}
     UpperCAmelCase_      :							int       =      step_rules.split(''','''				)
     for rule_str in rule_list[:-1]:
          UpperCAmelCase_,   UpperCAmelCase_      :							Dict       =      rule_str.split(''':'''				)
          UpperCAmelCase_      :							Dict       =      int(_lowercase				)
          UpperCAmelCase_      :							Tuple       =      float(_lowercase				)
          UpperCAmelCase_      :							Optional[int]       =      value
     UpperCAmelCase_      :							Tuple       =      float(rule_list[-1]				)

     def create_rules_function(_lowercase			,						_lowercase				):
          def rule_func(_lowercase				) -> float:
               UpperCAmelCase_      :							Union[str, 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_      :							Tuple       =      create_rules_function(_lowercase			,						_lowercase				)

     return LambdaLR(_lowercase			,						_lowercase			,						last_epoch=_lowercase				)







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase=-1				):

     '''simple docstring'''


     def lr_lambda(_lowercase				):
          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			,						_lowercase			,						_lowercase			,						_lowercase = 0.5			,						_lowercase = -1				):

     '''simple docstring'''


     def lr_lambda(_lowercase				):
          if current_step < num_warmup_steps:
               return float(_lowercase				) / float(max(1			,						_lowercase				)				)
          UpperCAmelCase_      :							Dict       =      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			,						_lowercase			,						_lowercase			,						_lowercase = 1			,						_lowercase = -1				):

     '''simple docstring'''


     def lr_lambda(_lowercase				):
          if current_step < num_warmup_steps:
               return float(_lowercase				) / float(max(1			,						_lowercase				)				)
          UpperCAmelCase_      :							List[Any]       =      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			,						_lowercase			,						_lowercase			,						_lowercase=1E-7			,						_lowercase=1.0			,						_lowercase=-1				):

     '''simple docstring'''


     UpperCAmelCase_      :							List[str]       =      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				):
          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_      :							Any       =      lr_init - lr_end
               UpperCAmelCase_      :							Union[str, Any]       =      num_training_steps - num_warmup_steps
               UpperCAmelCase_      :							Optional[Any]       =      1 - (current_step - num_warmup_steps) / decay_steps
               UpperCAmelCase_      :							str       =      lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(_lowercase			,						_lowercase			,						_lowercase				)


__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 lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase = None			,						_lowercase = None			,						_lowercase = None			,						_lowercase = 1			,						_lowercase = 1.0			,						_lowercase = -1			,						):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, 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				)





from ...processing_utils import ProcessorMixin

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''SpeechT5FeatureExtractor'''
    lowerCAmelCase        =				'''SpeechT5Tokenizer'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         super().__init__(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def __call__(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''audio'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      kwargs.pop('''text'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''text_target'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      kwargs.pop('''audio_target'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      kwargs.pop('''sampling_rate'''       ,_SCREAMING_SNAKE_CASE  )

         if audio is not None and text is not None:
              raise ValueError(
                  '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''  )
         if audio_target is not None and text_target is not None:
              raise ValueError(
                  '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''  )
         if audio is None and audio_target is None and text is None and text_target is None:
              raise ValueError(
                  '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''  )

         if audio is not None:
              UpperCAmelCase_      :							Optional[Any]       =      self.feature_extractor(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         elif text is not None:
              UpperCAmelCase_      :							Dict       =      self.tokenizer(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							List[str]       =      None

         if audio_target is not None:
              UpperCAmelCase_      :							List[Any]       =      self.feature_extractor(audio_target=_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							int       =      targets['''input_values''']
         elif text_target is not None:
              UpperCAmelCase_      :							Optional[int]       =      self.tokenizer(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[Any]       =      targets['''input_ids''']
         else:
              UpperCAmelCase_      :							Tuple       =      None

         if inputs is None:
              return targets

         if targets is not None:
              UpperCAmelCase_      :							Dict       =      labels

              UpperCAmelCase_      :							Optional[int]       =      targets.get('''attention_mask'''  )
              if decoder_attention_mask is not None:
                   UpperCAmelCase_      :							List[Any]       =      decoder_attention_mask

         return inputs





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							Dict       =      kwargs.pop('''input_values'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      kwargs.pop('''input_ids'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''labels'''       ,_SCREAMING_SNAKE_CASE  )

         if input_values is not None and input_ids is not None:
              raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''  )
         if input_values is None and input_ids is None and labels is None:
              raise ValueError(
                  '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''  )

         if input_values is not None:
              UpperCAmelCase_      :							Tuple       =      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         elif input_ids is not None:
              UpperCAmelCase_      :							Tuple       =      self.tokenizer.pad(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							List[str]       =      None

         if labels is not None:
              if "input_ids" in labels or (isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) and "input_ids" in labels[0]):
                   UpperCAmelCase_      :							Tuple       =      self.tokenizer.pad(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Any       =      targets['''input_ids''']
              else:
                   UpperCAmelCase_      :							int       =      self.feature_extractor.feature_size
                   UpperCAmelCase_      :							List[str]       =      self.feature_extractor.num_mel_bins
                   UpperCAmelCase_      :							Optional[Any]       =      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							List[Any]       =      feature_size_hack
                   UpperCAmelCase_      :							List[Any]       =      targets['''input_values''']
         else:
              UpperCAmelCase_      :							Optional[int]       =      None

         if inputs is None:
              return targets

         if targets is not None:
              UpperCAmelCase_      :							Optional[int]       =      labels

              UpperCAmelCase_      :							Union[str, Any]       =      targets.get('''attention_mask'''  )
              if decoder_attention_mask is not None:
                   UpperCAmelCase_      :							List[Any]       =      decoder_attention_mask

         return inputs





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )