



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin





@flax.struct.dataclass
class 		UpperCAmelCase						:
   # setable values
   SCREAMING_SNAKE_CASE__							=     None
   SCREAMING_SNAKE_CASE__							=     None
   SCREAMING_SNAKE_CASE__							=     None  # sigma(t_i)





   @classmethod
   def 	__lowerCAmelCase							(	cls			):
         return cls()






@dataclass
class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     4_2
   SCREAMING_SNAKE_CASE__							=     4_2
   SCREAMING_SNAKE_CASE__							=     4_2






class 		UpperCAmelCase						(						snake_case_       ,snake_case_				):





   @property
   def 	__lowerCAmelCase							(	self			):
         return True





   @register_to_config
   def __init__(	self       ,     _lowerCAmelCase = 0.02       ,     _lowerCAmelCase = 100       ,     _lowerCAmelCase = 1.007       ,     _lowerCAmelCase = 80       ,     _lowerCAmelCase = 0.05       ,     _lowerCAmelCase = 50       ,     ):
         pass





   def 	__lowerCAmelCase							(	self			):
         return KarrasVeSchedulerState.create()





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = ()			):
         _lowerCAmelCase 			=  jnp.arange(0       ,     _lowerCAmelCase			)[::-1].copy()
         _lowerCAmelCase 			=  [
             (
                 self.config.sigma_max**2
                 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
             )
             for i in timesteps
         ]

         return state.replace(
             num_inference_steps=_lowerCAmelCase       ,     schedule=jnp.array(_lowerCAmelCase       ,     dtype=jnp.floataa			)       ,     timesteps=_lowerCAmelCase       ,     )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     ):
         if self.config.s_min <= sigma <= self.config.s_max:
               _lowerCAmelCase 			=  min(self.config.s_churn / state.num_inference_steps       ,     2**0.5 - 1			)
         else:
               _lowerCAmelCase 			=  0

         # sample eps ~ N(0, S_noise^2 * I)
         _lowerCAmelCase 			=  random.split(_lowerCAmelCase       ,     num=1			)
         _lowerCAmelCase 			=  self.config.s_noise * random.normal(key=_lowerCAmelCase       ,     shape=sample.shape			)
         _lowerCAmelCase 			=  sigma + gamma * sigma
         _lowerCAmelCase 			=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = True       ,     ):
         _lowerCAmelCase 			=  sample_hat + sigma_hat * model_output
         _lowerCAmelCase 			=  (sample_hat - pred_original_sample) / sigma_hat
         _lowerCAmelCase 			=  sample_hat + (sigma_prev - sigma_hat) * derivative

         if not return_dict:
               return (sample_prev, derivative, state)

         return FlaxKarrasVeOutput(prev_sample=_lowerCAmelCase       ,     derivative=_lowerCAmelCase       ,     state=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = True       ,     ):
         _lowerCAmelCase 			=  sample_prev + sigma_prev * model_output
         _lowerCAmelCase 			=  (sample_prev - pred_original_sample) / sigma_prev
         _lowerCAmelCase 			=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

         if not return_dict:
               return (sample_prev, derivative, state)

         return FlaxKarrasVeOutput(prev_sample=_lowerCAmelCase       ,     derivative=_lowerCAmelCase       ,     state=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         raise NotImplementedError()










from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     '''ClapFeatureExtractor'''
   SCREAMING_SNAKE_CASE__							=     ('''RobertaTokenizer''', '''RobertaTokenizerFast''')





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def __call__(	self       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     **_lowerCAmelCase			):
         _lowerCAmelCase 			=  kwargs.pop('''sampling_rate'''       ,     _lowerCAmelCase			)

         if text is None and audios is None:
               raise ValueError('''You have to specify either text or audios. Both cannot be none.'''			)

         if text is not None:
               _lowerCAmelCase 			=  self.tokenizer(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if audios is not None:
               _lowerCAmelCase 			=  self.feature_extractor(
                   _lowerCAmelCase       ,     sampling_rate=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if text is not None and audios is not None:
               _lowerCAmelCase 			=  audio_features.input_features
               return encoding
         elif text is not None:
               return encoding
         else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)       ,     tensor_type=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   @property
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer.model_input_names
         _lowerCAmelCase 			=  self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names			)			)