from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class      __lowercase					(    __SCREAMING_SNAKE_CASE    ):
						lowercase					 =     'EncodecFeatureExtractor'
						lowercase					 =     ('T5Tokenizer', 'T5TokenizerFast')

						def __init__(	self				:      List[str]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      List[str]   )							->		Any:


									'''simple docstring'''

									super().__init__(__snake_case							,							__snake_case   )
									lowercase							=		self.feature_extractor
									lowercase							=		False

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      Union[str, Any]=None							,							__lowerCamelCase				:      str=None							,							__lowerCamelCase				:      List[Any]=True   )							->		str:


									'''simple docstring'''

									return self.tokenizer.get_decoder_prompt_ids(task=__snake_case							,							language=__snake_case							,							no_timestamps=__snake_case   )

						def __call__(	self				:      int							,							*__lowerCamelCase				:      Optional[int]							,							**__lowerCamelCase				:      int   )							->		List[str]:


									'''simple docstring'''

									if self._in_target_context_manager:
												return self.current_processor(*__snake_case							,							**__snake_case   )

									lowercase							=		kwargs.pop('''audio'''							,							__snake_case   )
									lowercase							=		kwargs.pop('''sampling_rate'''							,							__snake_case   )
									lowercase							=		kwargs.pop('''text'''							,							__snake_case   )
									if len(__snake_case   ) > 0:
												lowercase							=		args[0]
												lowercase							=		args[1:]

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

									if text is not None:
												lowercase							=		self.tokenizer(__snake_case							,							**__snake_case   )

									if audio is not None:
												lowercase							=		self.feature_extractor(__snake_case							,							*__snake_case							,							sampling_rate=__snake_case							,							**__snake_case   )

									if audio is None:
												return inputs

									elif text is None:
												return audio_inputs

									else:
												lowercase							=		audio_inputs['''input_values''']
												if "padding_mask" in audio_inputs:
															lowercase							=		audio_inputs['''padding_mask''']
												return inputs

						def        __a		(	self				:      Tuple							,							*__lowerCamelCase				:      Union[str, Any]							,							**__lowerCamelCase				:      Optional[int]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		kwargs.pop('''audio'''							,							__snake_case   )
									lowercase							=		kwargs.pop('''padding_mask'''							,							__snake_case   )

									if len(__snake_case   ) > 0:
												lowercase							=		args[0]
												lowercase							=		args[1:]

									if audio_values is not None:
												return self._decode_audio(__snake_case							,							padding_mask=__snake_case   )
									else:
												return self.tokenizer.batch_decode(*__snake_case							,							**__snake_case   )

						def        __a		(	self				:      str							,							*__lowerCamelCase				:      Any							,							**__lowerCamelCase				:      int   )							->		List[str]:


									'''simple docstring'''

									return self.tokenizer.decode(*__snake_case							,							**__snake_case   )



						def        __a		(	self				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional = None   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		to_numpy(__snake_case   )
									lowercase							=		audio_values.shape

									if padding_mask is None:
												return list(__snake_case   )

									lowercase							=		to_numpy(__snake_case   )

									# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
									# token (so that the generated audio values are **not** treated as padded tokens)
									lowercase							=		seq_len - padding_mask.shape[-1]
									lowercase							=		1 - self.feature_extractor.padding_value
									lowercase							=		np.pad(__snake_case							,							((0, 0), (0, difference))							,							'''constant'''							,							constant_values=__snake_case   )

									lowercase							=		audio_values.tolist()
									for i in range(__snake_case   ):
												lowercase							=		np.asarray(audio_values[i]   )[
												    padding_mask[i][None, :] != self.feature_extractor.padding_value
												]
												lowercase							=		sliced_audio.reshape(__snake_case							,							-1   )

									return audio_values






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      __lowercase					:
						# setable values
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None  # sigma(t_i)

						@classmethod
						def        __a		(	cls				:      List[str]   )							->		Dict:


									'''simple docstring'''

									return cls()






@dataclass
class      __lowercase					(    _A    ):
						lowercase					 =     42
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _A       ,     _A    ):

						@property
						def        __a		(	self				:      Union[str, Any]   )							->		Optional[Any]:


									'''simple docstring'''

									return True

						@register_to_config
						def __init__(	self				:      int							,							__lowerCamelCase				:      float = 0.02							,							__lowerCamelCase				:      float = 1_00							,							__lowerCamelCase				:      float = 1.007							,							__lowerCamelCase				:      float = 80							,							__lowerCamelCase				:      float = 0.05							,							__lowerCamelCase				:      float = 50							,							)							->		List[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									return KarrasVeSchedulerState.create()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Tuple = ()   )							->		KarrasVeSchedulerState:


									'''simple docstring'''

									lowercase							=		jnp.arange(0							,							__lowerCamelCase   )[::-1].copy()
									lowercase							=		[
									    (
									        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        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      random.KeyArray							,							)							->		Tuple[jnp.ndarray, float]:


									'''simple docstring'''

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

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

									return sample_hat, sigma_hat

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_hat + sigma_hat * model_output
									lowercase							=		(sample_hat - pred_original_sample) / sigma_hat
									lowercase							=		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        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_prev + sigma_prev * model_output
									lowercase							=		(sample_prev - pred_original_sample) / sigma_prev
									lowercase							=		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        __a		(	self				:      int							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Dict   )							->		Tuple:


									'''simple docstring'''

									raise NotImplementedError()





