


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class _lowerCamelCase(		_a  ):
       lowercase_					:      Dict       					=     """Wav2Vec2FeatureExtractor"""
       lowercase_					:      Tuple       					=     """AutoTokenizer"""
       def __init__(   self, lowerCamelCase, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           super().__init__(lowerCamelCase, lowerCamelCase)
           _lowercase			:				Any 						=						self.feature_extractor
           _lowercase			:				Dict 						=						False
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase, **lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           try:
               return super().from_pretrained(lowerCamelCase, **lowerCamelCase)
           except OSError:
               warnings.warn(
                   F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
                   ' include a `tokenizer_class` attribute is deprecated and will be '
                   'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
                   ' attribute to either your `config.json` or `tokenizer_config.json` '
                   'file to suppress this warning: ', lowerCamelCase, )

               _lowercase			:				Optional[Any] 						=						WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase, **lowerCamelCase)
               _lowercase			:				int 						=						WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase, **lowerCamelCase)

               return cls(feature_extractor=lowerCamelCase, tokenizer=lowerCamelCase)
       def __call__(   self, *lowerCamelCase, **lowerCamelCase)			->  List[Any]:




           """simple docstring"""

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

           if "raw_speech" in kwargs:
               warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.')
               _lowercase			:				Any 						=						kwargs.pop('raw_speech')
           else:
               _lowercase			:				Union[str, Any] 						=						kwargs.pop('audio', lowerCamelCase)
           _lowercase			:				int 						=						kwargs.pop('sampling_rate', lowerCamelCase)
           _lowercase			:				Optional[int] 						=						kwargs.pop('text', lowerCamelCase)
           if len(lowerCamelCase) > 0:
               _lowercase			:				Optional[int] 						=						args[0]
               _lowercase			:				Union[str, Any] 						=						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 audio is not None:
               _lowercase			:				Union[str, Any] 						=						self.feature_extractor(lowerCamelCase, *lowerCamelCase, sampling_rate=lowerCamelCase, **lowerCamelCase)
           if text is not None:
               _lowercase			:				List[str] 						=						self.tokenizer(lowerCamelCase, **lowerCamelCase)

           if text is None:
               return inputs
           elif audio is None:
               return encodings
           else:
               _lowercase			:				Any 						=						encodings['input_ids']
               return inputs
       def      UpperCamelCase	(   self, *lowerCamelCase, **lowerCamelCase)			->  int:




           """simple docstring"""

           if self._in_target_context_manager:
               return self.current_processor.pad(*lowerCamelCase, **lowerCamelCase)

           _lowercase			:				Optional[int] 						=						kwargs.pop('input_features', lowerCamelCase)
           _lowercase			:				Dict 						=						kwargs.pop('labels', lowerCamelCase)
           if len(lowerCamelCase) > 0:
               _lowercase			:				List[str] 						=						args[0]
               _lowercase			:				List[Any] 						=						args[1:]

           if input_features is not None:
               _lowercase			:				Optional[int] 						=						self.feature_extractor.pad(lowerCamelCase, *lowerCamelCase, **lowerCamelCase)
           if labels is not None:
               _lowercase			:				Dict 						=						self.tokenizer.pad(lowerCamelCase, **lowerCamelCase)

           if labels is None:
               return input_features
           elif input_features is None:
               return labels
           else:
               _lowercase			:				Any 						=						labels['input_ids']
               return input_features
       def      UpperCamelCase	(   self, *lowerCamelCase, **lowerCamelCase)			->  List[Any]:




           """simple docstring"""

           return self.tokenizer.batch_decode(*lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, *lowerCamelCase, **lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

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




       @contextmanager
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           warnings.warn(
               '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
               'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
               'your audio inputs, or in a separate call.')
           _lowercase			:				Optional[int] 						=						True
           _lowercase			:				int 						=						self.tokenizer
           yield
           _lowercase			:				Any 						=						self.feature_extractor
           _lowercase			:				List[Any] 						=						False






import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE			: str				   =					"scheduler_config.json"






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     1
       lowercase_					:      Dict       					=     2
       lowercase_					:      Union[str, Any]       					=     3
       lowercase_					:      Tuple       					=     4
       lowercase_					:      Optional[Any]       					=     5






@dataclass
class _lowerCamelCase(		_a  ):
       lowercase_					:      jnp.ndarray






class _lowerCamelCase:
       lowercase_					:      Union[str, Any]       					=     SCHEDULER_CONFIG_NAME
       lowercase_					:      str       					=     ["""dtype"""]
       lowercase_					:      Dict       					=     []
       lowercase_					:      int       					=     True
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=False, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase, subfolder=lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase, )
           _lowercase						,							_lowercase			:				Tuple 						=						cls.from_config(lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase)

           if hasattr(lowerCamelCase, 'create_state') and getattr(lowerCamelCase, 'has_state', lowerCamelCase):
               _lowercase			:				List[Any] 						=						scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, **lowerCamelCase)			->  Any:




           """simple docstring"""

           self.save_config(save_directory=lowerCamelCase, push_to_hub=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def      UpperCamelCase	(   cls)			->  Any:




           """simple docstring"""

           _lowercase			:				Any 						=						list(set([cls.__name__] + cls._compatibles))
           _lowercase			:				Dict 						=						importlib.import_module(__name__.split('.')[0])
           _lowercase			:				Any 						=						[
               getattr(lowerCamelCase, lowerCamelCase) for c in compatible_classes_str if hasattr(lowerCamelCase, lowerCamelCase)
           ]
           return compatible_classes



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		jnp.ndarray:
    assert len(lowerCamelCase_					) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_					) - x.ndim)					)				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_=jnp.floataa					)       ->		jnp.ndarray:

    def alpha_bar(lowerCamelCase_					):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    _lowercase			:				List[Any] 						=						[]
    for i in range(lowerCamelCase_					):
        _lowercase			:				Any 						=						i / num_diffusion_timesteps
        _lowercase			:				Any 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(lowerCamelCase_					) / alpha_bar(lowerCamelCase_					)				,					lowerCamelCase_					)					)
    return jnp.array(lowerCamelCase_				,					dtype=lowerCamelCase_					)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						scheduler.config

           if config.trained_betas is not None:
               _lowercase			:				str 						=						jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase			:				List[Any] 						=						jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Dict 						=						(
                   jnp.linspace(
                       config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Optional[int] 						=						betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase			:				List[str] 						=						1.0 - betas

           _lowercase			:				Union[str, Any] 						=						jnp.cumprod(lowerCamelCase, axis=0)

           return cls(
               alphas=lowerCamelCase, betas=lowerCamelCase, alphas_cumprod=lowerCamelCase, )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				str 						=						state.alphas_cumprod

    _lowercase			:				str 						=						alphas_cumprod[timesteps] ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_alpha_prod.flatten()
    _lowercase			:				Tuple 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    _lowercase			:				Union[str, Any] 						=						(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_one_minus_alpha_prod.flatten()
    _lowercase			:				int 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    _lowercase						,							_lowercase			:				Optional[int] 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				str 						=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Tuple:
    _lowercase						,							_lowercase			:				Tuple 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity


