

import functools
import operator

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


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

UpperCAmelCase_       :				Optional[Any]		   =      {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}






class     lowerCamelCase_			(  UpperCamelCase_  ):
 _lowercase		:      Optional[Any]    				=							"""hubert"""




 def __init__(    self	:    Optional[int]				,			__A	:    List[Any]=32				,			__A	:    Tuple=768				,			__A	:    Any=12				,			__A	:    str=12				,			__A	:    Optional[int]=3072				,			__A	:    Optional[int]="gelu"				,			__A	:    List[Any]=0.1				,			__A	:    Optional[int]=0.1				,			__A	:    Union[str, Any]=0.1				,			__A	:    List[Any]=True				,			__A	:    Union[str, Any]=0.0				,			__A	:    Tuple=0.1				,			__A	:    Dict=0.1				,			__A	:    List[Any]=0.0_2				,			__A	:    Optional[Any]=1e-5				,			__A	:    List[str]="group"				,			__A	:    Tuple="gelu"				,			__A	:    int=(512, 512, 512, 512, 512, 512, 512)				,			__A	:    Tuple=(5, 2, 2, 2, 2, 2, 2)				,			__A	:    Dict=(10, 3, 3, 3, 3, 2, 2)				,			__A	:    List[str]=False				,			__A	:    List[Any]=128				,			__A	:    str=16				,			__A	:    str=False				,			__A	:    Union[str, Any]=True				,			__A	:    int=0.0_5				,			__A	:    Tuple=10				,			__A	:    Union[str, Any]=2				,			__A	:    Tuple=0.0				,			__A	:    Optional[Any]=10				,			__A	:    str=0				,			__A	:    Union[str, Any]="sum"				,			__A	:    List[Any]=False				,			__A	:    List[Any]=False				,			__A	:    List[Any]=256				,			__A	:    Dict=0				,			__A	:    Tuple=1				,			__A	:    str=2				,			**__A	:    int				,			):
  super().__init__(**__SCREAMING_SNAKE_CASE				,			pad_token_id=__SCREAMING_SNAKE_CASE				,			bos_token_id=__SCREAMING_SNAKE_CASE				,			eos_token_id=__SCREAMING_SNAKE_CASE       )
  __A   : str								= hidden_size
  __A   : List[Any]								= feat_extract_norm
  __A   : Union[str, Any]								= feat_extract_activation
  __A   : str								= list(__SCREAMING_SNAKE_CASE       )
  __A   : Union[str, Any]								= list(__SCREAMING_SNAKE_CASE       )
  __A   : Optional[Any]								= list(__SCREAMING_SNAKE_CASE       )
  __A   : Optional[int]								= conv_bias
  __A   : Tuple								= num_conv_pos_embeddings
  __A   : Dict								= num_conv_pos_embedding_groups
  __A   : List[str]								= len(self.conv_dim       )
  __A   : Union[str, Any]								= num_hidden_layers
  __A   : Any								= intermediate_size
  __A   : Optional[Any]								= hidden_act
  __A   : List[Any]								= num_attention_heads
  __A   : Dict								= hidden_dropout
  __A   : str								= attention_dropout
  __A   : Union[str, Any]								= activation_dropout
  __A   : List[str]								= feat_proj_layer_norm
  __A   : str								= feat_proj_dropout
  __A   : Optional[int]								= final_dropout
  __A   : Optional[int]								= layerdrop
  __A   : List[str]								= layer_norm_eps
  __A   : Union[str, Any]								= initializer_range
  __A   : List[str]								= vocab_size
  __A   : List[Any]								= do_stable_layer_norm
  __A   : str								= use_weighted_layer_sum
  __A   : List[Any]								= classifier_proj_size

  if (
      (len(self.conv_stride       ) != self.num_feat_extract_layers)
      or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
      or (len(self.conv_dim       ) != self.num_feat_extract_layers)
  ):
   raise ValueError(
       """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
       """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
       F""" {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,"""
       F""" `len(config.conv_kernel) = {len(self.conv_kernel       )}`."""       )

  # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
  __A   : Tuple								= apply_spec_augment
  __A   : List[str]								= mask_time_prob
  __A   : List[Any]								= mask_time_length
  __A   : Any								= mask_time_min_masks
  __A   : List[Any]								= mask_feature_prob
  __A   : Optional[Any]								= mask_feature_length
  __A   : List[str]								= mask_feature_min_masks

  # ctc loss
  __A   : Tuple								= ctc_loss_reduction
  __A   : Optional[int]								= ctc_zero_infinity




 @property
 def 					lowerCAmelCase_					(    self	:    List[Any]       ):
  return functools.reduce(operator.mul				,			self.conv_stride				,			1       )








# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class     lowerCamelCase_			:
	_lowercase		:      CommonSchedulerState
	_lowercase		:      jnp.ndarray

	# setable values
	_lowercase		:      jnp.ndarray
	_lowercase		:      jnp.ndarray
	_lowercase		:      Optional[int]    				=							None




	@classmethod
	def 					lowerCAmelCase_					(    cls	:    int				,			__A	:    CommonSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return cls(
		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)






@dataclass
class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      DDIMSchedulerState






class     lowerCamelCase_			(  _lowercase	,			_lowercase  ):
	_lowercase		:      Union[str, Any]    				=							[e.name for e in FlaxKarrasDiffusionSchedulers]

	_lowercase		:      jnp.dtype




	@property
	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		return True




	@register_to_config
	def __init__(    self	:    Any				,			__A	:    int = 1000				,			__A	:    float = 0.0_0_0_1				,			__A	:    float = 0.0_2				,			__A	:    str = "linear"				,			__A	:    Optional[jnp.ndarray] = None				,			__A	:    bool = True				,			__A	:    int = 0				,			__A	:    str = "epsilon"				,			__A	:    jnp.dtype = jnp.floataa				,			):
		__A   : Optional[int]								= dtype




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    Optional[CommonSchedulerState] = None       ):
		if common is None:
			__A   : Any								= CommonSchedulerState.create(self       )

		# At every step in ddim, we are looking into the previous alphas_cumprod
		# For the final step, there is no previous alphas_cumprod because we are already at 0
		# `set_alpha_to_one` decides whether we set this parameter simply to one or
		# whether we use the final alpha of the "non-previous" one.
		__A   : Tuple								= (
		    jnp.array(1.0				,			dtype=self.dtype       ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
		)

		# standard deviation of the initial noise distribution
		__A   : List[str]								= jnp.array(1.0				,			dtype=self.dtype       )

		__A   : Any								= jnp.arange(0				,			self.config.num_train_timesteps       ).round()[::-1]

		return DDIMSchedulerState.create(
		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    Optional[int] = None       ):
		return sample




	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    DDIMSchedulerState				,			__A	:    int				,			__A	:    Tuple = ()       ):
		__A   : Union[str, Any]								= self.config.num_train_timesteps // num_inference_steps
		# creates integer timesteps by multiplying by ratio
		# rounding to avoid issues when num_inference_step is power of 3
		__A   : Optional[Any]								= (jnp.arange(0				,			__A       ) * step_ratio).round()[::-1] + self.config.steps_offset

		return state.replace(
		    num_inference_steps=__A				,			timesteps=__A				,			)




	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    DDIMSchedulerState				,			__A	:    str				,			__A	:    List[Any]       ):
		__A   : Tuple								= state.common.alphas_cumprod[timestep]
		__A   : Tuple								= jnp.where(
		    prev_timestep >= 0				,			state.common.alphas_cumprod[prev_timestep]				,			state.final_alpha_cumprod       )
		__A   : List[Any]								= 1 - alpha_prod_t
		__A   : Tuple								= 1 - alpha_prod_t_prev

		__A   : List[Any]								= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

		return variance




	def 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    int				,			__A	:    jnp.ndarray				,			__A	:    float = 0.0				,			__A	:    bool = True				,			):
		if state.num_inference_steps is None:
			raise ValueError(
			    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""       )

		# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
		# Ideally, read DDIM paper in-detail understanding

		# Notation (<variable name> -> <name in paper>
		# - pred_noise_t -> e_theta(x_t, t)
		# - pred_original_sample -> f_theta(x_t, t) or x_0
		# - std_dev_t -> sigma_t
		# - eta -> η
		# - pred_sample_direction -> "direction pointing to x_t"
		# - pred_prev_sample -> "x_t-1"

		# 1. get previous step value (=t-1)
		__A   : Optional[Any]								= timestep - self.config.num_train_timesteps // state.num_inference_steps

		__A   : Optional[int]								= state.common.alphas_cumprod
		__A   : Optional[int]								= state.final_alpha_cumprod

		# 2. compute alphas, betas
		__A   : Optional[int]								= alphas_cumprod[timestep]
		__A   : str								= jnp.where(prev_timestep >= 0				,			alphas_cumprod[prev_timestep]				,			__A       )

		__A   : Any								= 1 - alpha_prod_t

		# 3. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		if self.config.prediction_type == "epsilon":
			__A   : Any								= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			__A   : List[Any]								= model_output
		elif self.config.prediction_type == "sample":
			__A   : Dict								= model_output
			__A   : Tuple								= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
		elif self.config.prediction_type == "v_prediction":
			__A   : int								= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
			__A   : str								= (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
		else:
			raise ValueError(
			    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
			    """ `v_prediction`"""       )

		# 4. compute variance: "sigma_t(η)" -> see formula (16)
		# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
		__A   : Union[str, Any]								= self._get_variance(__A				,			__A				,			__A       )
		__A   : Any								= eta * variance ** 0.5

		# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		__A   : Any								= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

		# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		__A   : Optional[int]								= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

		if not return_dict:
			return (prev_sample, state)

		return FlaxDDIMSchedulerOutput(prev_sample=__A				,			state=__A       )




	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return add_noise_common(state.common				,			__A				,			__A				,			__A       )




	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return get_velocity_common(state.common				,			__A				,			__A				,			__A       )




	def __len__(    self	:    Union[str, Any]       ):
		return self.config.num_train_timesteps





