






"""simple docstring"""



import functools
import operator

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


A__	:    Tuple			 = logging.get_logger(__name__)

A__	:    Union[str, Any]			 = {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Any             =			"unispeech-sat"


			def __init__(				self					:  Optional[Any]       ,	snake_case__					:  Dict=32       ,	snake_case__					:  Dict=768       ,	snake_case__					:  Union[str, Any]=12       ,	snake_case__					:  Dict=12       ,	snake_case__					:  Tuple=3072       ,	snake_case__					:  List[Any]="gelu"       ,	snake_case__					:  List[Any]=0.1       ,	snake_case__					:  Tuple=0.1       ,	snake_case__					:  List[str]=0.1       ,	snake_case__					:  Optional[int]=0.0       ,	snake_case__					:  str=0.0       ,	snake_case__					:  int=0.1       ,	snake_case__					:  Tuple=0.1       ,	snake_case__					:  Any=0.02       ,	snake_case__					:  List[str]=1E-5       ,	snake_case__					:  Optional[Any]="group"       ,	snake_case__					:  str="gelu"       ,	snake_case__					:  Dict=(512, 512, 512, 512, 512, 512, 512)       ,	snake_case__					:  str=(5, 2, 2, 2, 2, 2, 2)       ,	snake_case__					:  Tuple=(10, 3, 3, 3, 3, 2, 2)       ,	snake_case__					:  int=False       ,	snake_case__					:  List[str]=128       ,	snake_case__					:  Optional[Any]=16       ,	snake_case__					:  str=False       ,	snake_case__					:  str=True       ,	snake_case__					:  List[Any]=0.05       ,	snake_case__					:  Optional[Any]=10       ,	snake_case__					:  Any=2       ,	snake_case__					:  Dict=0.0       ,	snake_case__					:  List[str]=10       ,	snake_case__					:  Any=0       ,	snake_case__					:  Optional[int]=320       ,	snake_case__					:  Dict=2       ,	snake_case__					:  Tuple=0.1       ,	snake_case__					:  Dict=100       ,	snake_case__					:  List[str]=256       ,	snake_case__					:  Union[str, Any]=256       ,	snake_case__					:  Any=0.1       ,	snake_case__					:  List[Any]="mean"       ,	snake_case__					:  Tuple=False       ,	snake_case__					:  Tuple=False       ,	snake_case__					:  Optional[Any]=256       ,	snake_case__					:  Optional[int]=(512, 512, 512, 512, 1500)       ,	snake_case__					:  Optional[int]=(5, 3, 3, 1, 1)       ,	snake_case__					:  str=(1, 2, 3, 1, 1)       ,	snake_case__					:  Optional[Any]=512       ,	snake_case__					:  Any=0       ,	snake_case__					:  Tuple=1       ,	snake_case__					:  int=2       ,	snake_case__					:  Optional[int]=504       ,	**snake_case__					:  Optional[Any]       ,	):
										super().__init__(**snake_case__       ,	pad_token_id=snake_case__       ,	bos_token_id=snake_case__       ,	eos_token_id=snake_case__ )
										lowerCamelCase_	:							Optional[Any]									=hidden_size
										lowerCamelCase_	:							List[str]									=feat_extract_norm
										lowerCamelCase_	:							Optional[int]									=feat_extract_activation
										lowerCamelCase_	:							Optional[Any]									=list(snake_case__ )
										lowerCamelCase_	:							str									=list(snake_case__ )
										lowerCamelCase_	:							Optional[Any]									=list(snake_case__ )
										lowerCamelCase_	:							Optional[int]									=conv_bias
										lowerCamelCase_	:							Tuple									=num_conv_pos_embeddings
										lowerCamelCase_	:							str									=num_conv_pos_embedding_groups
										lowerCamelCase_	:							List[str]									=len(self.conv_dim )
										lowerCamelCase_	:							List[Any]									=num_hidden_layers
										lowerCamelCase_	:							str									=intermediate_size
										lowerCamelCase_	:							Optional[int]									=hidden_act
										lowerCamelCase_	:							Dict									=num_attention_heads
										lowerCamelCase_	:							List[Any]									=hidden_dropout
										lowerCamelCase_	:							Dict									=attention_dropout
										lowerCamelCase_	:							List[str]									=activation_dropout
										lowerCamelCase_	:							List[Any]									=feat_proj_dropout
										lowerCamelCase_	:							Optional[Any]									=final_dropout
										lowerCamelCase_	:							int									=layerdrop
										lowerCamelCase_	:							Any									=layer_norm_eps
										lowerCamelCase_	:							Optional[int]									=initializer_range
										lowerCamelCase_	:							List[str]									=vocab_size
										lowerCamelCase_	:							Dict									=num_clusters
										lowerCamelCase_	:							str									=do_stable_layer_norm
										lowerCamelCase_	:							int									=use_weighted_layer_sum

										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
										lowerCamelCase_	:							Optional[int]									=apply_spec_augment
										lowerCamelCase_	:							Union[str, Any]									=mask_time_prob
										lowerCamelCase_	:							Any									=mask_time_length
										lowerCamelCase_	:							Dict									=mask_time_min_masks
										lowerCamelCase_	:							Dict									=mask_feature_prob
										lowerCamelCase_	:							Optional[Any]									=mask_feature_length
										lowerCamelCase_	:							Optional[Any]									=mask_feature_min_masks

										# parameters for pretraining with codevector quantized representations
										lowerCamelCase_	:							int									=num_codevectors_per_group
										lowerCamelCase_	:							int									=num_codevector_groups
										lowerCamelCase_	:							Dict									=contrastive_logits_temperature
										lowerCamelCase_	:							Union[str, Any]									=feat_quantizer_dropout
										lowerCamelCase_	:							Dict									=num_negatives
										lowerCamelCase_	:							int									=codevector_dim
										lowerCamelCase_	:							str									=proj_codevector_dim
										lowerCamelCase_	:							int									=diversity_loss_weight

										# ctc loss
										lowerCamelCase_	:							Tuple									=ctc_loss_reduction
										lowerCamelCase_	:							int									=ctc_zero_infinity

										# SequenceClassification-specific parameter. Feel free to ignore for other classes.
										lowerCamelCase_	:							int									=classifier_proj_size

										# XVector-specific parameters. Feel free to ignore for other classes.
										lowerCamelCase_	:							str									=list(snake_case__ )
										lowerCamelCase_	:							str									=list(snake_case__ )
										lowerCamelCase_	:							Optional[int]									=list(snake_case__ )
										lowerCamelCase_	:							Any									=xvector_output_dim


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











"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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        lowercase__      :
			_UpperCAmelCase		:CommonSchedulerState

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


			@classmethod
			def      UpperCAmelCase__						(				cls					:  int       ,	snake_case__					:  CommonSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray ):
										return cls(common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__ )







@dataclass
class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:DDPMSchedulerState







class        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:Any             =			[e.name for e in FlaxKarrasDiffusionSchedulers]

			_UpperCAmelCase		:jnp.dtype


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return True


			@register_to_config
			def __init__(				self					:  Optional[int]       ,	snake_case__					:  int = 1000       ,	snake_case__					:  float = 0.0_001       ,	snake_case__					:  float = 0.02       ,	snake_case__					:  str = "linear"       ,	snake_case__					:  Optional[jnp.ndarray] = None       ,	snake_case__					:  str = "fixed_small"       ,	snake_case__					:  bool = True       ,	snake_case__					:  str = "epsilon"       ,	snake_case__					:  jnp.dtype = jnp.floataa       ,	):
										lowerCamelCase_	:							str									=dtype


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Optional[CommonSchedulerState] = None ):
										if common is None:
																	lowerCamelCase_	:							int									=CommonSchedulerState.create(self )

										# standard deviation of the initial noise distribution
										lowerCamelCase_	:							Optional[Any]									=jnp.array(1.0       ,	dtype=self.dtype )

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

										return DDPMSchedulerState.create(
										    common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[int] = None ):
										return sample


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  int       ,	snake_case__					:  Tuple = () ):
										lowerCamelCase_	:							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
										lowerCamelCase_	:							List[str]									=(jnp.arange(0       ,	snake_case__ ) * step_ratio).round()[::-1]

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


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  Any=None ):
										lowerCamelCase_	:							List[str]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Union[str, Any]									=jnp.where(t > 0       ,	state.common.alphas_cumprod[t - 1]       ,	jnp.array(1.0       ,	dtype=self.dtype ) )

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
										# and sample from it to get previous sample
										# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
										lowerCamelCase_	:							Tuple									=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

										if variance_type is None:
																	lowerCamelCase_	:							List[Any]									=self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small":
																	lowerCamelCase_	:							List[str]									=jnp.clip(snake_case__       ,	a_min=1E-20 )
										# for rl-diffuser https://arxiv.org/abs/2205.09991
										elif variance_type == "fixed_small_log":
																	lowerCamelCase_	:							Dict									=jnp.log(jnp.clip(snake_case__       ,	a_min=1E-20 ) )
										elif variance_type == "fixed_large":
																	lowerCamelCase_	:							Optional[Any]									=state.common.betas[t]
										elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowerCamelCase_	:							Any									=jnp.log(state.common.betas[t] )
										elif variance_type == "learned":
																	return predicted_variance
										elif variance_type == "learned_range":
																	lowerCamelCase_	:							List[str]									=variance
																	lowerCamelCase_	:							Optional[int]									=state.common.betas[t]
																	lowerCamelCase_	:							Dict									=(predicted_variance + 1) / 2
																	lowerCamelCase_	:							Dict									=frac * max_log + (1 - frac) * min_log

										return variance


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  int       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[jax.random.KeyArray] = None       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							Union[str, Any]									=timestep

										if key is None:
																	lowerCamelCase_	:							Dict									=jax.random.PRNGKey(0 )

										if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																	lowerCamelCase_							,						lowerCamelCase_	:							Optional[Any]									=jnp.split(snake_case__       ,	sample.shape[1]       ,	axis=1 )
										else:
																	lowerCamelCase_	:							List[str]									=None

										# 1. compute alphas, betas
										lowerCamelCase_	:							Union[str, Any]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Dict									=jnp.where(t > 0       ,	state.common.alphas_cumprod[t - 1]       ,	jnp.array(1.0       ,	dtype=self.dtype ) )
										lowerCamelCase_	:							Any									=1 - alpha_prod_t
										lowerCamelCase_	:							List[str]									=1 - alpha_prod_t_prev

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										if self.config.prediction_type == "epsilon":
																	lowerCamelCase_	:							int									=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
																	lowerCamelCase_	:							List[Any]									=model_output
										elif self.config.prediction_type == "v_prediction":
																	lowerCamelCase_	:							Tuple									=(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
										else:
																	raise ValueError(
																	    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
																	    " for the FlaxDDPMScheduler." )

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
																	lowerCamelCase_	:							List[Any]									=jnp.clip(snake_case__       ,	-1       ,	1 )

										# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							int									=(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
										lowerCamelCase_	:							Optional[Any]									=state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							Any									=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										def random_variance():
																	lowerCamelCase_	:							Union[str, Any]									=jax.random.split(snake_case__       ,	num=1 )
																	lowerCamelCase_	:							List[Any]									=jax.random.normal(snake_case__       ,	shape=model_output.shape       ,	dtype=self.dtype )
																	return (self._get_variance(snake_case__       ,	snake_case__       ,	predicted_variance=snake_case__ ) ** 0.5) * noise

										lowerCamelCase_	:							Tuple									=jnp.where(t > 0       ,	random_variance()       ,	jnp.zeros(model_output.shape       ,	dtype=self.dtype ) )

										lowerCamelCase_	:							str									=pred_prev_sample + variance

										if not return_dict:
																	return (pred_prev_sample, state)

										return FlaxDDPMSchedulerOutput(prev_sample=snake_case__       ,	state=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return add_noise_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return get_velocity_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def __len__(				self					:  Tuple ):
										return self.config.num_train_timesteps



