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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_A				:							List[str]							= logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 				__snake_case  (     lowerCAmelCase__						):


    '''simple docstring'''







    lowerCamelCase__						:		int							=  4_2
    lowerCamelCase__						:		int							=  None





def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_=0.9_99   ,       lowerCAmelCase_="cosine"   ,       )							->	Union[str, Any]:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(lowerCAmelCase_  ):
            return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2  ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(lowerCAmelCase_  ):
            return math.exp(t * -12.0  )

    else:
        raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''  )

    SCREAMING_SNAKE_CASE__       							= []
    for i in range(lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__       							= i / num_diffusion_timesteps
        SCREAMING_SNAKE_CASE__       							= (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(lowerCAmelCase_  ) / alpha_bar_fn(lowerCAmelCase_  )   ,       lowerCAmelCase_  )  )
    return torch.tensor(lowerCAmelCase_   ,       dtype=torch.floataa  )



class 				__snake_case  (     lowerCAmelCase__ ,    lowerCAmelCase__						):


    '''simple docstring'''







    lowerCamelCase__						:		Any							=  [e.name for e in KarrasDiffusionSchedulers]
    lowerCamelCase__						:		int							=  1



    @register_to_config
    def __init__(					self      ,							A_ = 10_00      ,							A_ = 0.0001      ,							A_ = 0.02      ,							A_ = "linear"      ,							A_ = None      ,							A_ = "epsilon"      ,							A_ = "linspace"      ,							A_ = 0      ,							):





        '''simple docstring'''

        if trained_betas is not None:
            SCREAMING_SNAKE_CASE__       							= torch.tensor(_lowerCamelCase      ,							dtype=torch.floataa    )
        elif beta_schedule == "linear":
            SCREAMING_SNAKE_CASE__       							= torch.linspace(_lowerCamelCase      ,							_lowerCamelCase      ,							_lowerCamelCase      ,							dtype=torch.floataa    )
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            SCREAMING_SNAKE_CASE__       							= (
                torch.linspace(beta_start**0.5      ,							beta_end**0.5      ,							_lowerCamelCase      ,							dtype=torch.floataa    ) ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            SCREAMING_SNAKE_CASE__       							= betas_for_alpha_bar(_lowerCamelCase    )
        else:
            raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''    )

        SCREAMING_SNAKE_CASE__       							= 1.0 - self.betas
        SCREAMING_SNAKE_CASE__       							= torch.cumprod(self.alphas      ,							dim=0    )

        SCREAMING_SNAKE_CASE__       							= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
        SCREAMING_SNAKE_CASE__       							= np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
        SCREAMING_SNAKE_CASE__       							= torch.from_numpy(_lowerCamelCase    )

        # setable values
        SCREAMING_SNAKE_CASE__       							= None
        SCREAMING_SNAKE_CASE__       							= np.linspace(0      ,							num_train_timesteps - 1      ,							_lowerCamelCase      ,							dtype=_lowerCamelCase    )[::-1].copy()
        SCREAMING_SNAKE_CASE__       							= torch.from_numpy(_lowerCamelCase    )
        SCREAMING_SNAKE_CASE__       							= False



    @property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        if self.config.timestep_spacing in ["linspace", "trailing"]:
            return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5



    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        if isinstance(_lowerCamelCase      ,							torch.Tensor    ):
            SCREAMING_SNAKE_CASE__       							= timestep.to(self.timesteps.device    )
        SCREAMING_SNAKE_CASE__       							= (self.timesteps == timestep).nonzero().item()
        SCREAMING_SNAKE_CASE__       							= self.sigmas[step_index]
        SCREAMING_SNAKE_CASE__       							= sample / ((sigma**2 + 1) ** 0.5)
        SCREAMING_SNAKE_CASE__       							= True
        return sample



    def    lowercase_				(					self      ,							A_      ,							A_ = None    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= num_inference_steps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
            SCREAMING_SNAKE_CASE__       							= np.linspace(0      ,							self.config.num_train_timesteps - 1      ,							_lowerCamelCase      ,							dtype=_lowerCamelCase    )[
                ::-1
            ].copy()
        elif self.config.timestep_spacing == "leading":
            SCREAMING_SNAKE_CASE__       							= self.config.num_train_timesteps // self.num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            SCREAMING_SNAKE_CASE__       							= (np.arange(0      ,							_lowerCamelCase    ) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase    )
            timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
            SCREAMING_SNAKE_CASE__       							= self.config.num_train_timesteps / self.num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            SCREAMING_SNAKE_CASE__       							= (np.arange(self.config.num_train_timesteps      ,							0      ,							-step_ratio    )).round().copy().astype(_lowerCamelCase    )
            timesteps -= 1
        else:
            raise ValueError(
                f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''    )

        SCREAMING_SNAKE_CASE__       							= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
        SCREAMING_SNAKE_CASE__       							= np.interp(_lowerCamelCase      ,							np.arange(0      ,							len(_lowerCamelCase    )    )      ,							_lowerCamelCase    )
        SCREAMING_SNAKE_CASE__       							= np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
        SCREAMING_SNAKE_CASE__       							= torch.from_numpy(_lowerCamelCase    ).to(device=_lowerCamelCase    )
        if str(_lowerCamelCase    ).startswith('''mps'''    ):
            # mps does not support float64
            SCREAMING_SNAKE_CASE__       							= torch.from_numpy(_lowerCamelCase    ).to(_lowerCamelCase      ,							dtype=torch.floataa    )
        else:
            SCREAMING_SNAKE_CASE__       							= torch.from_numpy(_lowerCamelCase    ).to(device=_lowerCamelCase    )



    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_ = None      ,							A_ = True      ,							):





        '''simple docstring'''

        if (
            isinstance(_lowerCamelCase      ,							_lowerCamelCase    )
            or isinstance(_lowerCamelCase      ,							torch.IntTensor    )
            or isinstance(_lowerCamelCase      ,							torch.LongTensor    )
        ):
            raise ValueError(
                (
                    '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                    ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
                    ''' one of the `scheduler.timesteps` as a timestep.'''
                )      ,							)

        if not self.is_scale_input_called:
            logger.warning(
                '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                '''See `StableDiffusionPipeline` for a usage example.'''    )

        if isinstance(_lowerCamelCase      ,							torch.Tensor    ):
            SCREAMING_SNAKE_CASE__       							= timestep.to(self.timesteps.device    )

        SCREAMING_SNAKE_CASE__       							= (self.timesteps == timestep).nonzero().item()
        SCREAMING_SNAKE_CASE__       							= self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
            SCREAMING_SNAKE_CASE__       							= sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
            # * c_out + input * c_skip
            SCREAMING_SNAKE_CASE__       							= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
            raise NotImplementedError('''prediction_type not implemented yet: sample'''    )
        else:
            raise ValueError(
                f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''    )

        SCREAMING_SNAKE_CASE__       							= self.sigmas[step_index]
        SCREAMING_SNAKE_CASE__       							= self.sigmas[step_index + 1]
        SCREAMING_SNAKE_CASE__       							= (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
        SCREAMING_SNAKE_CASE__       							= (sigma_to**2 - sigma_up**2) ** 0.5

        # 2. Convert to an ODE derivative
        SCREAMING_SNAKE_CASE__       							= (sample - pred_original_sample) / sigma

        SCREAMING_SNAKE_CASE__       							= sigma_down - sigma

        SCREAMING_SNAKE_CASE__       							= sample + derivative * dt

        SCREAMING_SNAKE_CASE__       							= model_output.device
        SCREAMING_SNAKE_CASE__       							= randn_tensor(model_output.shape      ,							dtype=model_output.dtype      ,							device=_lowerCamelCase      ,							generator=_lowerCamelCase    )

        SCREAMING_SNAKE_CASE__       							= prev_sample + noise * sigma_up

        if not return_dict:
            return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=_lowerCamelCase      ,							pred_original_sample=_lowerCamelCase    )



    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.sigmas.to(device=original_samples.device      ,							dtype=original_samples.dtype    )
        if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase    ):
            # mps does not support float64
            SCREAMING_SNAKE_CASE__       							= self.timesteps.to(original_samples.device      ,							dtype=torch.floataa    )
            SCREAMING_SNAKE_CASE__       							= timesteps.to(original_samples.device      ,							dtype=torch.floataa    )
        else:
            SCREAMING_SNAKE_CASE__       							= self.timesteps.to(original_samples.device    )
            SCREAMING_SNAKE_CASE__       							= timesteps.to(original_samples.device    )

        SCREAMING_SNAKE_CASE__       							= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

        SCREAMING_SNAKE_CASE__       							= sigmas[step_indices].flatten()
        while len(sigma.shape    ) < len(original_samples.shape    ):
            SCREAMING_SNAKE_CASE__       							= sigma.unsqueeze(-1    )

        SCREAMING_SNAKE_CASE__       							= original_samples + noise * sigma
        return noisy_samples




    def __len__(					self    ):





        '''simple docstring'''

        return self.config.num_train_timesteps







import functools
import operator

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


_A				:							Any							= logging.get_logger(__name__)

_A				:							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 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    lowerCamelCase__						:		int							=  """hubert"""



    def __init__(					self      ,							A_=32      ,							A_=7_68      ,							A_=12      ,							A_=12      ,							A_=30_72      ,							A_="gelu"      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=True      ,							A_=0.0      ,							A_=0.1      ,							A_=0.1      ,							A_=0.02      ,							A_=1E-5      ,							A_="group"      ,							A_="gelu"      ,							A_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)      ,							A_=(5, 2, 2, 2, 2, 2, 2)      ,							A_=(10, 3, 3, 3, 3, 2, 2)      ,							A_=False      ,							A_=1_28      ,							A_=16      ,							A_=False      ,							A_=True      ,							A_=0.05      ,							A_=10      ,							A_=2      ,							A_=0.0      ,							A_=10      ,							A_=0      ,							A_="sum"      ,							A_=False      ,							A_=False      ,							A_=2_56      ,							A_=0      ,							A_=1      ,							A_=2      ,							**A_      ,							):





        '''simple docstring'''

        super().__init__(**A_      ,							pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_    )
        SCREAMING_SNAKE_CASE__       							= hidden_size
        SCREAMING_SNAKE_CASE__       							= feat_extract_norm
        SCREAMING_SNAKE_CASE__       							= feat_extract_activation
        SCREAMING_SNAKE_CASE__       							= list(A_    )
        SCREAMING_SNAKE_CASE__       							= list(A_    )
        SCREAMING_SNAKE_CASE__       							= list(A_    )
        SCREAMING_SNAKE_CASE__       							= conv_bias
        SCREAMING_SNAKE_CASE__       							= num_conv_pos_embeddings
        SCREAMING_SNAKE_CASE__       							= num_conv_pos_embedding_groups
        SCREAMING_SNAKE_CASE__       							= len(self.conv_dim    )
        SCREAMING_SNAKE_CASE__       							= num_hidden_layers
        SCREAMING_SNAKE_CASE__       							= intermediate_size
        SCREAMING_SNAKE_CASE__       							= hidden_act
        SCREAMING_SNAKE_CASE__       							= num_attention_heads
        SCREAMING_SNAKE_CASE__       							= hidden_dropout
        SCREAMING_SNAKE_CASE__       							= attention_dropout
        SCREAMING_SNAKE_CASE__       							= activation_dropout
        SCREAMING_SNAKE_CASE__       							= feat_proj_layer_norm
        SCREAMING_SNAKE_CASE__       							= feat_proj_dropout
        SCREAMING_SNAKE_CASE__       							= final_dropout
        SCREAMING_SNAKE_CASE__       							= layerdrop
        SCREAMING_SNAKE_CASE__       							= layer_norm_eps
        SCREAMING_SNAKE_CASE__       							= initializer_range
        SCREAMING_SNAKE_CASE__       							= vocab_size
        SCREAMING_SNAKE_CASE__       							= do_stable_layer_norm
        SCREAMING_SNAKE_CASE__       							= use_weighted_layer_sum
        SCREAMING_SNAKE_CASE__       							= 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
        SCREAMING_SNAKE_CASE__       							= apply_spec_augment
        SCREAMING_SNAKE_CASE__       							= mask_time_prob
        SCREAMING_SNAKE_CASE__       							= mask_time_length
        SCREAMING_SNAKE_CASE__       							= mask_time_min_masks
        SCREAMING_SNAKE_CASE__       							= mask_feature_prob
        SCREAMING_SNAKE_CASE__       							= mask_feature_length
        SCREAMING_SNAKE_CASE__       							= mask_feature_min_masks

        # ctc loss
        SCREAMING_SNAKE_CASE__       							= ctc_loss_reduction
        SCREAMING_SNAKE_CASE__       							= ctc_zero_infinity




    @property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        return functools.reduce(operator.mul      ,							self.conv_stride      ,							1    )
