


from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


SCREAMING_SNAKE_CASE			: Optional[int]				   =					8



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=BITS					)       ->		List[Any]:
    _lowercase			:				List[str] 						=						x.device

    _lowercase			:				Union[str, Any] 						=						(x * 255).int().clamp(0				,					255					)

    _lowercase			:				List[str] 						=						2 ** torch.arange(bits - 1				,					-1				,					-1				,					device=lowerCamelCase_					)
    _lowercase			:				Union[str, Any] 						=						rearrange(lowerCamelCase_				,					'd -> d 1 1'					)
    _lowercase			:				Union[str, Any] 						=						rearrange(lowerCamelCase_				,					'b c h w -> b c 1 h w'					)

    _lowercase			:				Union[str, Any] 						=						((x & mask) != 0).float()
    _lowercase			:				Dict 						=						rearrange(lowerCamelCase_				,					'b c d h w -> b (c d) h w'					)
    _lowercase			:				Tuple 						=						bits * 2 - 1
    return bits



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=BITS					)       ->		Dict:
    _lowercase			:				List[Any] 						=						x.device

    _lowercase			:				List[Any] 						=						(x > 0).int()
    _lowercase			:				Tuple 						=						2 ** torch.arange(bits - 1				,					-1				,					-1				,					device=lowerCamelCase_				,					dtype=torch.intaa					)

    _lowercase			:				List[str] 						=						rearrange(lowerCamelCase_				,					'd -> d 1 1'					)
    _lowercase			:				Tuple 						=						rearrange(lowerCamelCase_				,					'b (c d) h w -> b c d h w'				,					d=8					)
    _lowercase			:				Optional[int] 						=						reduce(x * mask				,					'b c d h w -> b c h w'				,					'sum'					)
    return (dec / 255).clamp(0.0				,					1.0					)



def 							UpperCamelCase_( self				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_ = 0.0				,					lowerCamelCase_ = True				,					lowerCamelCase_=None				,					lowerCamelCase_ = True				,					)       ->		Union[DDIMSchedulerOutput, Tuple]:
    if self.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)
    _lowercase			:				Optional[int] 						=						timestep - self.config.num_train_timesteps // self.num_inference_steps

    # 2. compute alphas, betas
    _lowercase			:				int 						=						self.alphas_cumprod[timestep]
    _lowercase			:				Optional[Any] 						=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

    _lowercase			:				Optional[int] 						=						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
    _lowercase			:				List[Any] 						=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

    # 4. Clip "predicted x_0"
    _lowercase			:				Any 						=						self.bit_scale
    if self.config.clip_sample:
        _lowercase			:				str 						=						torch.clamp(lowerCamelCase_				,					-scale				,					lowerCamelCase_					)

    # 5. 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)
    _lowercase			:				Dict 						=						self._get_variance(lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				Union[str, Any] 						=						eta * variance ** 0.5

    if use_clipped_model_output:
        # the model_output is always re-derived from the clipped x_0 in Glide
        _lowercase			:				str 						=						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

    # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    _lowercase			:				Tuple 						=						(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

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

    if eta > 0:
        # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
        _lowercase			:				List[str] 						=						model_output.device if torch.is_tensor(lowerCamelCase_					) else 'cpu'
        _lowercase			:				List[Any] 						=						torch.randn(model_output.shape				,					dtype=model_output.dtype				,					generator=lowerCamelCase_					).to(lowerCamelCase_					)
        _lowercase			:				Union[str, Any] 						=						self._get_variance(lowerCamelCase_				,					lowerCamelCase_					) ** 0.5 * eta * noise

        _lowercase			:				Optional[Any] 						=						prev_sample + variance

    if not return_dict:
        return (prev_sample,)

    return DDIMSchedulerOutput(prev_sample=lowerCamelCase_				,					pred_original_sample=lowerCamelCase_					)



def 							UpperCamelCase_( self				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_="epsilon"				,					lowerCamelCase_=None				,					lowerCamelCase_ = True				,					)       ->		Union[DDPMSchedulerOutput, Tuple]:
    _lowercase			:				Union[str, Any] 						=						timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
        _lowercase						,							_lowercase			:				Union[str, Any] 						=						torch.split(lowerCamelCase_				,					sample.shape[1]				,					dim=1					)
    else:
        _lowercase			:				Optional[int] 						=						None

    # 1. compute alphas, betas
    _lowercase			:				Union[str, Any] 						=						self.alphas_cumprod[t]
    _lowercase			:				Dict 						=						self.alphas_cumprod[t - 1] if t > 0 else self.one
    _lowercase			:				Tuple 						=						1 - alpha_prod_t
    _lowercase			:				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 prediction_type == "epsilon":
        _lowercase			:				List[Any] 						=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif prediction_type == "sample":
        _lowercase			:				str 						=						model_output
    else:
        raise ValueError(F'''Unsupported prediction_type {prediction_type}.'''					)

    # 3. Clip "predicted x_0"
    _lowercase			:				Union[str, Any] 						=						self.bit_scale
    if self.config.clip_sample:
        _lowercase			:				List[str] 						=						torch.clamp(lowerCamelCase_				,					-scale				,					lowerCamelCase_					)

    # 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
    _lowercase			:				List[Any] 						=						(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
    _lowercase			:				Optional[Any] 						=						self.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
    _lowercase			:				int 						=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    _lowercase			:				int 						=						0
    if t > 0:
        _lowercase			:				Union[str, Any] 						=						torch.randn(
            model_output.size()				,					dtype=model_output.dtype				,					layout=model_output.layout				,					generator=lowerCamelCase_					).to(model_output.device					)
        _lowercase			:				Tuple 						=						(self._get_variance(lowerCamelCase_				,					predicted_variance=lowerCamelCase_					) ** 0.5) * noise

    _lowercase			:				List[Any] 						=						pred_prev_sample + variance

    if not return_dict:
        return (pred_prev_sample,)

    return DDPMSchedulerOutput(prev_sample=lowerCamelCase_				,					pred_original_sample=lowerCamelCase_					)






class _lowerCamelCase(		_a  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = 1.0, )			->  Union[str, Any]:




           """simple docstring"""

           super().__init__()
           _lowercase			:				str 						=						bit_scale
           _lowercase			:				Dict 						=						(
               ddim_bit_scheduler_step if isinstance(lowerCamelCase, lowerCamelCase) else ddpm_bit_scheduler_step
           )

           self.register_modules(unet=lowerCamelCase, scheduler=lowerCamelCase)




       @torch.no_grad()
       def __call__(   self, lowerCamelCase = 2_56, lowerCamelCase = 2_56, lowerCamelCase = 50, lowerCamelCase = None, lowerCamelCase = 1, lowerCamelCase = "pil", lowerCamelCase = True, **lowerCamelCase, )			->  Union[Tuple, ImagePipelineOutput]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						torch.randn(
               (batch_size, self.unet.config.in_channels, height, width), generator=lowerCamelCase, )
           _lowercase			:				Optional[int] 						=						decimal_to_bits(lowerCamelCase) * self.bit_scale
           _lowercase			:				Tuple 						=						latents.to(self.device)

           self.scheduler.set_timesteps(lowerCamelCase)

           for t in self.progress_bar(self.scheduler.timesteps):
               # predict the noise residual
               _lowercase			:				List[str] 						=						self.unet(lowerCamelCase, lowerCamelCase).sample

               # compute the previous noisy sample x_t -> x_t-1
               _lowercase			:				Tuple 						=						self.scheduler.step(lowerCamelCase, lowerCamelCase, lowerCamelCase).prev_sample

           _lowercase			:				Optional[Any] 						=						bits_to_decimal(lowerCamelCase)

           if output_type == "pil":
               _lowercase			:				Any 						=						self.numpy_to_pil(lowerCamelCase)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=lowerCamelCase)







"""simple docstring"""







import math

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


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					List[str]							  =     "data2vec-audio"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 512, 512, 512)						,	SCREAMING_SNAKE_CASE__=(5, 2, 2, 2, 2, 2, 2)						,	SCREAMING_SNAKE_CASE__=(10, 3, 3, 3, 3, 2, 2)						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=19						,	SCREAMING_SNAKE_CASE__=5						,	SCREAMING_SNAKE_CASE__=0.0_5						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__="sum"						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 1500)						,	SCREAMING_SNAKE_CASE__=(5, 3, 3, 1, 1)						,	SCREAMING_SNAKE_CASE__=(1, 2, 3, 1, 1)						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Dict:
				super().__init__(**SCREAMING_SNAKE_CASE__						,	pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__		)
				A__											=					hidden_size
				A__											=					feat_extract_activation
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					conv_bias
				A__											=					num_conv_pos_embeddings
				A__											=					num_conv_pos_embedding_groups
				A__											=					conv_pos_kernel_size
				A__											=					len(self.conv_dim		)
				A__											=					num_hidden_layers
				A__											=					intermediate_size
				A__											=					hidden_act
				A__											=					num_attention_heads
				A__											=					hidden_dropout
				A__											=					attention_dropout
				A__											=					activation_dropout
				A__											=					feat_proj_dropout
				A__											=					final_dropout
				A__											=					layerdrop
				A__											=					layer_norm_eps
				A__											=					initializer_range
				A__											=					vocab_size
				A__											=					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
				A__											=					mask_time_prob
				A__											=					mask_time_length
				A__											=					mask_time_min_masks
				A__											=					mask_feature_prob
				A__											=					mask_feature_length
				A__											=					mask_feature_min_masks

				# ctc loss
				A__											=					ctc_loss_reduction
				A__											=					ctc_zero_infinity

				# adapter
				A__											=					add_adapter
				A__											=					adapter_kernel_size
				A__											=					adapter_stride
				A__											=					num_adapter_layers
				A__											=					output_hidden_size or hidden_size

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				A__											=					classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					xvector_output_dim







			@property
			def   snake_case__	(      self		)      ->	List[str]:
				return math.prod(self.conv_stride		)




