
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


lowercase        =  8



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      List[str]=BITS       )  ->    List[str]:
							lowerCamelCase_							  =					x.device

							lowerCamelCase_							  =					(x * 2_5_5).int().clamp(0			,				2_5_5       )

							lowerCamelCase_							  =					2 ** torch.arange(bits - 1			,				-1			,				-1			,				device=UpperCAmelCase__       )
							lowerCamelCase_							  =					rearrange(UpperCAmelCase__			,				"""d -> d 1 1"""       )
							lowerCamelCase_							  =					rearrange(UpperCAmelCase__			,				"""b c h w -> b c 1 h w"""       )

							lowerCamelCase_							  =					((x & mask) != 0).float()
							lowerCamelCase_							  =					rearrange(UpperCAmelCase__			,				"""b c d h w -> b (c d) h w"""       )
							lowerCamelCase_							  =					bits * 2 - 1
							return bits



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      Dict=BITS       )  ->    Union[str, Any]:
							lowerCamelCase_							  =					x.device

							lowerCamelCase_							  =					(x > 0).int()
							lowerCamelCase_							  =					2 ** torch.arange(bits - 1			,				-1			,				-1			,				device=UpperCAmelCase__			,				dtype=torch.intaa       )

							lowerCamelCase_							  =					rearrange(UpperCAmelCase__			,				"""d -> d 1 1"""       )
							lowerCamelCase_							  =					rearrange(UpperCAmelCase__			,				"""b (c d) h w -> b c d h w"""			,				d=8       )
							lowerCamelCase_							  =					reduce(x * mask			,				"""b c d h w -> b c h w"""			,				"""sum"""       )
							return (dec / 2_5_5).clamp(0.0			,				1.0       )



def      __lowerCAmelCase						(							self				:      Any			,				UpperCAmelCase__				:      torch.FloatTensor			,				UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      torch.FloatTensor			,				UpperCAmelCase__				:      float = 0.0			,				UpperCAmelCase__				:      bool = True			,				UpperCAmelCase__				:      Any=None			,				UpperCAmelCase__				:      bool = 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)
							lowerCamelCase_							  =					timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							lowerCamelCase_							  =					self.alphas_cumprod[timestep]
							lowerCamelCase_							  =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

							lowerCamelCase_							  =					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
							lowerCamelCase_							  =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

							# 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)
							lowerCamelCase_							  =					self._get_variance(UpperCAmelCase__			,				UpperCAmelCase__       )
							lowerCamelCase_							  =					eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														lowerCamelCase_							  =					(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
							lowerCamelCase_							  =					(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
							lowerCamelCase_							  =					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
														lowerCamelCase_							  =					model_output.device if torch.is_tensor(UpperCAmelCase__       ) else """cpu"""
														lowerCamelCase_							  =					torch.randn(model_output.shape			,				dtype=model_output.dtype			,				generator=UpperCAmelCase__       ).to(UpperCAmelCase__       )
														lowerCamelCase_							  =					self._get_variance(UpperCAmelCase__			,				UpperCAmelCase__       ) ** 0.5 * eta * noise

														lowerCamelCase_							  =					prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=UpperCAmelCase__			,				pred_original_sample=UpperCAmelCase__       )



def      __lowerCAmelCase						(							self				:      Optional[Any]			,				UpperCAmelCase__				:      torch.FloatTensor			,				UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      torch.FloatTensor			,				UpperCAmelCase__				:      int="epsilon"			,				UpperCAmelCase__				:      Any=None			,				UpperCAmelCase__				:      bool = True			,				)  ->    Union[DDPMSchedulerOutput, Tuple]:
							lowerCamelCase_							  =					timestep

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

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

							# 3. Clip "predicted x_0"
							lowerCamelCase_							  =					self.bit_scale
							if self.config.clip_sample:
														lowerCamelCase_							  =					torch.clamp(UpperCAmelCase__			,				-scale			,				UpperCAmelCase__       )

							# 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_							  =					(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							lowerCamelCase_							  =					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
							lowerCamelCase_							  =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							lowerCamelCase_							  =					0
							if t > 0:
														lowerCamelCase_							  =					torch.randn(
														    model_output.size()			,				dtype=model_output.dtype			,				layout=model_output.layout			,				generator=UpperCAmelCase__       ).to(model_output.device       )
														lowerCamelCase_							  =					(self._get_variance(UpperCAmelCase__			,				predicted_variance=UpperCAmelCase__       ) ** 0.5) * noise

							lowerCamelCase_							  =					pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=UpperCAmelCase__			,				pred_original_sample=UpperCAmelCase__       )





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     int   ,					__UpperCamelCase					:     UNetaDConditionModel   ,					__UpperCamelCase					:     Union[DDIMScheduler, DDPMScheduler]   ,					__UpperCamelCase					:     Optional[float] = 1.0   ,					):
												super().__init__()
												lowerCamelCase_							  =					bit_scale
												lowerCamelCase_							  =					(
												    ddim_bit_scheduler_step if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else ddpm_bit_scheduler_step
												)

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



					@torch.no_grad()
					def __call__(	self					:     Any   ,					__UpperCamelCase					:     Optional[int] = 2_5_6   ,					__UpperCamelCase					:     Optional[int] = 2_5_6   ,					__UpperCamelCase					:     Optional[int] = 5_0   ,					__UpperCamelCase					:     Optional[torch.Generator] = None   ,					__UpperCamelCase					:     Optional[int] = 1   ,					__UpperCamelCase					:     Optional[str] = "pil"   ,					__UpperCamelCase					:     bool = True   ,					**__UpperCamelCase					:     str   ,					):
												lowerCamelCase_							  =					torch.randn(
												    (batch_size, self.unet.config.in_channels, height, width)   ,					generator=__UpperCamelCase   ,					)
												lowerCamelCase_							  =					decimal_to_bits(__UpperCamelCase   ) * self.bit_scale
												lowerCamelCase_							  =					latents.to(self.device   )

												self.scheduler.set_timesteps(__UpperCamelCase   )

												for t in self.progress_bar(self.scheduler.timesteps   ):
																			# predict the noise residual
																			lowerCamelCase_							  =					self.unet(__UpperCamelCase   ,					__UpperCamelCase   ).sample

																			# compute the previous noisy sample x_t -> x_t-1
																			lowerCamelCase_							  =					self.scheduler.step(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ).prev_sample

												lowerCamelCase_							  =					bits_to_decimal(__UpperCamelCase   )

												if output_type == "pil":
																			lowerCamelCase_							  =					self.numpy_to_pil(__UpperCamelCase   )

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase   )







import functools
import operator

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


lowercase        =  logging.get_logger(__name__)

lowercase        =  {
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    '''wav2vec2'''

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     Optional[int]=3_2   ,					__UpperCamelCase					:     List[str]=7_6_8   ,					__UpperCamelCase					:     Tuple=1_2   ,					__UpperCamelCase					:     str=1_2   ,					__UpperCamelCase					:     Any=3_0_7_2   ,					__UpperCamelCase					:     Tuple="gelu"   ,					__UpperCamelCase					:     Tuple=0.1   ,					__UpperCamelCase					:     Any=0.1   ,					__UpperCamelCase					:     List[str]=0.1   ,					__UpperCamelCase					:     List[Any]=0.0   ,					__UpperCamelCase					:     str=0.0   ,					__UpperCamelCase					:     str=0.1   ,					__UpperCamelCase					:     List[Any]=0.1   ,					__UpperCamelCase					:     Dict=0.02   ,					__UpperCamelCase					:     int=1E-5   ,					__UpperCamelCase					:     Dict="group"   ,					__UpperCamelCase					:     Tuple="gelu"   ,					__UpperCamelCase					:     Optional[int]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)   ,					__UpperCamelCase					:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)   ,					__UpperCamelCase					:     int=(1_0, 3, 3, 3, 3, 2, 2)   ,					__UpperCamelCase					:     List[Any]=False   ,					__UpperCamelCase					:     Tuple=1_2_8   ,					__UpperCamelCase					:     Any=1_6   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     Dict=0.05   ,					__UpperCamelCase					:     Union[str, Any]=1_0   ,					__UpperCamelCase					:     int=2   ,					__UpperCamelCase					:     List[Any]=0.0   ,					__UpperCamelCase					:     List[Any]=1_0   ,					__UpperCamelCase					:     List[str]=0   ,					__UpperCamelCase					:     Optional[Any]=3_2_0   ,					__UpperCamelCase					:     Optional[int]=2   ,					__UpperCamelCase					:     List[Any]=0.1   ,					__UpperCamelCase					:     int=1_0_0   ,					__UpperCamelCase					:     int=2_5_6   ,					__UpperCamelCase					:     str=2_5_6   ,					__UpperCamelCase					:     Dict=0.1   ,					__UpperCamelCase					:     Union[str, Any]="sum"   ,					__UpperCamelCase					:     Optional[int]=False   ,					__UpperCamelCase					:     Optional[int]=False   ,					__UpperCamelCase					:     Dict=2_5_6   ,					__UpperCamelCase					:     List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)   ,					__UpperCamelCase					:     int=(5, 3, 3, 1, 1)   ,					__UpperCamelCase					:     Optional[Any]=(1, 2, 3, 1, 1)   ,					__UpperCamelCase					:     Dict=5_1_2   ,					__UpperCamelCase					:     Any=0   ,					__UpperCamelCase					:     Union[str, Any]=1   ,					__UpperCamelCase					:     List[str]=2   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     int=3   ,					__UpperCamelCase					:     Dict=2   ,					__UpperCamelCase					:     Optional[Any]=3   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     Tuple=None   ,					**__UpperCamelCase					:     Tuple   ,					):
												super().__init__(**__UpperCamelCase   ,					pad_token_id=__UpperCamelCase   ,					bos_token_id=__UpperCamelCase   ,					eos_token_id=__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_size
												lowerCamelCase_							  =					feat_extract_norm
												lowerCamelCase_							  =					feat_extract_activation
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					conv_bias
												lowerCamelCase_							  =					num_conv_pos_embeddings
												lowerCamelCase_							  =					num_conv_pos_embedding_groups
												lowerCamelCase_							  =					len(self.conv_dim   )
												lowerCamelCase_							  =					num_hidden_layers
												lowerCamelCase_							  =					intermediate_size
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					hidden_dropout
												lowerCamelCase_							  =					attention_dropout
												lowerCamelCase_							  =					activation_dropout
												lowerCamelCase_							  =					feat_proj_dropout
												lowerCamelCase_							  =					final_dropout
												lowerCamelCase_							  =					layerdrop
												lowerCamelCase_							  =					layer_norm_eps
												lowerCamelCase_							  =					initializer_range
												lowerCamelCase_							  =					vocab_size
												lowerCamelCase_							  =					do_stable_layer_norm
												lowerCamelCase_							  =					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_							  =					apply_spec_augment
												lowerCamelCase_							  =					mask_time_prob
												lowerCamelCase_							  =					mask_time_length
												lowerCamelCase_							  =					mask_time_min_masks
												lowerCamelCase_							  =					mask_feature_prob
												lowerCamelCase_							  =					mask_feature_length
												lowerCamelCase_							  =					mask_feature_min_masks

												# parameters for pretraining with codevector quantized representations
												lowerCamelCase_							  =					num_codevectors_per_group
												lowerCamelCase_							  =					num_codevector_groups
												lowerCamelCase_							  =					contrastive_logits_temperature
												lowerCamelCase_							  =					feat_quantizer_dropout
												lowerCamelCase_							  =					num_negatives
												lowerCamelCase_							  =					codevector_dim
												lowerCamelCase_							  =					proj_codevector_dim
												lowerCamelCase_							  =					diversity_loss_weight

												# ctc loss
												lowerCamelCase_							  =					ctc_loss_reduction
												lowerCamelCase_							  =					ctc_zero_infinity

												# adapter
												lowerCamelCase_							  =					add_adapter
												lowerCamelCase_							  =					adapter_kernel_size
												lowerCamelCase_							  =					adapter_stride
												lowerCamelCase_							  =					num_adapter_layers
												lowerCamelCase_							  =					output_hidden_size or hidden_size
												lowerCamelCase_							  =					adapter_attn_dim

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

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



					@property
					def    lowercase__      (	self					:     Dict   ):
												return functools.reduce(operator.mul   ,					self.conv_stride   ,					1   )





