

import functools
import operator

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


lowerCAmelCase							:						Dict     			=		logging.get_logger(__name__)

lowerCAmelCase							:						int     			=		{
    """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 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							int    							=  '''hubert'''



			def __init__(				self						:  Any     ,		lowerCAmelCase__						:  int=32     ,		lowerCAmelCase__						:  Union[str, Any]=768     ,		lowerCAmelCase__						:  List[str]=12     ,		lowerCAmelCase__						:  int=12     ,		lowerCAmelCase__						:  List[Any]=3072     ,		lowerCAmelCase__						:  List[Any]="gelu"     ,		lowerCAmelCase__						:  Optional[int]=0.1     ,		lowerCAmelCase__						:  List[str]=0.1     ,		lowerCAmelCase__						:  List[str]=0.1     ,		lowerCAmelCase__						:  str=True     ,		lowerCAmelCase__						:  List[Any]=0.0     ,		lowerCAmelCase__						:  Optional[int]=0.1     ,		lowerCAmelCase__						:  Any=0.1     ,		lowerCAmelCase__						:  Dict=0.02     ,		lowerCAmelCase__						:  Tuple=1E-5     ,		lowerCAmelCase__						:  List[Any]="group"     ,		lowerCAmelCase__						:  str="gelu"     ,		lowerCAmelCase__						:  List[str]=(512, 512, 512, 512, 512, 512, 512)     ,		lowerCAmelCase__						:  Optional[int]=(5, 2, 2, 2, 2, 2, 2)     ,		lowerCAmelCase__						:  str=(10, 3, 3, 3, 3, 2, 2)     ,		lowerCAmelCase__						:  int=False     ,		lowerCAmelCase__						:  List[Any]=128     ,		lowerCAmelCase__						:  Optional[Any]=16     ,		lowerCAmelCase__						:  List[str]=False     ,		lowerCAmelCase__						:  List[str]=True     ,		lowerCAmelCase__						:  Any=0.05     ,		lowerCAmelCase__						:  int=10     ,		lowerCAmelCase__						:  Dict=2     ,		lowerCAmelCase__						:  Optional[Any]=0.0     ,		lowerCAmelCase__						:  Union[str, Any]=10     ,		lowerCAmelCase__						:  Any=0     ,		lowerCAmelCase__						:  str="sum"     ,		lowerCAmelCase__						:  Dict=False     ,		lowerCAmelCase__						:  Tuple=False     ,		lowerCAmelCase__						:  Any=256     ,		lowerCAmelCase__						:  Any=0     ,		lowerCAmelCase__						:  List[Any]=1     ,		lowerCAmelCase__						:  List[Any]=2     ,		**lowerCAmelCase__						:  Optional[int]     ,		):
									super().__init__(**lowerCAmelCase__     ,		pad_token_id=lowerCAmelCase__     ,		bos_token_id=lowerCAmelCase__     ,		eos_token_id=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Dict							= hidden_size
									SCREAMING_SNAKE_CASE_:				Dict							= feat_extract_norm
									SCREAMING_SNAKE_CASE_:				Any							= feat_extract_activation
									SCREAMING_SNAKE_CASE_:				Optional[int]							= list(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				List[str]							= list(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Optional[int]							= list(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				int							= conv_bias
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= num_conv_pos_embeddings
									SCREAMING_SNAKE_CASE_:				Any							= num_conv_pos_embedding_groups
									SCREAMING_SNAKE_CASE_:				str							= len(self.conv_dim)
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= num_hidden_layers
									SCREAMING_SNAKE_CASE_:				Tuple							= intermediate_size
									SCREAMING_SNAKE_CASE_:				str							= hidden_act
									SCREAMING_SNAKE_CASE_:				List[Any]							= num_attention_heads
									SCREAMING_SNAKE_CASE_:				Optional[int]							= hidden_dropout
									SCREAMING_SNAKE_CASE_:				int							= attention_dropout
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= activation_dropout
									SCREAMING_SNAKE_CASE_:				List[Any]							= feat_proj_layer_norm
									SCREAMING_SNAKE_CASE_:				str							= feat_proj_dropout
									SCREAMING_SNAKE_CASE_:				Dict							= final_dropout
									SCREAMING_SNAKE_CASE_:				Dict							= layerdrop
									SCREAMING_SNAKE_CASE_:				Tuple							= layer_norm_eps
									SCREAMING_SNAKE_CASE_:				Dict							= initializer_range
									SCREAMING_SNAKE_CASE_:				Dict							= vocab_size
									SCREAMING_SNAKE_CASE_:				str							= do_stable_layer_norm
									SCREAMING_SNAKE_CASE_:				List[str]							= use_weighted_layer_sum
									SCREAMING_SNAKE_CASE_:				Tuple							= 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_:				int							= apply_spec_augment
									SCREAMING_SNAKE_CASE_:				Optional[int]							= mask_time_prob
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= mask_time_length
									SCREAMING_SNAKE_CASE_:				List[str]							= mask_time_min_masks
									SCREAMING_SNAKE_CASE_:				List[str]							= mask_feature_prob
									SCREAMING_SNAKE_CASE_:				Dict							= mask_feature_length
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= mask_feature_min_masks

									# ctc loss
									SCREAMING_SNAKE_CASE_:				Optional[int]							= ctc_loss_reduction
									SCREAMING_SNAKE_CASE_:				List[Any]							= ctc_zero_infinity




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









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							UNetaDModel
			_UpperCAmelCase     :							PNDMScheduler



			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  UNetaDModel     ,		lowerCAmelCase__						:  PNDMScheduler):
									super().__init__()

									SCREAMING_SNAKE_CASE_:				Dict							= PNDMScheduler.from_config(scheduler.config)

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




			@torch.no_grad()
			def __call__(				self						:  int     ,		lowerCAmelCase__						:  int = 1     ,		lowerCAmelCase__						:  int = 50     ,		lowerCAmelCase__						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		lowerCAmelCase__						:  Optional[str] = "pil"     ,		lowerCAmelCase__						:  bool = True     ,		**lowerCAmelCase__						:  str     ,		):
									SCREAMING_SNAKE_CASE_:				Dict							= randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,		generator=lowerCAmelCase__     ,		device=self.device     ,		)

									self.scheduler.set_timesteps(lowerCAmelCase__)
									for t in self.progress_bar(self.scheduler.timesteps):
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.unet(lowerCAmelCase__     ,		lowerCAmelCase__).sample

															SCREAMING_SNAKE_CASE_:				Any							= self.scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__).prev_sample

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= (image / 2 + 0.5).clamp(0     ,		1)
									SCREAMING_SNAKE_CASE_:				Dict							= image.cpu().permute(0     ,		2     ,		3     ,		1).numpy()
									if output_type == "pil":
															SCREAMING_SNAKE_CASE_:				Any							= self.numpy_to_pil(lowerCAmelCase__)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)






