



"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase : List[str]      						=						logging.get_logger(__name__)

_lowercase : Dict      						=						{
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							'convbert'



					def __init__(       self							:  Any,  lowerCamelCase							:  List[str]=3_0522,  lowerCamelCase							:  Tuple=768,  lowerCamelCase							:  str=12,  lowerCamelCase							:  Any=12,  lowerCamelCase							:  str=3072,  lowerCamelCase							:  List[str]="gelu",  lowerCamelCase							:  List[str]=0.1,  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  List[Any]=512,  lowerCamelCase							:  Optional[int]=2,  lowerCamelCase							:  str=0.02,  lowerCamelCase							:  int=1E-12,  lowerCamelCase							:  Tuple=1,  lowerCamelCase							:  Tuple=0,  lowerCamelCase							:  Optional[int]=2,  lowerCamelCase							:  Optional[Any]=768,  lowerCamelCase							:  int=2,  lowerCamelCase							:  str=9,  lowerCamelCase							:  int=1,  lowerCamelCase							:  Union[str, Any]=None,  **lowerCamelCase							:  str,  )->					Any:
								super().__init__(
								    pad_token_id=lowerCamelCase,  bos_token_id=lowerCamelCase,  eos_token_id=lowerCamelCase,  **lowerCamelCase,  )

								lowerCamelCase__		:		Any			=vocab_size
								lowerCamelCase__		:		List[Any]			=hidden_size
								lowerCamelCase__		:		Optional[int]			=num_hidden_layers
								lowerCamelCase__		:		Optional[Any]			=num_attention_heads
								lowerCamelCase__		:		List[str]			=intermediate_size
								lowerCamelCase__		:		List[Any]			=hidden_act
								lowerCamelCase__		:		List[Any]			=hidden_dropout_prob
								lowerCamelCase__		:		Dict			=attention_probs_dropout_prob
								lowerCamelCase__		:		Optional[int]			=max_position_embeddings
								lowerCamelCase__		:		Tuple			=type_vocab_size
								lowerCamelCase__		:		Optional[int]			=initializer_range
								lowerCamelCase__		:		Any			=layer_norm_eps
								lowerCamelCase__		:		Any			=embedding_size
								lowerCamelCase__		:		Optional[int]			=head_ratio
								lowerCamelCase__		:		List[str]			=conv_kernel_size
								lowerCamelCase__		:		Optional[Any]			=num_groups
								lowerCamelCase__		:		Optional[int]			=classifier_dropout





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					@property
					def 		snake_case       (       self							:  str     )->					Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
											lowerCamelCase__		:		List[Any]			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
											lowerCamelCase__		:		Dict			={0: '''batch''', 1: '''sequence'''}
								return OrderedDict(
								    [
								        ('''input_ids''', dynamic_axis),
								        ('''attention_mask''', dynamic_axis),
								        ('''token_type_ids''', dynamic_axis),
								    ]     )









"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_lowercase : int      						=						logging.get_logger(__name__)  # pylint: disable=invalid-name

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict     )->					Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase     )




					@torch.no_grad()
					def __call__(       self							:  Optional[int],  lowerCamelCase							:  int = 1,  lowerCamelCase							:  int = 100,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  bool = True,  )->					Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
											lowerCamelCase__		:		int			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase__		:		List[str]			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase__		:		Any			=2 ** len(self.unet.up_blocks     )
								if sample_size < 3 * down_scale_factor:
											raise ValueError(
											    F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
											    F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''     )

								lowerCamelCase__		:		Optional[int]			=int(lowerCamelCase     )
								if sample_size % down_scale_factor != 0:
											lowerCamelCase__		:		Tuple			=(
											    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
											) * down_scale_factor
											logger.info(
											    F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
											    F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
											    ''' process.'''     )
								lowerCamelCase__		:		int			=int(lowerCamelCase     )

								lowerCamelCase__		:		str			=next(iter(self.unet.parameters()     )     ).dtype
								lowerCamelCase__		:		Union[str, Any]			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(lowerCamelCase,  lowerCamelCase     ) and len(lowerCamelCase     ) != batch_size:
											raise ValueError(
											    F'''You have passed a list of generators of length {len(lowerCamelCase     )}, but requested an effective batch'''
											    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

								lowerCamelCase__		:		str			=randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=self.device,  dtype=lowerCamelCase     )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase,  device=audio.device     )
								lowerCamelCase__		:		Any			=self.scheduler.timesteps.to(lowerCamelCase     )

								for t in self.progress_bar(self.scheduler.timesteps     ):
											# 1. predict noise model_output
											lowerCamelCase__		:		str			=self.unet(lowerCamelCase,  lowerCamelCase     ).sample

											# 2. compute previous image: x_t -> t_t-1
											lowerCamelCase__		:		Dict			=self.scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     ).prev_sample

								lowerCamelCase__		:		Optional[int]			=audio.clamp(-1,  1     ).float().cpu().numpy()

								lowerCamelCase__		:		List[Any]			=audio[:, :, :original_sample_size]

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=lowerCamelCase     )




