"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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


lowerCAmelCase__  :  Optional[Any]						     =	logging.get_logger(__name__)  # pylint: disable=invalid-name
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def __init__(				self   :	Any    ,lowerCamelCase__   :	int    ,lowerCamelCase__   :	List[str]			):
									super().__init__()
									self.register_modules(unet=lowerCamelCase__    ,scheduler=lowerCamelCase__			)







					@torch.no_grad()
					def __call__(				self   :	Any    ,lowerCamelCase__   :	int = 1    ,lowerCamelCase__   :	int = 100    ,lowerCamelCase__   :	Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,lowerCamelCase__   :	Optional[float] = None    ,lowerCamelCase__   :	bool = True    ,):

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

									UpperCAmelCase__             =							audio_length_in_s * self.unet.config.sample_rate

									UpperCAmelCase__             =							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}.'''			)

									UpperCAmelCase__             =							int(lowerCamelCase__			)
									if sample_size % down_scale_factor != 0:
													UpperCAmelCase__             =							(
													    (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.'			)
									UpperCAmelCase__             =							int(lowerCamelCase__			)

									UpperCAmelCase__             =							next(iter(self.unet.parameters()			)			).dtype
									UpperCAmelCase__             =							(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.'''			)

									UpperCAmelCase__             =							randn_tensor(lowerCamelCase__    ,generator=lowerCamelCase__    ,device=self.device    ,dtype=lowerCamelCase__			)

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

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

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

									UpperCAmelCase__             =							audio.clamp(-1    ,1			).float().cpu().numpy()

									UpperCAmelCase__             =							audio[:, :, :original_sample_size]

									if not return_dict:
													return (audio,)

									return AudioPipelineOutput(audios=lowerCamelCase__			)




"""simple docstring"""






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


lowerCAmelCase__  :  int						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  str						     =	{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "xglm"
					snake_case__				    = ["past_key_values"]

					snake_case__				    = {
					    "num_attention_heads": "attention_heads",
					    "hidden_size": "d_model",
					    "num_hidden_layers": "num_layers",
					}







					def __init__(				self   :	Any    ,lowerCamelCase__   :	Any=256_008    ,lowerCamelCase__   :	Optional[Any]=2_048    ,lowerCamelCase__   :	List[str]=1_024    ,lowerCamelCase__   :	List[str]=4_096    ,lowerCamelCase__   :	Tuple=24    ,lowerCamelCase__   :	Optional[int]=16    ,lowerCamelCase__   :	int="gelu"    ,lowerCamelCase__   :	Dict=0.1    ,lowerCamelCase__   :	int=0.1    ,lowerCamelCase__   :	List[Any]=0.0    ,lowerCamelCase__   :	List[str]=0.0    ,lowerCamelCase__   :	Optional[Any]=0.0_2    ,lowerCamelCase__   :	List[str]=True    ,lowerCamelCase__   :	Optional[Any]=True    ,lowerCamelCase__   :	str=2    ,lowerCamelCase__   :	Dict=1    ,lowerCamelCase__   :	Optional[int]=0    ,lowerCamelCase__   :	Tuple=2    ,**lowerCamelCase__   :	List[Any]    ,):
									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							max_position_embeddings
									UpperCAmelCase__             =							d_model
									UpperCAmelCase__             =							ffn_dim
									UpperCAmelCase__             =							num_layers
									UpperCAmelCase__             =							attention_heads
									UpperCAmelCase__             =							activation_function
									UpperCAmelCase__             =							dropout
									UpperCAmelCase__             =							attention_dropout
									UpperCAmelCase__             =							activation_dropout
									UpperCAmelCase__             =							layerdrop
									UpperCAmelCase__             =							init_std
									UpperCAmelCase__             =							scale_embedding  # scale factor will be sqrt(d_model) if True
									UpperCAmelCase__             =							use_cache

									super().__init__(
									    pad_token_id=lowerCamelCase__    ,bos_token_id=lowerCamelCase__    ,eos_token_id=lowerCamelCase__    ,decoder_start_token_id=lowerCamelCase__    ,**lowerCamelCase__    ,)



