

from typing import List, Optional, Tuple, Union

import torch

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


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






class 	A_		(						SCREAMING_SNAKE_CASE    ):
							def __init__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								super().__init__()
								self.register_modules(unet=SCREAMING_SNAKE_CASE__       ,scheduler=SCREAMING_SNAKE_CASE__)


							@torch.no_grad()
							def __call__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int = 1       ,SCREAMING_SNAKE_CASE__		:      int = 1_0_0       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[float] = None       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,):

								if audio_length_in_s is None:
									__lowerCamelCase    :    List[str] 	=			self.unet.config.sample_size / self.unet.config.sample_rate

								__lowerCamelCase    :    Any 	=			audio_length_in_s * self.unet.config.sample_rate

								__lowerCamelCase    :    Optional[int] 	=			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    :    Any 	=			int(SCREAMING_SNAKE_CASE__)
								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    :    Union[str, Any] 	=			int(SCREAMING_SNAKE_CASE__)

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

								__lowerCamelCase    :    Optional[Any] 	=			randn_tensor(SCREAMING_SNAKE_CASE__       ,generator=SCREAMING_SNAKE_CASE__       ,device=self.device       ,dtype=SCREAMING_SNAKE_CASE__)

								# set step values
								self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__       ,device=audio.device)
								__lowerCamelCase    :    int 	=			self.scheduler.timesteps.to(SCREAMING_SNAKE_CASE__)

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

									# 2. compute previous image: x_t -> t_t-1
									__lowerCamelCase    :    Union[str, Any] 	=			self.scheduler.step(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__).prev_sample

								__lowerCamelCase    :    Any 	=			audio.clamp(-1       ,1).float().cpu().numpy()

								__lowerCamelCase    :    str 	=			audio[:, :, :original_sample_size]

								if not return_dict:
									return (audio,)

								return AudioPipelineOutput(audios=SCREAMING_SNAKE_CASE__)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		={
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




