


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

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


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





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	def __init__(   self						,				__a						,				__a							):

						'''simple docstring'''





						super().__init__()
						self.register_modules(unet=__a						,				scheduler=__a							)





	@torch.no_grad()
	def __call__(   self						,				__a = 1						,				__a = 100						,				__a = None						,				__a = None						,				__a = True						,				):

						'''simple docstring'''





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

						__a			:       Optional[Any]					    =  audio_length_in_s * self.unet.config.sample_rate

						__a			:       str					    =  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}."""							)

						__a			:       Tuple					    =  int(__a							)
						if sample_size % down_scale_factor != 0:
											__a			:       Dict					    =  (
											    (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.'							)
						__a			:       Tuple					    =  int(__a							)

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

						__a			:       Tuple					    =  randn_tensor(__a						,				generator=__a						,				device=self.device						,				dtype=__a							)

						# set step values
						self.scheduler.set_timesteps(__a						,				device=audio.device							)
						__a			:       str					    =  self.scheduler.timesteps.to(__a							)

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

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

						__a			:       Union[str, Any]					    =  audio.clamp(-1						,				1							).float().cpu().numpy()

						__a			:       List[str]					    =  audio[:, :, :original_sample_size]

						if not return_dict:
											return (audio,)

						return AudioPipelineOutput(audios=__a							)







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase	:						Union[str, Any]       =	{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	['BlenderbotTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	[
	    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BlenderbotForCausalLM',
	    'BlenderbotForConditionalGeneration',
	    'BlenderbotModel',
	    'BlenderbotPreTrainedModel',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Union[str, Any]       =	[
	    'TFBlenderbotForConditionalGeneration',
	    'TFBlenderbotModel',
	    'TFBlenderbotPreTrainedModel',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Dict       =	[
	    'FlaxBlenderbotForConditionalGeneration',
	    'FlaxBlenderbotModel',
	    'FlaxBlenderbotPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						str       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



