

'''simple docstring'''

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							:				List[str]     	=			{
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			[
							    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Wav2Vec2ForAudioFrameClassification",
							    "Wav2Vec2ForCTC",
							    "Wav2Vec2ForMaskedLM",
							    "Wav2Vec2ForPreTraining",
							    "Wav2Vec2ForSequenceClassification",
							    "Wav2Vec2ForXVector",
							    "Wav2Vec2Model",
							    "Wav2Vec2PreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE							:				Tuple     	=			[
							    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFWav2Vec2ForCTC",
							    "TFWav2Vec2Model",
							    "TFWav2Vec2PreTrainedModel",
							    "TFWav2Vec2ForSequenceClassification",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE							:				Dict     	=			[
							    "FlaxWav2Vec2ForCTC",
							    "FlaxWav2Vec2ForPreTraining",
							    "FlaxWav2Vec2Model",
							    "FlaxWav2Vec2PreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
							from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
							from .processing_wavaveca import WavaVecaProcessor
							from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_wavaveca import (
														    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    WavaVecaForAudioFrameClassification,
														    WavaVecaForCTC,
														    WavaVecaForMaskedLM,
														    WavaVecaForPreTraining,
														    WavaVecaForSequenceClassification,
														    WavaVecaForXVector,
														    WavaVecaModel,
														    WavaVecaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_wavaveca import (
														    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFWavaVecaForCTC,
														    TFWavaVecaForSequenceClassification,
														    TFWavaVecaModel,
														    TFWavaVecaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_wavaveca import (
														    FlaxWavaVecaForCTC,
														    FlaxWavaVecaForPreTraining,
														    FlaxWavaVecaModel,
														    FlaxWavaVecaPreTrainedModel,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE							:				Any     	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

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






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE_				=				latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





