





from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class SCREAMING_SNAKE_CASE_  (    __snake_case    ):
						__magic_name__:	List[Any]       					=				["vqvae"]






						def __init__(					self					:					List[str]						,      _A					:					List[Any]						,      _A					:					List[Any]						,      _A					:					Dict						,      _A					:					List[Any]						,      )							->			str:







							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_A						,      scheduler=_A						,      mel=_A						,      vqvae=_A		)






						def UpperCAmelCase_ (					self					:					Dict		)							->			int:







							"""simple docstring"""


							return 50 if isinstance(self.scheduler						,      _A		) else 1000






						@torch.no_grad()
						def __call__(					self					:					Optional[Any]						,      _A					:					Optional[Any] = 1						,      _A					:					List[str] = None						,      _A					:					List[Any] = None						,      _A					:					List[Any] = 0						,      _A					:					Tuple = 0						,      _A					:					str = None						,      _A					:					Dict = None						,      _A					:					int = 0						,      _A					:					Tuple = 0						,      _A					:					Union[str, Any] = None						,      _A					:					Any = 0						,      _A					:					str = None						,      _A					:					Tuple = None						,      _A					:					Any=True						,      )							->			Union[
						    Union[AudioPipelineOutput, ImagePipelineOutput],
						    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
						]:







							"""simple docstring"""


							snake_case_						:	Optional[Any]      =		steps or self.get_default_steps()
							self.scheduler.set_timesteps(_A		)
							snake_case_						:	Optional[Any]      =		step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size		) == int:
								snake_case_						:	Union[str, Any]      =		(self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
								snake_case_						:	int      =		randn_tensor(
								    (
								        batch_size,
								        self.unet.config.in_channels,
								        self.unet.config.sample_size[0],
								        self.unet.config.sample_size[1],
								    )						,      generator=_A						,      device=self.device						,      )
							snake_case_						:	Union[str, Any]      =		noise
							snake_case_						:	int      =		None

							if audio_file is not None or raw_audio is not None:
								self.mel.load_audio(_A						,      _A		)
								snake_case_						:	Tuple      =		self.mel.audio_slice_to_image(_A		)
								snake_case_						:	Optional[int]      =		np.frombuffer(input_image.tobytes()						,      dtype='uint8'		).reshape(
								    (input_image.height, input_image.width)		)
								snake_case_						:	Tuple      =		(input_image / 255) * 2 - 1
								snake_case_						:	Union[str, Any]      =		torch.tensor(input_image[np.newaxis, :, :]						,      dtype=torch.float		).to(self.device		)

								if self.vqvae is not None:
									snake_case_						:	Optional[int]      =		self.vqvae.encode(torch.unsqueeze(_A						,      0		)		).latent_dist.sample(
									    generator=_A		)[0]
									snake_case_						:	Optional[Any]      =		self.vqvae.config.scaling_factor * input_images

								if start_step > 0:
									snake_case_						:	Union[str, Any]      =		self.scheduler.add_noise(_A						,      _A						,      self.scheduler.timesteps[start_step - 1]		)

								snake_case_						:	Optional[Any]      =		(
								    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
								)
								snake_case_						:	str      =		int(mask_start_secs * pixels_per_second		)
								snake_case_						:	int      =		int(mask_end_secs * pixels_per_second		)
								snake_case_						:	List[Any]      =		self.scheduler.add_noise(_A						,      _A						,      torch.tensor(self.scheduler.timesteps[start_step:]		)		)

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
								if isinstance(self.unet						,      _A		):
									snake_case_						:	List[str]      =		self.unet(_A						,      _A						,      _A		)['''sample''']
								else:
									snake_case_						:	int      =		self.unet(_A						,      _A		)['''sample''']

								if isinstance(self.scheduler						,      _A		):
									snake_case_						:	Any      =		self.scheduler.step(
									    model_output=_A						,      timestep=_A						,      sample=_A						,      eta=_A						,      generator=_A						,      )['''prev_sample''']
								else:
									snake_case_						:	Optional[int]      =		self.scheduler.step(
									    model_output=_A						,      timestep=_A						,      sample=_A						,      generator=_A						,      )['''prev_sample''']

								if mask is not None:
									if mask_start > 0:
										snake_case_						:	str      =		mask[:, step, :, :mask_start]
									if mask_end > 0:
										snake_case_						:	List[str]      =		mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
								# 0.18215 was scaling factor used in training to ensure unit variance
								snake_case_						:	str      =		1 / self.vqvae.config.scaling_factor * images
								snake_case_						:	Tuple      =		self.vqvae.decode(_A		)['''sample''']

							snake_case_						:	Tuple      =		(images / 2 + 0.5).clamp(0						,      1		)
							snake_case_						:	Union[str, Any]      =		images.cpu().permute(0						,      2						,      3						,      1		).numpy()
							snake_case_						:	Optional[Any]      =		(images * 255).round().astype('uint8'		)
							snake_case_						:	Optional[int]      =		list(
							    (Image.fromarray(_[:, :, 0]		) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(_A						,      mode='RGB'		).convert('L'		) for _ in images)		)

							snake_case_						:	Any      =		[self.mel.image_to_audio(_A		) for _ in images]
							if not return_dict:
								return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(_A		)[:, np.newaxis, :]		)						,      **ImagePipelineOutput(_A		)		)






						@torch.no_grad()
						def UpperCAmelCase_ (					self					:					str						,      _A					:					Tuple						,      _A					:					Dict = 50		)							->			np.ndarray:







							"""simple docstring"""


							assert isinstance(self.scheduler						,      _A		)
							self.scheduler.set_timesteps(_A		)
							snake_case_						:	Union[str, Any]      =		np.array(
							    [np.frombuffer(image.tobytes()						,      dtype='uint8'		).reshape((1, image.height, image.width)		) for image in images]		)
							snake_case_						:	int      =		(sample / 255) * 2 - 1
							snake_case_						:	Union[str, Any]      =		torch.Tensor(_A		).to(self.device		)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,      (0,)		)		):
								snake_case_						:	Union[str, Any]      =		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
								snake_case_						:	Union[str, Any]      =		self.scheduler.alphas_cumprod[t]
								snake_case_						:	Tuple      =		(
								    self.scheduler.alphas_cumprod[prev_timestep]
								    if prev_timestep >= 0
								    else self.scheduler.final_alpha_cumprod
								)
								snake_case_						:	List[Any]      =		1 - alpha_prod_t
								snake_case_						:	Dict      =		self.unet(_A						,      _A		)['''sample''']
								snake_case_						:	List[str]      =		(1 - alpha_prod_t_prev) ** 0.5 * model_output
								snake_case_						:	List[Any]      =		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
								snake_case_						:	Union[str, Any]      =		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample






						@staticmethod
						def UpperCAmelCase_ (					_A					:					List[Any]						,      _A					:					Optional[int]						,      _A					:					List[Any]		)							->			torch.Tensor:







							"""simple docstring"""


							snake_case_						:	List[str]      =		acos(torch.dot(torch.flatten(_A		)						,      torch.flatten(_A		)		) / torch.norm(_A		) / torch.norm(_A		)		)
							return sin((1 - alpha) * theta		) * xa / sin(_A		) + sin(alpha * theta		) * xa / sin(_A		)



'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


lowercase			:				Dict    		=			{
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[Any]    		=			['Speech2TextTokenizer']

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[str]    		=			['Speech2TextFeatureExtractor']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Dict    		=			[
          'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSpeech2TextForConditionalGeneration',
          'TFSpeech2TextModel',
          'TFSpeech2TextPreTrainedModel',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Any    		=			[
          'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Speech2TextForConditionalGeneration',
          'Speech2TextModel',
          'Speech2TextPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
      from .processing_speech_to_text import SpeechaTextProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speech_to_text import SpeechaTextTokenizer

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_speech_to_text import (
                TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSpeechaTextForConditionalGeneration,
                TFSpeechaTextModel,
                TFSpeechaTextPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text import (
                SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextForConditionalGeneration,
                SpeechaTextModel,
                SpeechaTextPreTrainedModel,
            )

else:
      import sys

      lowercase			:				List[Any]    		=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




