




'''simple docstring'''







from typing import TYPE_CHECKING

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


A     ={
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =['WhisperTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =[
       'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'WhisperForConditionalGeneration',
       'WhisperModel',
       'WhisperPreTrainedModel',
       'WhisperForAudioClassification',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =[
       'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFWhisperForConditionalGeneration',
       'TFWhisperModel',
       'TFWhisperPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =[
       'FlaxWhisperForConditionalGeneration',
       'FlaxWhisperModel',
       'FlaxWhisperPreTrainedModel',
       'FlaxWhisperForAudioClassification',
   ]


if TYPE_CHECKING:
   from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
   from .feature_extraction_whisper import WhisperFeatureExtractor
   from .processing_whisper import WhisperProcessor
   from .tokenization_whisper import WhisperTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_whisper_fast import WhisperTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_whisper import (
          WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WhisperForAudioClassification,
          WhisperForConditionalGeneration,
          WhisperModel,
          WhisperPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_whisper import (
          TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWhisperForConditionalGeneration,
          TFWhisperModel,
          TFWhisperPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_whisper import (
          FlaxWhisperForAudioClassification,
          FlaxWhisperForConditionalGeneration,
          FlaxWhisperModel,
          FlaxWhisperPreTrainedModel,
      )

else:
   import sys

   A     =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










'''simple docstring'''







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 			_a  (	__a ):
       __a				:     str   				=					["""vqvae"""]
       def __init__(	self						:	str   ,				lowercase						:	AutoencoderKL   ,				lowercase						:	UNetaDConditionModel   ,				lowercase						:	Mel   ,				lowercase						:	Union[DDIMScheduler, DDPMScheduler]   ,				):



           '''simple docstring'''

           super().__init__()
           self.register_modules(unet=lowercase   ,				scheduler=lowercase   ,				mel=lowercase   ,				vqvae=lowercase				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           return 50 if isinstance(self.scheduler   ,				lowercase				) else 1_000
       @torch.no_grad()
       def __call__(	self						:	Optional[Any]   ,				lowercase						:	int = 1   ,				lowercase						:	str = None   ,				lowercase						:	np.ndarray = None   ,				lowercase						:	int = 0   ,				lowercase						:	int = 0   ,				lowercase						:	int = None   ,				lowercase						:	torch.Generator = None   ,				lowercase						:	float = 0   ,				lowercase						:	float = 0   ,				lowercase						:	torch.Generator = None   ,				lowercase						:	float = 0   ,				lowercase						:	torch.Tensor = None   ,				lowercase						:	torch.Tensor = None   ,				lowercase						:	Tuple=True   ,				):



           '''simple docstring'''

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

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

               if self.vqvae is not None:
                   UpperCAmelCase			       =		self.vqvae.encode(torch.unsqueeze(lowercase   ,				0				)				).latent_dist.sample(
                       generator=lowercase				)[0]
                   UpperCAmelCase			       =		self.vqvae.config.scaling_factor * input_images

               if start_step > 0:
                   UpperCAmelCase			       =		self.scheduler.add_noise(lowercase   ,				lowercase   ,				self.scheduler.timesteps[start_step - 1]				)

               UpperCAmelCase			       =		(
                   self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
               )
               UpperCAmelCase			       =		int(mask_start_secs * pixels_per_second				)
               UpperCAmelCase			       =		int(mask_end_secs * pixels_per_second				)
               UpperCAmelCase			       =		self.scheduler.add_noise(lowercase   ,				lowercase   ,				torch.tensor(self.scheduler.timesteps[start_step:]				)				)

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
               if isinstance(self.unet   ,				lowercase				):
                   UpperCAmelCase			       =		self.unet(lowercase   ,				lowercase   ,				lowercase				)['''sample''']
               else:
                   UpperCAmelCase			       =		self.unet(lowercase   ,				lowercase				)['''sample''']

               if isinstance(self.scheduler   ,				lowercase				):
                   UpperCAmelCase			       =		self.scheduler.step(
                       model_output=lowercase   ,				timestep=lowercase   ,				sample=lowercase   ,				eta=lowercase   ,				generator=lowercase   ,				)['''prev_sample''']
               else:
                   UpperCAmelCase			       =		self.scheduler.step(
                       model_output=lowercase   ,				timestep=lowercase   ,				sample=lowercase   ,				generator=lowercase   ,				)['''prev_sample''']

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

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

           UpperCAmelCase			       =		(images / 2 + 0.5).clamp(0   ,				1				)
           UpperCAmelCase			       =		images.cpu().permute(0   ,				2   ,				3   ,				1				).numpy()
           UpperCAmelCase			       =		(images * 255).round().astype('''uint8'''				)
           UpperCAmelCase			       =		list(
               (Image.fromarray(_[:, :, 0]				) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(lowercase   ,				mode='''RGB'''				).convert('''L'''				) for _ in images)				)

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

           return BaseOutput(**AudioPipelineOutput(np.array(lowercase				)[:, np.newaxis, :]				)   ,				**ImagePipelineOutput(lowercase				)				)
       @torch.no_grad()
       def     A      (	self						:	Dict   ,				lowercase						:	List[Image.Image]   ,				lowercase						:	int = 50				):



           '''simple docstring'''

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

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

           return sample







       @staticmethod
       def     A      (	lowercase						:	torch.Tensor   ,				lowercase						:	torch.Tensor   ,				lowercase						:	float				):



           '''simple docstring'''

           UpperCAmelCase			       =		acos(torch.dot(torch.flatten(lowercase				)   ,				torch.flatten(lowercase				)				) / torch.norm(lowercase				) / torch.norm(lowercase				)				)
           return sin((1 - alpha) * theta				) * xa / sin(lowercase				) + sin(alpha * theta				) * xa / sin(lowercase				)




