





"""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 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Tuple							=  ['''vqvae''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,)			->      str:





             """simple docstring"""






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




       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             return 50 if isinstance(self.scheduler		,SCREAMING_SNAKE_CASE__							) else 10_00




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





             """simple docstring"""






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

             if audio_file is not None or raw_audio is not None:
                   self.mel.load_audio(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE   :List[str]						=  self.mel.audio_slice_to_image(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE   :Dict						=  np.frombuffer(input_image.tobytes()		,dtype='''uint8'''							).reshape(
                       (input_image.height, input_image.width)							)
                   __SCREAMING_SNAKE_CASE   :List[str]						=  (input_image / 2_55) * 2 - 1
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.tensor(input_image[np.newaxis, :, :]		,dtype=torch.float							).to(self.device							)

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

                   if start_step > 0:
                         __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.scheduler.add_noise(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.scheduler.timesteps[start_step - 1]							)

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

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

                   if isinstance(self.scheduler		,SCREAMING_SNAKE_CASE__							):
                         __SCREAMING_SNAKE_CASE   :List[Any]						=  self.scheduler.step(
                             model_output=SCREAMING_SNAKE_CASE__		,timestep=SCREAMING_SNAKE_CASE__		,sample=SCREAMING_SNAKE_CASE__		,eta=SCREAMING_SNAKE_CASE__		,generator=SCREAMING_SNAKE_CASE__		,)['''prev_sample''']
                   else:
                         __SCREAMING_SNAKE_CASE   :Tuple						=  self.scheduler.step(
                             model_output=SCREAMING_SNAKE_CASE__		,timestep=SCREAMING_SNAKE_CASE__		,sample=SCREAMING_SNAKE_CASE__		,generator=SCREAMING_SNAKE_CASE__		,)['''prev_sample''']

                   if mask is not None:
                         if mask_start > 0:
                               __SCREAMING_SNAKE_CASE   :str						=  mask[:, step, :, :mask_start]
                         if mask_end > 0:
                               __SCREAMING_SNAKE_CASE   :Optional[int]						=  mask[:, step, :, -mask_end:]

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

             __SCREAMING_SNAKE_CASE   :int						=  (images / 2 + 0.5).clamp(0		,1							)
             __SCREAMING_SNAKE_CASE   :int						=  images.cpu().permute(0		,2		,3		,1							).numpy()
             __SCREAMING_SNAKE_CASE   :Dict						=  (images * 2_55).round().astype('''uint8'''							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  list(
                 (Image.fromarray(_[:, :, 0]							) for _ in images)
                 if images.shape[3] == 1
                 else (Image.fromarray(SCREAMING_SNAKE_CASE__		,mode='''RGB'''							).convert('''L'''							) for _ in images)							)

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [self.mel.image_to_audio(SCREAMING_SNAKE_CASE__							) for _ in images]
             if not return_dict:
                   return images, (self.mel.get_sample_rate(), audios)

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




       @torch.no_grad()
       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = 50							)			->      np.ndarray:





             """simple docstring"""






             assert isinstance(self.scheduler		,SCREAMING_SNAKE_CASE__							)
             self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Dict						=  np.array(
                 [np.frombuffer(image.tobytes()		,dtype='''uint8'''							).reshape((1, image.height, image.width)							) for image in images]							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  (sample / 2_55) * 2 - 1
             __SCREAMING_SNAKE_CASE   :List[Any]						=  torch.Tensor(SCREAMING_SNAKE_CASE__							).to(self.device							)

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

             return sample




       @staticmethod
       def     _UpperCamelCase     (						SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)			->      torch.Tensor:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  acos(torch.dot(torch.flatten(SCREAMING_SNAKE_CASE__							)		,torch.flatten(SCREAMING_SNAKE_CASE__							)							) / torch.norm(SCREAMING_SNAKE_CASE__							) / torch.norm(SCREAMING_SNAKE_CASE__							)							)
             return sin((1 - alpha) * theta							) * xa / sin(SCREAMING_SNAKE_CASE__							) + sin(alpha * theta							) * xa / sin(SCREAMING_SNAKE_CASE__							)






"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  '''xmod'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=3_05_22		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__="absolute"		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=("en_XX",)		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  vocab_size
             __SCREAMING_SNAKE_CASE   :List[Any]						=  hidden_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Tuple						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Dict						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :str						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  type_vocab_size
             __SCREAMING_SNAKE_CASE   :str						=  initializer_range
             __SCREAMING_SNAKE_CASE   :List[Any]						=  layer_norm_eps
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  position_embedding_type
             __SCREAMING_SNAKE_CASE   :Any						=  use_cache
             __SCREAMING_SNAKE_CASE   :List[str]						=  classifier_dropout
             __SCREAMING_SNAKE_CASE   :Any						=  pre_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reduction_factor
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_layer_norm
             __SCREAMING_SNAKE_CASE   :Dict						=  adapter_reuse_layer_norm
             __SCREAMING_SNAKE_CASE   :Tuple						=  ln_before_adapter
             __SCREAMING_SNAKE_CASE   :Any						=  list(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  default_language






class 					_SCREAMING_SNAKE_CASE(   A  ):




       @property
       def     _UpperCamelCase     (						self							)			->      Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task == "multiple-choice":
                   __SCREAMING_SNAKE_CASE   :Tuple						=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  {0: '''batch''', 1: '''sequence'''}
             return OrderedDict(
                 [
                     ('''input_ids''', dynamic_axis),
                     ('''attention_mask''', dynamic_axis),
                 ]							)