






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					= {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['ReformerTokenizer']

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ReformerAttention',
      'ReformerForMaskedLM',
      'ReformerForQuestionAnswering',
      'ReformerForSequenceClassification',
      'ReformerLayer',
      'ReformerModel',
      'ReformerModelWithLMHead',
      'ReformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer import ReformerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer_fast import ReformerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_reformer import (
        REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ReformerAttention,
        ReformerForMaskedLM,
        ReformerForQuestionAnswering,
        ReformerForSequenceClassification,
        ReformerLayer,
        ReformerModel,
        ReformerModelWithLMHead,
        ReformerPreTrainedModel,
    )

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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							["""vqvae"""]




 def __init__(      self	:							int					,      __lowercase	:							AutoencoderKL					,      __lowercase	:							UNetaDConditionModel					,      __lowercase	:							Mel					,      __lowercase	:							Union[DDIMScheduler, DDPMScheduler]					,      )	->						int:
       super().__init__()
       self.register_modules(unet=__lowercase					,      scheduler=__lowercase					,      mel=__lowercase					,      vqvae=__lowercase      )




 def      __magic_name__	(      self	:							List[str]      )	->						int:
       return 50 if isinstance(self.scheduler					,      __lowercase      ) else 10_00




 @torch.no_grad()
 def __call__(      self	:							Dict					,      __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	:							Dict=True					,      )	->						Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=steps or self.get_default_steps()
       self.scheduler.set_timesteps(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size      ) == int:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=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					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=noise
       SCREAMING_SNAKE_CASE__     :				List[str]									=None

       if audio_file is not None or raw_audio is not None:
             self.mel.load_audio(__lowercase					,      __lowercase      )
             SCREAMING_SNAKE_CASE__     :				Dict									=self.mel.audio_slice_to_image(__lowercase      )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=np.frombuffer(input_image.tobytes()					,      dtype='''uint8'''      ).reshape(
                 (input_image.height, input_image.width)      )
             SCREAMING_SNAKE_CASE__     :				int									=(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__     :				Optional[int]									=self.vqvae.encode(torch.unsqueeze(__lowercase					,      0      )      ).latent_dist.sample(
                       generator=__lowercase      )[0]
                   SCREAMING_SNAKE_CASE__     :				int									=self.vqvae.config.scaling_factor * input_images

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

             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(
                 self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
             )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=int(mask_start_secs * pixels_per_second      )
             SCREAMING_SNAKE_CASE__     :				Tuple									=int(mask_end_secs * pixels_per_second      )
             SCREAMING_SNAKE_CASE__     :				int									=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      ):
                   SCREAMING_SNAKE_CASE__     :				str									=self.unet(__lowercase					,      __lowercase					,      __lowercase      )['''sample''']
             else:
                   SCREAMING_SNAKE_CASE__     :				str									=self.unet(__lowercase					,      __lowercase      )['''sample''']

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

             if mask is not None:
                   if mask_start > 0:
                         SCREAMING_SNAKE_CASE__     :				Optional[Any]									=mask[:, step, :, :mask_start]
                   if mask_end > 0:
                         SCREAMING_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
             SCREAMING_SNAKE_CASE__     :				str									=1 / self.vqvae.config.scaling_factor * images
             SCREAMING_SNAKE_CASE__     :				int									=self.vqvae.decode(__lowercase      )['''sample''']

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

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=[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      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							List[Image.Image]					,      __lowercase	:							int = 50      )	->						np.ndarray:
       assert isinstance(self.scheduler					,      __lowercase      )
       self.scheduler.set_timesteps(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=np.array(
           [np.frombuffer(image.tobytes()					,      dtype='''uint8'''      ).reshape((1, image.height, image.width)      ) for image in images]      )
       SCREAMING_SNAKE_CASE__     :				str									=(sample / 2_55) * 2 - 1
       SCREAMING_SNAKE_CASE__     :				str									=torch.Tensor(__lowercase      ).to(self.device      )

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

       return sample




 @staticmethod
 def      __magic_name__	(      __lowercase	:							torch.Tensor					,      __lowercase	:							torch.Tensor					,      __lowercase	:							float      )	->						torch.Tensor:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=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      )