





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_ ):
       _UpperCAmelCase				:  Any			   =   ["vqvae"]







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







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->int:
          return 5_0 if isinstance(self.scheduler		, A      ) else 1_0_0_0







       @torch.no_grad()
       def __call__(				self					:     Dict		, A					:     int = 1		, A					:     str = None		, A					:     np.ndarray = None		, A					:     int = 0		, A					:     int = 0		, A					:     int = None		, A					:     torch.Generator = None		, A					:     float = 0		, A					:     float = 0		, A					:     torch.Generator = None		, A					:     float = 0		, A					:     torch.Tensor = None		, A					:     torch.Tensor = None		, A					:     Tuple=True		, )       ->Union[
           Union[AudioPipelineOutput, ImagePipelineOutput],
           Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
       ]:
          lowerCamelCase__       :						List[Any]        =							steps or self.get_default_steps()
          self.scheduler.set_timesteps(A      )
          lowerCamelCase__       :						Tuple        =							step_generator or generator
          # For backwards compatibility
          if type(self.unet.config.sample_size      ) == int:
             lowerCamelCase__       :						Any        =							(self.unet.config.sample_size, self.unet.config.sample_size)
          if noise is None:
             lowerCamelCase__       :						Any        =							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		, )
          lowerCamelCase__       :						List[Any]        =							noise
          lowerCamelCase__       :						Dict        =							None

          if audio_file is not None or raw_audio is not None:
             self.mel.load_audio(A		, A      )
             lowerCamelCase__       :						Optional[int]        =							self.mel.audio_slice_to_image(A      )
             lowerCamelCase__       :						str        =							np.frombuffer(input_image.tobytes()		, dtype='''uint8'''      ).reshape(
                 (input_image.height, input_image.width)      )
             lowerCamelCase__       :						int        =							(input_image / 2_5_5) * 2 - 1
             lowerCamelCase__       :						List[str]        =							torch.tensor(input_image[np.newaxis, :, :]		, dtype=torch.float      ).to(self.device      )

             if self.vqvae is not None:
                lowerCamelCase__       :						Tuple        =							self.vqvae.encode(torch.unsqueeze(A		, 0      )      ).latent_dist.sample(
                    generator=A      )[0]
                lowerCamelCase__       :						List[Any]        =							self.vqvae.config.scaling_factor * input_images

             if start_step > 0:
                lowerCamelCase__       :						List[Any]        =							self.scheduler.add_noise(A		, A		, self.scheduler.timesteps[start_step - 1]      )

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

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

             if mask is not None:
                if mask_start > 0:
                   lowerCamelCase__       :						Tuple        =							mask[:, step, :, :mask_start]
                if mask_end > 0:
                   lowerCamelCase__       :						Union[str, Any]        =							mask[:, step, :, -mask_end:]

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

          lowerCamelCase__       :						List[Any]        =							(images / 2 + 0.5).clamp(0		, 1      )
          lowerCamelCase__       :						str        =							images.cpu().permute(0		, 2		, 3		, 1      ).numpy()
          lowerCamelCase__       :						Dict        =							(images * 2_5_5).round().astype('''uint8'''      )
          lowerCamelCase__       :						List[Any]        =							list(
              (Image.fromarray(_[:, :, 0]      ) for _ in images)
              if images.shape[3] == 1
              else (Image.fromarray(A		, mode='''RGB'''      ).convert('''L'''      ) for _ in images)      )

          lowerCamelCase__       :						int        =							[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 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     List[Image.Image]		, A					:     int = 5_0      )       ->np.ndarray:
          assert isinstance(self.scheduler		, A      )
          self.scheduler.set_timesteps(A      )
          lowerCamelCase__       :						int        =							np.array(
              [np.frombuffer(image.tobytes()		, dtype='''uint8'''      ).reshape((1, image.height, image.width)      ) for image in images]      )
          lowerCamelCase__       :						Tuple        =							(sample / 2_5_5) * 2 - 1
          lowerCamelCase__       :						str        =							torch.Tensor(A      ).to(self.device      )

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

          return sample







       @staticmethod
       def 						__lowerCamelCase   (				A					:     torch.Tensor		, A					:     torch.Tensor		, A					:     float      )       ->torch.Tensor:
          lowerCamelCase__       :						Tuple        =							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      )










from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_A :   Tuple  			=  logging.get_logger(__name__)

_A :   List[str]  			=  {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   "nat"

       _UpperCAmelCase				:  Tuple			   =   {
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self					:     List[Any]		, A					:     Union[str, Any]=4		, A					:     str=3		, A					:     List[Any]=6_4		, A					:     Optional[Any]=[3, 4, 6, 5]		, A					:     int=[2, 4, 8, 1_6]		, A					:     Optional[int]=7		, A					:     List[Any]=3.0		, A					:     str=True		, A					:     str=0.0		, A					:     Any=0.0		, A					:     int=0.1		, A					:     Tuple="gelu"		, A					:     List[Any]=0.02		, A					:     str=1e-5		, A					:     Optional[int]=0.0		, A					:     Optional[Any]=None		, A					:     Dict=None		, **A					:     str		, )       ->Union[str, Any]:
          super().__init__(**A      )

          lowerCamelCase__       :						Any        =							patch_size
          lowerCamelCase__       :						Optional[Any]        =							num_channels
          lowerCamelCase__       :						Any        =							embed_dim
          lowerCamelCase__       :						str        =							depths
          lowerCamelCase__       :						Union[str, Any]        =							len(A      )
          lowerCamelCase__       :						int        =							num_heads
          lowerCamelCase__       :						Optional[int]        =							kernel_size
          lowerCamelCase__       :						Optional[int]        =							mlp_ratio
          lowerCamelCase__       :						List[Any]        =							qkv_bias
          lowerCamelCase__       :						Tuple        =							hidden_dropout_prob
          lowerCamelCase__       :						List[str]        =							attention_probs_dropout_prob
          lowerCamelCase__       :						List[Any]        =							drop_path_rate
          lowerCamelCase__       :						Union[str, Any]        =							hidden_act
          lowerCamelCase__       :						Union[str, Any]        =							layer_norm_eps
          lowerCamelCase__       :						List[Any]        =							initializer_range
          # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__       :						str        =							int(embed_dim * 2 ** (len(A      ) - 1)      )
          lowerCamelCase__       :						Dict        =							layer_scale_init_value
          lowerCamelCase__       :						Dict        =							['''stem'''] + [F"stage{idx}" for idx in range(1		, len(A      ) + 1      )]
          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							get_aligned_output_features_output_indices(
              out_features=A		, out_indices=A		, stage_names=self.stage_names      )



