



'''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__(   lowerCamelCase__			):
    lowercase__				=					["""vqvae"""]







    def __init__(   self				:						Union[str, Any]		,    __snake_case				:						AutoencoderKL		,    __snake_case				:						UNetaDConditionModel		,    __snake_case				:						Mel		,    __snake_case				:						Union[DDIMScheduler, DDPMScheduler]		,    ):
      super().__init__()
      self.register_modules(unet=__snake_case		,    scheduler=__snake_case		,    mel=__snake_case		,    vqvae=__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      return 50 if isinstance(self.scheduler		,    __snake_case			) else 10_00







    @torch.no_grad()
    def __call__(   self				:						List[str]		,    __snake_case				:						int = 1		,    __snake_case				:						str = None		,    __snake_case				:						np.ndarray = None		,    __snake_case				:						int = 0		,    __snake_case				:						int = 0		,    __snake_case				:						int = None		,    __snake_case				:						torch.Generator = None		,    __snake_case				:						float = 0		,    __snake_case				:						float = 0		,    __snake_case				:						torch.Generator = None		,    __snake_case				:						float = 0		,    __snake_case				:						torch.Tensor = None		,    __snake_case				:						torch.Tensor = None		,    __snake_case				:						int=True		,    ):
      a		:      str       =  steps or self.get_default_steps()
      self.scheduler.set_timesteps(__snake_case			)
      a		:      Tuple       =  step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size			) == int:
        a		:      Union[str, Any]       =  (self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
        a		:      str       =  randn_tensor(
            (
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size[0],
                self.unet.config.sample_size[1],
            )		,    generator=__snake_case		,    device=self.device		,    )
      a		:      List[Any]       =  noise
      a		:      str       =  None

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

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

        if start_step > 0:
          a		:      Dict       =  self.scheduler.add_noise(__snake_case		,    __snake_case		,    self.scheduler.timesteps[start_step - 1]			)

        a		:      Any       =  (
            self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
        )
        a		:      Dict       =  int(mask_start_secs * pixels_per_second			)
        a		:      str       =  int(mask_end_secs * pixels_per_second			)
        a		:      Union[str, Any]       =  self.scheduler.add_noise(__snake_case		,    __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		,    __snake_case			):
          a		:      int       =  self.unet(__snake_case		,    __snake_case		,    __snake_case			)['sample']
        else:
          a		:      Any       =  self.unet(__snake_case		,    __snake_case			)['sample']

        if isinstance(self.scheduler		,    __snake_case			):
          a		:      List[str]       =  self.scheduler.step(
              model_output=__snake_case		,    timestep=__snake_case		,    sample=__snake_case		,    eta=__snake_case		,    generator=__snake_case		,    )['prev_sample']
        else:
          a		:      Optional[int]       =  self.scheduler.step(
              model_output=__snake_case		,    timestep=__snake_case		,    sample=__snake_case		,    generator=__snake_case		,    )['prev_sample']

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

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

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

      a		:      Optional[int]       =  [self.mel.image_to_audio(__snake_case			) for _ in images]
      if not return_dict:
        return images, (self.mel.get_sample_rate(), audios)

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







    @torch.no_grad()
    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						List[Image.Image]		,    __snake_case				:						int = 50			):
      assert isinstance(self.scheduler		,    __snake_case			)
      self.scheduler.set_timesteps(__snake_case			)
      a		:      Dict       =  np.array(
          [np.frombuffer(image.tobytes()		,    dtype='uint8'			).reshape((1, image.height, image.width)			) for image in images]			)
      a		:      Tuple       =  (sample / 2_55) * 2 - 1
      a		:      Dict       =  torch.Tensor(__snake_case			).to(self.device			)

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

      return sample







    @staticmethod
    def     lowercase_ (   __snake_case				:						torch.Tensor		,    __snake_case				:						torch.Tensor		,    __snake_case				:						float			):
      a		:      Optional[Any]       =  acos(torch.dot(torch.flatten(__snake_case			)		,    torch.flatten(__snake_case			)			) / torch.norm(__snake_case			) / torch.norm(__snake_case			)			)
      return sin((1 - alpha) * theta			) * xa / sin(__snake_case			) + sin(alpha * theta			) * xa / sin(__snake_case			)




'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

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


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, TensorType

lowerCAmelCase:      List[Any]		 =		logging.get_logger(__name__)

lowerCAmelCase:      List[str]		 =		{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}

class 							a__(      lowerCamelCase__			):
    lowercase__				=					"""imagegpt"""
    lowercase__				=					["""past_key_values"""]
    lowercase__				=					{
        """hidden_size""": """n_embd""",
        """max_position_embeddings""": """n_positions""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(   self				:						List[str]		,    __snake_case				:						int=5_12 + 1		,    __snake_case				:						Optional[Any]=32 * 32		,    __snake_case				:						Optional[Any]=5_12		,    __snake_case				:						List[str]=24		,    __snake_case				:						Tuple=8		,    __snake_case				:						Optional[int]=None		,    __snake_case				:						Dict="quick_gelu"		,    __snake_case				:						Dict=0.1		,    __snake_case				:						List[str]=0.1		,    __snake_case				:						Optional[Any]=0.1		,    __snake_case				:						Tuple=1e-5		,    __snake_case				:						Union[str, Any]=0.02		,    __snake_case				:						Optional[int]=True		,    __snake_case				:						Dict=True		,    __snake_case				:						str=False		,    __snake_case				:						Any=False		,    __snake_case				:						List[Any]=False		,    **__snake_case				:						List[str]		,    ):
      a		:      Optional[Any]       =  vocab_size
      a		:      Union[str, Any]       =  n_positions
      a		:      int       =  n_embd
      a		:      int       =  n_layer
      a		:      str       =  n_head
      a		:      int       =  n_inner
      a		:      int       =  activation_function
      a		:      Any       =  resid_pdrop
      a		:      Tuple       =  embd_pdrop
      a		:      List[Any]       =  attn_pdrop
      a		:      Tuple       =  layer_norm_epsilon
      a		:      Any       =  initializer_range
      a		:      str       =  scale_attn_weights
      a		:      List[Any]       =  use_cache
      a		:      Any       =  scale_attn_by_inverse_layer_idx
      a		:      Optional[Any]       =  reorder_and_upcast_attn
      a		:      str       =  tie_word_embeddings

      super().__init__(tie_word_embeddings=__snake_case		,    **__snake_case			)

class 							a__(      lowerCamelCase__			):







    @property
    def     lowercase_ (   self				:						Union[str, Any]			):
      return OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'sequence'}),
          ]			)







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						"FeatureExtractionMixin"		,    __snake_case				:						int = 1		,    __snake_case				:						int = -1		,    __snake_case				:						bool = False		,    __snake_case				:						Optional["TensorType"] = None		,    __snake_case				:						int = 3		,    __snake_case				:						int = 32		,    __snake_case				:						int = 32		,    ):
      a		:      Dict       =  self._generate_dummy_images(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)
      a		:      Union[str, Any]       =  dict(preprocessor(images=__snake_case		,    return_tensors=__snake_case			)			)

      return inputs