




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 			_UpperCamelCase      (				SCREAMING_SNAKE_CASE__		):
      def __init__(			self   :Tuple ,      lowerCamelCase   :Dict ,      lowerCamelCase   :str ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :List[Any] = None ,      )     ->			List[str]:
       super().__init__()
       self.register_modules(transformer=snake_case__ ,      vae=snake_case__ ,      scheduler=snake_case__      )

       # create a imagenet -> id dictionary for easier use
       UpperCAmelCase__							 =						{}
       if idalabel is not None:
        for key, value in idalabel.items():
         for label in value.split(","      ):
          UpperCAmelCase__							 =						int(snake_case__      )
        UpperCAmelCase__							 =						dict(sorted(self.labels.items()      )      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Tuple      )     ->			int:

       if not isinstance(snake_case__ ,      snake_case__      ):
        UpperCAmelCase__							 =						list(snake_case__      )

       for l in label:
        if l not in self.labels:
         raise ValueError(
             f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''      )

       return [self.labels[l] for l in label]







      @torch.no_grad()
      def __call__(			self   :Optional[Any] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Dict = 4.0 ,      lowerCamelCase   :Tuple = None ,      lowerCamelCase   :Tuple = 50 ,      lowerCamelCase   :Union[str, Any] = "pil" ,      lowerCamelCase   :Union[str, Any] = True ,      )     ->			Any:
       UpperCAmelCase__							 =						len(snake_case__      )
       UpperCAmelCase__							 =						self.transformer.config.sample_size
       UpperCAmelCase__							 =						self.transformer.config.in_channels

       UpperCAmelCase__							 =						randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size) ,      generator=snake_case__ ,      device=self.device ,      dtype=self.transformer.dtype ,      )
       UpperCAmelCase__							 =						torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

       UpperCAmelCase__							 =						torch.tensor(snake_case__ ,      device=self.device      ).reshape(-1      )
       UpperCAmelCase__							 =						torch.tensor([1000] * batch_size ,      device=self.device      )
       UpperCAmelCase__							 =						torch.cat([class_labels, class_null] ,      0      ) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(snake_case__      )

       for t in self.progress_bar(self.scheduler.timesteps      ):
        if guidance_scale > 1:
         UpperCAmelCase__							 =						latent_model_input[: len(snake_case__      ) // 2]
         UpperCAmelCase__							 =						torch.cat([half, half] ,      dim=0      )
        UpperCAmelCase__							 =						self.scheduler.scale_model_input(snake_case__ ,      snake_case__      )

        UpperCAmelCase__							 =						t
        if not torch.is_tensor(snake_case__      ):
         # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
         # This would be a good case for the `match` statement (Python 3.10+)
         UpperCAmelCase__							 =						latent_model_input.device.type == "mps"
         if isinstance(snake_case__ ,      snake_case__      ):
          UpperCAmelCase__							 =						torch.floataa if is_mps else torch.floataa
         else:
          UpperCAmelCase__							 =						torch.intaa if is_mps else torch.intaa
         UpperCAmelCase__							 =						torch.tensor([timesteps] ,      dtype=snake_case__ ,      device=latent_model_input.device      )
        elif len(timesteps.shape      ) == 0:
         UpperCAmelCase__							 =						timesteps[None].to(latent_model_input.device      )
        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        UpperCAmelCase__							 =						timesteps.expand(latent_model_input.shape[0]      )
        # predict noise model_output
        UpperCAmelCase__							 =						self.transformer(
            snake_case__ ,      timestep=snake_case__ ,      class_labels=snake_case__      ).sample

        # perform guidance
        if guidance_scale > 1:
         UpperCAmelCase__							 =						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
         UpperCAmelCase__							 =						torch.split(snake_case__ ,      len(snake_case__      ) // 2 ,      dim=0      )

         UpperCAmelCase__							 =						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
         UpperCAmelCase__							 =						torch.cat([half_eps, half_eps] ,      dim=0      )

         UpperCAmelCase__							 =						torch.cat([eps, rest] ,      dim=1      )

        # learned sigma
        if self.transformer.config.out_channels // 2 == latent_channels:
         UpperCAmelCase__							 =						torch.split(snake_case__ ,      snake_case__ ,      dim=1      )
        else:
         UpperCAmelCase__							 =						noise_pred

        # compute previous image: x_t -> x_t-1
        UpperCAmelCase__							 =						self.scheduler.step(snake_case__ ,      snake_case__ ,      snake_case__      ).prev_sample

       if guidance_scale > 1:
        UpperCAmelCase__							 =						latent_model_input.chunk(2 ,      dim=0      )
       else:
        UpperCAmelCase__							 =						latent_model_input

       UpperCAmelCase__							 =						1 / self.vae.config.scaling_factor * latents
       UpperCAmelCase__							 =						self.vae.decode(snake_case__      ).sample

       UpperCAmelCase__							 =						(samples / 2 + 0.5).clamp(0 ,      1      )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       UpperCAmelCase__							 =						samples.cpu().permute(0 ,      2 ,      3 ,      1      ).float().numpy()

       if output_type == "pil":
        UpperCAmelCase__							 =						self.numpy_to_pil(snake_case__      )

       if not return_dict:
        return (samples,)

       return ImagePipelineOutput(images=snake_case__      )











from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCAmelCase						:   Any		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		["""input_values""", """padding_mask"""]
      def __init__(			self   :Dict ,      lowerCamelCase   :int = 1 ,      lowerCamelCase   :int = 2_4000 ,      lowerCamelCase   :float = 0.0 ,      lowerCamelCase   :float = None ,      lowerCamelCase   :float = None ,      **lowerCamelCase   :Optional[Any] ,      )     ->			str:
       super().__init__(feature_size=lowerCamelCase ,      sampling_rate=lowerCamelCase ,      padding_value=lowerCamelCase ,      **lowerCamelCase      )
       UpperCAmelCase__							 =						chunk_length_s
       UpperCAmelCase__							 =						overlap
      @property
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Optional[int]:
       if self.chunk_length_s is None:
        return None
       else:
        return int(self.chunk_length_s * self.sampling_rate      )
      @property
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[int]:
       if self.chunk_length_s is None or self.overlap is None:
        return None
       else:
        return max(1 ,      int((1.0 - self.overlap) * self.chunk_length      )      )







      def __call__(			self   :List[Any] ,      lowerCamelCase   :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,      lowerCamelCase   :Optional[Union[bool, str, PaddingStrategy]] = None ,      lowerCamelCase   :Optional[bool] = False ,      lowerCamelCase   :Optional[int] = None ,      lowerCamelCase   :Optional[Union[str, TensorType]] = None ,      lowerCamelCase   :Optional[int] = None ,      )     ->			BatchFeature:
       if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
         raise ValueError(
             f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
             f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
             f''' {self.sampling_rate} and not {sampling_rate}.'''      )
       else:
        logger.warning(
            "It is strongly recommended to pass the `sampling_rate` argument to this function. "
            "Failing to do so can result in silent errors that might be hard to debug."      )

       if padding and truncation:
        raise ValueError("Both padding and truncation were set. Make sure you only set one."      )
       elif padding is None:
        # by default let's pad the inputs
        UpperCAmelCase__							 =						True

       UpperCAmelCase__							 =						bool(
           isinstance(lowerCamelCase ,      (list, tuple)      ) and (isinstance(raw_audio[0] ,      (np.ndarray, tuple, list)      ))      )

       if is_batched:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase ,      dtype=np.floataa      ).T for audio in raw_audio]
       elif not is_batched and not isinstance(lowerCamelCase ,      np.ndarray      ):
        UpperCAmelCase__							 =						np.asarray(lowerCamelCase ,      dtype=np.floataa      )
       elif isinstance(lowerCamelCase ,      np.ndarray      ) and raw_audio.dtype is np.dtype(np.floataa      ):
        UpperCAmelCase__							 =						raw_audio.astype(np.floataa      )

       # always return batch
       if not is_batched:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase      ).T]

       # verify inputs are valid
       for idx, example in enumerate(lowerCamelCase      ):
        if example.ndim > 2:
         raise ValueError(f'''Expected input shape (channels, length) but got shape {example.shape}'''      )
        if self.feature_size == 1 and example.ndim != 1:
         raise ValueError(f'''Expected mono audio but example has {example.shape[-1]} channels'''      )
        if self.feature_size == 2 and example.shape[-1] != 2:
         raise ValueError(f'''Expected stereo audio but example has {example.shape[-1]} channels'''      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						BatchFeature({"input_values": raw_audio}      )
       if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
        if truncation:
         UpperCAmelCase__							 =						min(array.shape[0] for array in raw_audio      )
         UpperCAmelCase__							 =						int(np.floor(max_length / self.chunk_stride      )      )
         UpperCAmelCase__							 =						(nb_step - 1) * self.chunk_stride + self.chunk_length
        elif padding:
         UpperCAmelCase__							 =						max(array.shape[0] for array in raw_audio      )
         UpperCAmelCase__							 =						int(np.ceil(max_length / self.chunk_stride      )      )
         UpperCAmelCase__							 =						(nb_step - 1) * self.chunk_stride + self.chunk_length
         UpperCAmelCase__							 =						"max_length"
        else:
         UpperCAmelCase__							 =						input_values

        # normal padding on batch
       if padded_inputs is None:
        UpperCAmelCase__							 =						self.pad(
            lowerCamelCase ,      max_length=lowerCamelCase ,      truncation=lowerCamelCase ,      padding=lowerCamelCase ,      return_attention_mask=lowerCamelCase ,      )
        if padding:
         UpperCAmelCase__							 =						padded_inputs.pop("attention_mask"      )

       UpperCAmelCase__							 =						[]
       for example in padded_inputs.pop("input_values"      ):
        if self.feature_size == 1:
         UpperCAmelCase__							 =						example[..., None]
        input_values.append(example.T      )

       UpperCAmelCase__							 =						input_values
       if return_tensors is not None:
        UpperCAmelCase__							 =						padded_inputs.convert_to_tensors(lowerCamelCase      )

       return padded_inputs





