





from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE__							:      int         =    logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE__							:      List[Any]         =    """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase=8 ):
   a__						:    Any   		=	height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   a__						:    Union[str, Any]   		=	width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor







class 	__lowerCAmelCase      (       _UpperCamelCase	):





   def __init__(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      )					->	List[Any]:
      """simple docstring"""





      super().__init__()

      self.register_modules(
          unet=snake_case       ,      scheduler=snake_case       ,      movq=snake_case       ,      )
      a__						:    int   		=	2 ** (len(self.movq.config.block_out_channels   ) - 1)





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





      if latents is None:
         a__						:    str   		=	randn_tensor(snake_case       ,      generator=snake_case       ,      device=snake_case       ,      dtype=snake_case   )
      else:
         if latents.shape != shape:
            raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""   )
         a__						:    Tuple   		=	latents.to(snake_case   )

      a__						:    int   		=	latents * scheduler.init_noise_sigma
      return latents





   def 						_snake_case			(			self       ,      snake_case=0   )					->	Dict:
      """simple docstring"""





      if is_accelerate_available():
         from accelerate import cpu_offload
      else:
         raise ImportError("Please install accelerate via `pip install accelerate`"   )

      a__						:    Tuple   		=	torch.device(F"""cuda:{gpu_id}"""   )

      a__						:    List[Any]   		=	[
          self.unet,
          self.movq,
      ]
      for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
            cpu_offload(snake_case       ,      snake_case   )





   def 						_snake_case			(			self       ,      snake_case=0   )					->	List[Any]:
      """simple docstring"""





      if is_accelerate_available() and is_accelerate_version(">="       ,      "0.17.0.dev0"   ):
         from accelerate import cpu_offload_with_hook
      else:
         raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."   )

      a__						:    Tuple   		=	torch.device(F"""cuda:{gpu_id}"""   )

      if self.device.type != "cpu":
         self.to("cpu"       ,      silence_dtype_warnings=snake_case   )
         torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

      a__						:    str   		=	None
      for cpu_offloaded_model in [self.unet, self.movq]:
         a__		,  a__						:    int   		=	cpu_offload_with_hook(snake_case       ,      snake_case       ,      prev_module_hook=snake_case   )

      # We'll offload the last model manually.
      a__						:    Tuple   		=	hook





   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      if not hasattr(self.unet       ,      "_hf_hook"   ):
         return self.device
      for module in self.unet.modules():
         if (
             hasattr(snake_case       ,      "_hf_hook"   )
             and hasattr(module._hf_hook       ,      "execution_device"   )
             and module._hf_hook.execution_device is not None
         ):
            return torch.device(module._hf_hook.execution_device   )
      return self.device





   @torch.no_grad()
   @replace_example_docstring(snake_case   )
   def __call__(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case = 512       ,      snake_case = 512       ,      snake_case = 100       ,      snake_case = 4.0       ,      snake_case = 1       ,      snake_case = None       ,      snake_case = None       ,      snake_case = "pil"       ,      snake_case = True       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    str   		=	self._execution_device

      a__						:    List[str]   		=	guidance_scale > 1.0

      if isinstance(snake_case       ,      snake_case   ):
         a__						:    Union[str, Any]   		=	torch.cat(snake_case       ,      dim=0   )
      if isinstance(snake_case       ,      snake_case   ):
         a__						:    Any   		=	torch.cat(snake_case       ,      dim=0   )
      if isinstance(snake_case       ,      snake_case   ):
         a__						:    Optional[Any]   		=	torch.cat(snake_case       ,      dim=0   )

      a__						:    List[Any]   		=	image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
         a__						:    int   		=	image_embeds.repeat_interleave(snake_case       ,      dim=0   )
         a__						:    Any   		=	negative_image_embeds.repeat_interleave(snake_case       ,      dim=0   )
         a__						:    List[Any]   		=	hint.repeat_interleave(snake_case       ,      dim=0   )

      a__						:    int   		=	torch.cat([negative_image_embeds, image_embeds]       ,      dim=0   ).to(dtype=self.unet.dtype       ,      device=snake_case   )
      a__						:    Any   		=	torch.cat([hint, hint]       ,      dim=0   ).to(dtype=self.unet.dtype       ,      device=snake_case   )
      self.scheduler.set_timesteps(snake_case       ,      device=snake_case   )
      a__						:    List[Any]   		=	self.scheduler.timesteps

      a__						:    Any   		=	self.movq.config.latent_channels

      a__		,  a__						:    Any   		=	downscale_height_and_width(snake_case       ,      snake_case       ,      self.movq_scale_factor   )

      # create initial latent
      a__						:    Union[str, Any]   		=	self.prepare_latents(
          (batch_size, num_channels_latents, height, width)       ,      image_embeds.dtype       ,      snake_case       ,      snake_case       ,      snake_case       ,      self.scheduler       ,      )

      for i, t in enumerate(self.progress_bar(snake_case   )   ):
         # expand the latents if we are doing classifier free guidance
         a__						:    List[Any]   		=	torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

         a__						:    int   		=	{"image_embeds": image_embeds, "hint": hint}
         a__						:    int   		=	self.unet(
             sample=snake_case       ,      timestep=snake_case       ,      encoder_hidden_states=snake_case       ,      added_cond_kwargs=snake_case       ,      return_dict=snake_case       ,      )[0]

         if do_classifier_free_guidance:
            a__		,  a__						:    Any   		=	noise_pred.split(latents.shape[1]       ,      dim=1   )
            a__		,  a__						:    List[str]   		=	noise_pred.chunk(2   )
            a__		,  a__						:    Union[str, Any]   		=	variance_pred.chunk(2   )
            a__						:    List[Any]   		=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
            a__						:    Any   		=	torch.cat([noise_pred, variance_pred_text]       ,      dim=1   )

         if not (
             hasattr(self.scheduler.config       ,      "variance_type"   )
             and self.scheduler.config.variance_type in ["learned", "learned_range"]
         ):
            a__		,  a__						:    Any   		=	noise_pred.split(latents.shape[1]       ,      dim=1   )

         # compute the previous noisy sample x_t -> x_t-1
         a__						:    List[Any]   		=	self.scheduler.step(
             snake_case       ,      snake_case       ,      snake_case       ,      generator=snake_case       ,      )[0]
      # post-processing
      a__						:    Any   		=	self.movq.decode(snake_case       ,      force_not_quantize=snake_case   )["sample"]

      if output_type not in ["pt", "np", "pil"]:
         raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""   )

      if output_type in ["np", "pil"]:
         a__						:    Tuple   		=	image * 0.5 + 0.5
         a__						:    Any   		=	image.clamp(0       ,      1   )
         a__						:    Optional[int]   		=	image.cpu().permute(0       ,      2       ,      3       ,      1   ).float().numpy()

      if output_type == "pil":
         a__						:    Any   		=	self.numpy_to_pil(snake_case   )

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=snake_case   )








import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


SCREAMING_SNAKE_CASE__							:      Optional[int]         =    logging.get_logger(__name__)







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Tuple  		=					["""input_values""", """attention_mask"""]





   def __init__(			self       ,      snake_case = 1       ,      snake_case = 16_000       ,      snake_case = 0.0       ,      snake_case = False       ,      snake_case = 80       ,      snake_case = 16       ,      snake_case = 64       ,      snake_case = "hann_window"       ,      snake_case = 1.0       ,      snake_case = 80       ,      snake_case = 7_600       ,      snake_case = 1E-10       ,      snake_case = 2       ,      snake_case = True       ,      **snake_case       ,      )					->	Dict:
      """simple docstring"""





      super().__init__(feature_size=snake_case       ,      sampling_rate=snake_case       ,      padding_value=snake_case       ,      **snake_case   )
      a__						:    Any   		=	do_normalize
      a__						:    List[str]   		=	return_attention_mask

      a__						:    List[Any]   		=	num_mel_bins
      a__						:    List[str]   		=	hop_length
      a__						:    int   		=	win_length
      a__						:    List[Any]   		=	win_function
      a__						:    List[str]   		=	frame_signal_scale
      a__						:    List[Any]   		=	fmin
      a__						:    Optional[Any]   		=	fmax
      a__						:    Union[str, Any]   		=	mel_floor
      a__						:    Union[str, Any]   		=	reduction_factor

      a__						:    List[str]   		=	win_length * sampling_rate // 1_000
      a__						:    List[Any]   		=	hop_length * sampling_rate // 1_000
      a__						:    List[Any]   		=	optimal_fft_length(self.sample_size   )
      a__						:    Dict   		=	(self.n_fft // 2) + 1

      a__						:    str   		=	window_function(window_length=self.sample_size       ,      name=self.win_function       ,      periodic=snake_case   )

      a__						:    Tuple   		=	mel_filter_bank(
          num_frequency_bins=self.n_freqs       ,      num_mel_filters=self.num_mel_bins       ,      min_frequency=self.fmin       ,      max_frequency=self.fmax       ,      sampling_rate=self.sampling_rate       ,      norm="slaney"       ,      mel_scale="slaney"       ,      )

      if frame_signal_scale != 1.0:
         warnings.warn(
             "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"       ,      snake_case       ,      )
      if reduction_factor != 2.0:
         warnings.warn(
             "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"       ,      snake_case       ,      )





   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def 						_snake_case			(			snake_case       ,      snake_case       ,      snake_case = 0.0   )					->	List[np.ndarray]:
      """simple docstring"""





      if attention_mask is not None:
         a__						:    Tuple   		=	np.array(snake_case       ,      np.intaa   )
         a__						:    List[str]   		=	[]

         for vector, length in zip(snake_case       ,      attention_mask.sum(-1   )   ):
            a__						:    List[Any]   		=	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7   )
            if length < normed_slice.shape[0]:
               a__						:    List[str]   		=	padding_value

            normed_input_values.append(snake_case   )
      else:
         a__						:    Any   		=	[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

      return normed_input_values





   def 						_snake_case			(			self       ,      snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      a__						:    str   		=	spectrogram(
          snake_case       ,      window=self.window       ,      frame_length=self.sample_size       ,      hop_length=self.sample_stride       ,      fft_length=self.n_fft       ,      mel_filters=self.mel_filters       ,      mel_floor=self.mel_floor       ,      log_mel="log10"       ,      )
      return log_mel_spec.T





   def __call__(			self       ,      snake_case = None       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      **snake_case       ,      )					->	BatchFeature:
      """simple docstring"""





      if audio is None and audio_target is None:
         raise ValueError("You must provide either `audio` or `audio_target` values."   )

      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 audio is not None:
         a__						:    Dict   		=	self._process_audio(
             snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      **snake_case       ,      )
      else:
         a__						:    Optional[int]   		=	None

      if audio_target is not None:
         a__						:    List[Any]   		=	self._process_audio(
             snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      **snake_case       ,      )

         if inputs is None:
            return inputs_target
         else:
            a__						:    Tuple   		=	inputs_target["input_values"]
            a__						:    Tuple   		=	inputs_target.get("attention_mask"   )
            if decoder_attention_mask is not None:
               a__						:    Tuple   		=	decoder_attention_mask

      return inputs





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = False       ,      snake_case = False       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      **snake_case       ,      )					->	BatchFeature:
      """simple docstring"""





      a__						:    Optional[int]   		=	isinstance(snake_case       ,      np.ndarray   ) and len(speech.shape   ) > 1
      if is_batched_numpy and len(speech.shape   ) > 2:
         raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""   )
      a__						:    List[Any]   		=	is_batched_numpy or (
          isinstance(snake_case       ,      (list, tuple)   ) and (isinstance(speech[0]       ,      (np.ndarray, tuple, list)   ))
      )

      if is_batched:
         a__						:    int   		=	[np.asarray(snake_case       ,      dtype=np.floataa   ) for speech in speech]
      elif not is_batched and not isinstance(snake_case       ,      np.ndarray   ):
         a__						:    Any   		=	np.asarray(snake_case       ,      dtype=np.floataa   )
      elif isinstance(snake_case       ,      np.ndarray   ) and speech.dtype is np.dtype(np.floataa   ):
         a__						:    List[Any]   		=	speech.astype(np.floataa   )

      # always return batch
      if not is_batched:
         a__						:    Union[str, Any]   		=	[speech]

      # needed to make pad() work on spectrogram inputs
      a__						:    Optional[Any]   		=	self.feature_size

      # convert into correct format for padding
      if is_target:
         a__						:    List[str]   		=	[self._extract_mel_features(snake_case   ) for waveform in speech]
         a__						:    Optional[Any]   		=	BatchFeature({"input_values": features}   )
         a__						:    str   		=	self.num_mel_bins
      else:
         a__						:    int   		=	BatchFeature({"input_values": speech}   )

      a__						:    int   		=	self.pad(
          snake_case       ,      padding=snake_case       ,      max_length=snake_case       ,      truncation=snake_case       ,      pad_to_multiple_of=snake_case       ,      return_attention_mask=snake_case       ,      **snake_case       ,      )

      a__						:    Any   		=	feature_size_hack

      # convert input values to correct format
      a__						:    Tuple   		=	padded_inputs["input_values"]
      if not isinstance(input_values[0]       ,      np.ndarray   ):
         a__						:    int   		=	[np.asarray(snake_case       ,      dtype=np.floataa   ) for array in input_values]
      elif (
          not isinstance(snake_case       ,      np.ndarray   )
          and isinstance(input_values[0]       ,      np.ndarray   )
          and input_values[0].dtype is np.dtype(np.floataa   )
      ):
         a__						:    Union[str, Any]   		=	[array.astype(np.floataa   ) for array in input_values]
      elif isinstance(snake_case       ,      np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
         a__						:    Optional[int]   		=	input_values.astype(np.floataa   )

      # convert attention_mask to correct format
      a__						:    Optional[int]   		=	padded_inputs.get("attention_mask"   )
      if attention_mask is not None:
         a__						:    Tuple   		=	[np.asarray(snake_case       ,      dtype=np.intaa   ) for array in attention_mask]

      # zero-mean and unit-variance normalization
      if not is_target and self.do_normalize:
         a__						:    Any   		=	(
             attention_mask
             if self._get_padding_strategies(snake_case       ,      max_length=snake_case   ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         a__						:    Optional[Any]   		=	self.zero_mean_unit_var_norm(
             padded_inputs["input_values"]       ,      attention_mask=snake_case       ,      padding_value=self.padding_value   )

      if return_tensors is not None:
         a__						:    int   		=	padded_inputs.convert_to_tensors(snake_case   )

      return padded_inputs





   def 						_snake_case			(			self   )					->	Dict[str, Any]:
      """simple docstring"""





      a__						:    int   		=	super().to_dict()

      # Don't serialize these as they are derived from the other properties.
      a__						:    str   		=	["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
      for name in names:
         if name in output:
            del output[name]

      return output

