

"""simple docstring"""

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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

SCREAMING_SNAKE_CASE_         =  '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> 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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.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)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

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




def       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   int	,			lowerCAmelCase			:   Tuple=8	)	-> Tuple:






    """simple docstring"""




    UpperCAmelCase       =   height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    UpperCAmelCase       =   width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Union[str, Any]=512	,			lowerCAmelCase			:   Optional[Any]=512	)	-> str:






    """simple docstring"""




    UpperCAmelCase       =   pil_image.resize((w, h)	,			resample=Image.BICUBIC	,			reducing_gap=1	)
    UpperCAmelCase       =   np.array(pil_image.convert('RGB'	)	)
    UpperCAmelCase       =   arr.astype(np.floataa	) / 127.5 - 1
    UpperCAmelCase       =   np.transpose(lowerCAmelCase	,			[2, 0, 1]	)
    UpperCAmelCase       =   torch.from_numpy(lowerCAmelCase	).unsqueeze(0	)
    return image







class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):




      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    )  ->   List[str]:
          super().__init__()

          self.register_modules(
              unet=lowercase_				,    scheduler=lowercase_				,    movq=lowercase_				,    )
          UpperCAmelCase       =   2 ** (len(self.movq.config.block_out_channels							) - 1)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Tuple:
          # get the original timestep using init_timestep
          UpperCAmelCase       =   min(int(num_inference_steps * strength							)				,    lowercase_							)

          UpperCAmelCase       =   max(num_inference_steps - init_timestep				,    0							)
          UpperCAmelCase       =   self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_=None							)  ->   Optional[int]:
          if not isinstance(lowercase_				,    (torch.Tensor, PIL.Image.Image, list)							):
              raise ValueError(
                  F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_							)}"							)

          UpperCAmelCase       =   image.to(device=lowercase_				,    dtype=lowercase_							)

          UpperCAmelCase       =   batch_size * num_images_per_prompt

          if image.shape[1] == 4:
              UpperCAmelCase       =   image

          else:
              if isinstance(lowercase_				,    lowercase_							) and len(lowercase_							) != batch_size:
                  raise ValueError(
                      F"You have passed a list of generators of length {len(lowercase_							)}, but requested an effective batch"
                      F" size of {batch_size}. Make sure the batch size matches the length of the generators."							)

              elif isinstance(lowercase_				,    lowercase_							):
                  UpperCAmelCase       =   [
                      self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(lowercase_							)
                  ]
                  UpperCAmelCase       =   torch.cat(lowercase_				,    dim=0							)
              else:
                  UpperCAmelCase       =   self.movq.encode(lowercase_							).latent_dist.sample(lowercase_							)

              UpperCAmelCase       =   self.movq.config.scaling_factor * init_latents

          UpperCAmelCase       =   torch.cat([init_latents]				,    dim=0							)

          UpperCAmelCase       =   init_latents.shape
          UpperCAmelCase       =   randn_tensor(lowercase_				,    generator=lowercase_				,    device=lowercase_				,    dtype=lowercase_							)

          # get latents
          UpperCAmelCase       =   self.scheduler.add_noise(lowercase_				,    lowercase_				,    lowercase_							)

          UpperCAmelCase       =   init_latents

          return latents




      def       a_ (							self				,    lowercase_=0							)  ->   Optional[int]:
          if is_accelerate_available():
              from accelerate import cpu_offload
          else:
              raise ImportError('Please install accelerate via `pip install accelerate`'							)

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

          UpperCAmelCase       =   [
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                  cpu_offload(lowercase_				,    lowercase_							)




      def       a_ (							self				,    lowercase_=0							)  ->   Tuple:
          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.'							)

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

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

          UpperCAmelCase       =   None
          for cpu_offloaded_model in [self.unet, self.movq]:
              UpperCAmelCase     ,							UpperCAmelCase       =   cpu_offload_with_hook(lowercase_				,    lowercase_				,    prev_module_hook=lowercase_							)

          # We'll offload the last model manually.
          UpperCAmelCase       =   hook




      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def       a_ (							self							)  ->   Optional[int]:
          if not hasattr(self.unet				,    '_hf_hook'							):
              return self.device
          for module in self.unet.modules():
              if (
                  hasattr(lowercase_				,    '_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(lowercase_							)
      def __call__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = 5_1_2				,    lowercase_ = 5_1_2				,    lowercase_ = 1_0_0				,    lowercase_ = 4.0				,    lowercase_ = 0.3				,    lowercase_ = 1				,    lowercase_ = None				,    lowercase_ = "pil"				,    lowercase_ = True				,    )  ->   Dict:
          UpperCAmelCase       =   self._execution_device

          UpperCAmelCase       =   guidance_scale > 1.0

          if isinstance(lowercase_				,    lowercase_							):
              UpperCAmelCase       =   torch.cat(lowercase_				,    dim=0							)
          if isinstance(lowercase_				,    lowercase_							):
              UpperCAmelCase       =   torch.cat(lowercase_				,    dim=0							)
          if isinstance(lowercase_				,    lowercase_							):
              UpperCAmelCase       =   torch.cat(lowercase_				,    dim=0							)

          UpperCAmelCase       =   image_embeds.shape[0]

          if do_classifier_free_guidance:
              UpperCAmelCase       =   image_embeds.repeat_interleave(lowercase_				,    dim=0							)
              UpperCAmelCase       =   negative_image_embeds.repeat_interleave(lowercase_				,    dim=0							)
              UpperCAmelCase       =   hint.repeat_interleave(lowercase_				,    dim=0							)

          UpperCAmelCase       =   torch.cat([negative_image_embeds, image_embeds]				,    dim=0							).to(dtype=self.unet.dtype				,    device=lowercase_							)
          UpperCAmelCase       =   torch.cat([hint, hint]				,    dim=0							).to(dtype=self.unet.dtype				,    device=lowercase_							)

          if not isinstance(lowercase_				,    lowercase_							):
              UpperCAmelCase       =   [image]
          if not all(isinstance(lowercase_				,    (PIL.Image.Image, torch.Tensor)							) for i in image							):
              raise ValueError(
                  F"Input is in incorrect format: {[type(lowercase_							) for i in image]}. Currently, we only support  PIL image and pytorch tensor"							)

          UpperCAmelCase       =   torch.cat([prepare_image(lowercase_				,    lowercase_				,    lowercase_							) for i in image]				,    dim=0							)
          UpperCAmelCase       =   image.to(dtype=image_embeds.dtype				,    device=lowercase_							)

          UpperCAmelCase       =   self.movq.encode(lowercase_							)['latents']
          UpperCAmelCase       =   latents.repeat_interleave(lowercase_				,    dim=0							)
          self.scheduler.set_timesteps(lowercase_				,    device=lowercase_							)
          UpperCAmelCase     ,							UpperCAmelCase       =   self.get_timesteps(lowercase_				,    lowercase_				,    lowercase_							)
          UpperCAmelCase       =   timesteps[:1].repeat(batch_size * num_images_per_prompt							)
          UpperCAmelCase     ,							UpperCAmelCase       =   downscale_height_and_width(lowercase_				,    lowercase_				,    self.movq_scale_factor							)
          UpperCAmelCase       =   self.prepare_latents(
              lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    image_embeds.dtype				,    lowercase_				,    lowercase_							)
          for i, t in enumerate(self.progress_bar(lowercase_							)							):
              # expand the latents if we are doing classifier free guidance
              UpperCAmelCase       =   torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

              UpperCAmelCase       =   {'image_embeds': image_embeds, 'hint': hint}
              UpperCAmelCase       =   self.unet(
                  sample=lowercase_				,    timestep=lowercase_				,    encoder_hidden_states=lowercase_				,    added_cond_kwargs=lowercase_				,    return_dict=lowercase_				,    )[0]

              if do_classifier_free_guidance:
                  UpperCAmelCase     ,							UpperCAmelCase       =   noise_pred.split(latents.shape[1]				,    dim=1							)
                  UpperCAmelCase     ,							UpperCAmelCase       =   noise_pred.chunk(2							)
                  UpperCAmelCase     ,							UpperCAmelCase       =   variance_pred.chunk(2							)
                  UpperCAmelCase       =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                  UpperCAmelCase       =   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"]
              ):
                  UpperCAmelCase     ,							UpperCAmelCase       =   noise_pred.split(latents.shape[1]				,    dim=1							)

              # compute the previous noisy sample x_t -> x_t-1

              UpperCAmelCase       =   self.scheduler.step(
                  lowercase_				,    lowercase_				,    lowercase_				,    generator=lowercase_				,    )[0]

          # post-processing
          UpperCAmelCase       =   self.movq.decode(lowercase_				,    force_not_quantize=lowercase_							)['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"]:
              UpperCAmelCase       =   image * 0.5 + 0.5
              UpperCAmelCase       =   image.clamp(0				,    1							)
              UpperCAmelCase       =   image.cpu().permute(0				,    2				,    3				,    1							).float().numpy()

          if output_type == "pil":
              UpperCAmelCase       =   self.numpy_to_pil(lowercase_							)

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowercase_							)





"""simple docstring"""

import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)




def       lowercase__  (   lowerCAmelCase			:   bytes	,			lowerCAmelCase			:   int	)	-> np.array:






    """simple docstring"""




    UpperCAmelCase       =   F"{sampling_rate}"
    UpperCAmelCase       =   '1'
    UpperCAmelCase       =   'f32le'
    UpperCAmelCase       =   [
        'ffmpeg',
        '-i',
        'pipe:0',
        '-ac',
        ac,
        '-ar',
        ar,
        '-f',
        format_for_conversion,
        '-hide_banner',
        '-loglevel',
        'quiet',
        'pipe:1',
    ]

    try:
        UpperCAmelCase       =   subprocess.Popen(lowerCAmelCase	,			stdin=subprocess.PIPE	,			stdout=subprocess.PIPE	)
    except FileNotFoundError:
        raise ValueError('ffmpeg was not found but is required to load audio files from filename'	)
    UpperCAmelCase       =   ffmpeg_process.communicate(lowerCAmelCase	)
    UpperCAmelCase       =   output_stream[0]

    UpperCAmelCase       =   np.frombuffer(lowerCAmelCase	,			np.floataa	)
    if audio.shape[0] == 0:
        raise ValueError('Malformed soundfile'	)
    return audio







@add_end_docstrings(SCREAMING_SNAKE_CASE_							)
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):




      def __init__(							self				,    *lowercase_				,    **lowercase_							)  ->   List[Any]:
          # Default, might be overriden by the model.config.
          UpperCAmelCase       =   5
          super().__init__(*lowercase_				,    **lowercase_							)

          if self.framework != "pt":
              raise ValueError(F"The {self.__class__} is only available in PyTorch."							)

          self.check_model_type(lowercase_							)




      def __call__(							self				,    lowercase_				,    **lowercase_				,    )  ->   Tuple:
          return super().__call__(lowercase_				,    **lowercase_							)




      def       a_ (							self				,    lowercase_=None				,    **lowercase_							)  ->   Optional[Any]:
          # No parameters on this pipeline right now
          UpperCAmelCase       =   {}
          if top_k is not None:
              if top_k > self.model.config.num_labels:
                  UpperCAmelCase       =   self.model.config.num_labels
              UpperCAmelCase       =   top_k
          return {}, {}, postprocess_params




      def       a_ (							self				,    lowercase_							)  ->   List[str]:
          if isinstance(lowercase_				,    lowercase_							):
              if inputs.startswith('http://'							) or inputs.startswith('https://'							):
                  # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                  # like http_huggingface_co.png
                  UpperCAmelCase       =   requests.get(lowercase_							).content
              else:
                  with open(lowercase_				,    'rb'							) as f:
                      UpperCAmelCase       =   f.read()

          if isinstance(lowercase_				,    lowercase_							):
              UpperCAmelCase       =   ffmpeg_read(lowercase_				,    self.feature_extractor.sampling_rate							)

          if isinstance(lowercase_				,    lowercase_							):
              # Accepting `"array"` which is the key defined in `datasets` for
              # better integration
              if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                  raise ValueError(
                      'When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '
                      '"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
                      'containing the sampling_rate associated with that array'							)

              UpperCAmelCase       =   inputs.pop('raw'				,    lowercase_							)
              if _inputs is None:
                  # Remove path which will not be used from `datasets`.
                  inputs.pop('path'				,    lowercase_							)
                  UpperCAmelCase       =   inputs.pop('array'				,    lowercase_							)
              UpperCAmelCase       =   inputs.pop('sampling_rate'							)
              UpperCAmelCase       =   _inputs
              if in_sampling_rate != self.feature_extractor.sampling_rate:
                  import torch

                  if is_torchaudio_available():
                      from torchaudio import functional as F
                  else:
                      raise ImportError(
                          'torchaudio is required to resample audio samples in AudioClassificationPipeline. '
                          'The torchaudio package can be installed through: `pip install torchaudio`.'							)

                  UpperCAmelCase       =   F.resample(
                      torch.from_numpy(lowercase_							)				,    lowercase_				,    self.feature_extractor.sampling_rate							).numpy()

          if not isinstance(lowercase_				,    np.ndarray							):
              raise ValueError('We expect a numpy ndarray as input'							)
          if len(inputs.shape							) != 1:
              raise ValueError('We expect a single channel audio input for AudioClassificationPipeline'							)

          UpperCAmelCase       =   self.feature_extractor(
              lowercase_				,    sampling_rate=self.feature_extractor.sampling_rate				,    return_tensors='pt'							)
          return processed




      def       a_ (							self				,    lowercase_							)  ->   Any:
          UpperCAmelCase       =   self.model(**lowercase_							)
          return model_outputs




      def       a_ (							self				,    lowercase_				,    lowercase_=5							)  ->   int:
          UpperCAmelCase       =   model_outputs.logits[0].softmax(-1							)
          UpperCAmelCase     ,							UpperCAmelCase       =   probs.topk(lowercase_							)

          UpperCAmelCase       =   scores.tolist()
          UpperCAmelCase       =   ids.tolist()

          UpperCAmelCase       =   [{'score': score, 'label': self.model.config.idalabel[_id]} for score, _id in zip(lowercase_				,    lowercase_							)]

          return labels



