



"""simple docstring"""
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,
)


A:  Optional[Any]      				=						logging.get_logger(__name__)  # pylint: disable=invalid-name

A:  Optional[Any]      				=						"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"

def  _snake_case		(		UpperCamelCase		:						List[str]     ,						UpperCamelCase		:						int     ,						UpperCamelCase		:						List[Any]=8		):
 UpperCAmelCase      :   int      =      height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 UpperCAmelCase      :   int      =      width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor



class  SCREAMING_SNAKE_CASE__    (   UpperCAmelCase__					):

   def __init__(  self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	)    -> Optional[Any]:





    '''simple docstring'''




    super().__init__()

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

   def       SCREAMING_SNAKE_CASE	(  self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE		)    -> Union[str, Any]:





    '''simple docstring'''




    if latents is None:
     UpperCAmelCase      :   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}"		)
     UpperCAmelCase      :   List[Any]      =      latents.to(snake_case__		)

    UpperCAmelCase      :   Any      =      latents * scheduler.init_noise_sigma
    return latents

   def       SCREAMING_SNAKE_CASE	(  self ,	_SCREAMING_SNAKE_CASE=0		)    -> Any:





    '''simple docstring'''




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

    UpperCAmelCase      :   List[str]      =      torch.device(F"cuda:{gpu_id}"		)

    UpperCAmelCase      :   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       SCREAMING_SNAKE_CASE	(  self ,	_SCREAMING_SNAKE_CASE=0		)    -> Optional[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."""		)

    UpperCAmelCase      :   str      =      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)

    UpperCAmelCase      :   Optional[int]      =      None
    for cpu_offloaded_model in [self.unet, self.movq]:
     UpperCAmelCase      :   List[str]      =      cpu_offload_with_hook(snake_case__ ,	snake_case__ ,	prev_module_hook=snake_case__		)

    # We'll offload the last model manually.
    UpperCAmelCase      :   List[Any]      =      hook

   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def       SCREAMING_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 ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = 512 ,	_SCREAMING_SNAKE_CASE = 512 ,	_SCREAMING_SNAKE_CASE = 100 ,	_SCREAMING_SNAKE_CASE = 4.0 ,	_SCREAMING_SNAKE_CASE = 1 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = "pil" ,	_SCREAMING_SNAKE_CASE = True ,	)    -> int:





    '''simple docstring'''




    UpperCAmelCase      :   str      =      self._execution_device

    UpperCAmelCase      :   Dict      =      guidance_scale > 1.0

    if isinstance(snake_case__ ,	snake_case__		):
     UpperCAmelCase      :   List[str]      =      torch.cat(snake_case__ ,	dim=0		)
    if isinstance(snake_case__ ,	snake_case__		):
     UpperCAmelCase      :   List[Any]      =      torch.cat(snake_case__ ,	dim=0		)
    if isinstance(snake_case__ ,	snake_case__		):
     UpperCAmelCase      :   Tuple      =      torch.cat(snake_case__ ,	dim=0		)

    UpperCAmelCase      :   Dict      =      image_embeds.shape[0] * num_images_per_prompt

    if do_classifier_free_guidance:
     UpperCAmelCase      :   Dict      =      image_embeds.repeat_interleave(snake_case__ ,	dim=0		)
     UpperCAmelCase      :   Tuple      =      negative_image_embeds.repeat_interleave(snake_case__ ,	dim=0		)
     UpperCAmelCase      :   str      =      hint.repeat_interleave(snake_case__ ,	dim=0		)

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

    UpperCAmelCase      :   Tuple      =      self.movq.config.latent_channels

    UpperCAmelCase      :   int      =      downscale_height_and_width(snake_case__ ,	snake_case__ ,	self.movq_scale_factor		)

    # create initial latent
    UpperCAmelCase      :   Tuple      =      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
     UpperCAmelCase      :   Any      =      torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

     UpperCAmelCase      :   Optional[int]      =      {"image_embeds": image_embeds, "hint": hint}
     UpperCAmelCase      :   Dict      =      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:
      UpperCAmelCase      :   Any      =      noise_pred.split(latents.shape[1] ,	dim=1		)
      UpperCAmelCase      :   List[Any]      =      noise_pred.chunk(2		)
      UpperCAmelCase      :   Dict      =      variance_pred.chunk(2		)
      UpperCAmelCase      :   int      =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
      UpperCAmelCase      :   List[str]      =      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      :   Any      =      noise_pred.split(latents.shape[1] ,	dim=1		)

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase      :   List[str]      =      self.scheduler.step(
         snake_case__ ,	snake_case__ ,	snake_case__ ,	generator=snake_case__ ,	)[0]
    # post-processing
    UpperCAmelCase      :   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"]:
     UpperCAmelCase      :   int      =      image * 0.5 + 0.5
     UpperCAmelCase      :   Optional[int]      =      image.clamp(0 ,	1		)
     UpperCAmelCase      :   List[Any]      =      image.cpu().permute(0 ,	2 ,	3 ,	1		).float().numpy()

    if output_type == "pil":
     UpperCAmelCase      :   List[Any]      =      self.numpy_to_pil(snake_case__		)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=snake_case__		)


'''simple docstring'''





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     =       {
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    List[str]				=							"wavlm"







 def __init__(					self		:  Optional[int]			,				snake_case__		:  Dict=32			,				snake_case__		:  Optional[Any]=7_68			,				snake_case__		:  Tuple=12			,				snake_case__		:  List[Any]=12			,				snake_case__		:  List[str]=30_72			,				snake_case__		:  Tuple="gelu"			,				snake_case__		:  Optional[Any]=0.1			,				snake_case__		:  List[str]=0.1			,				snake_case__		:  Any=0.1			,				snake_case__		:  List[Any]=0.0			,				snake_case__		:  Dict=0.1			,				snake_case__		:  List[Any]=0.1			,				snake_case__		:  Union[str, Any]=0.02			,				snake_case__		:  int=1e-5			,				snake_case__		:  Dict="group"			,				snake_case__		:  Any="gelu"			,				snake_case__		:  List[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)			,				snake_case__		:  int=(5, 2, 2, 2, 2, 2, 2)			,				snake_case__		:  Dict=(10, 3, 3, 3, 3, 2, 2)			,				snake_case__		:  Tuple=False			,				snake_case__		:  int=1_28			,				snake_case__		:  Dict=16			,				snake_case__		:  int=3_20			,				snake_case__		:  Dict=8_00			,				snake_case__		:  List[str]=False			,				snake_case__		:  Union[str, Any]=True			,				snake_case__		:  str=0.05			,				snake_case__		:  List[Any]=10			,				snake_case__		:  Tuple=2			,				snake_case__		:  Optional[int]=0.0			,				snake_case__		:  int=10			,				snake_case__		:  Any=3_20			,				snake_case__		:  Dict=2			,				snake_case__		:  Optional[int]=0.1			,				snake_case__		:  Union[str, Any]=1_00			,				snake_case__		:  Dict=2_56			,				snake_case__		:  List[Any]=2_56			,				snake_case__		:  List[str]=0.1			,				snake_case__		:  List[Any]="mean"			,				snake_case__		:  int=False			,				snake_case__		:  Any=False			,				snake_case__		:  Any=2_56			,				snake_case__		:  Union[str, Any]=(5_12, 5_12, 5_12, 5_12, 15_00)			,				snake_case__		:  Union[str, Any]=(5, 3, 3, 1, 1)			,				snake_case__		:  Optional[Any]=(1, 2, 3, 1, 1)			,				snake_case__		:  List[Any]=5_12			,				snake_case__		:  str=80			,				snake_case__		:  Any=0			,				snake_case__		:  str=1			,				snake_case__		:  Union[str, Any]=2			,				snake_case__		:  int=False			,				snake_case__		:  Union[str, Any]=3			,				snake_case__		:  Union[str, Any]=2			,				snake_case__		:  List[str]=3			,				snake_case__		:  List[Any]=None			,				**snake_case__		:  int			,				):



   '''simple docstring'''




   super().__init__(**snake_case__			,				pad_token_id=snake_case__			,				bos_token_id=snake_case__			,				eos_token_id=snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							hidden_size
   UpperCAmelCase__      :					List[str]				       =							feat_extract_norm
   UpperCAmelCase__      :					int				       =							feat_extract_activation
   UpperCAmelCase__      :					Optional[int]				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							conv_bias
   UpperCAmelCase__      :					List[Any]				       =							num_buckets
   UpperCAmelCase__      :					Union[str, Any]				       =							max_bucket_distance
   UpperCAmelCase__      :					Tuple				       =							num_conv_pos_embeddings
   UpperCAmelCase__      :					Union[str, Any]				       =							num_conv_pos_embedding_groups
   UpperCAmelCase__      :					Optional[Any]				       =							len(self.conv_dim )
   UpperCAmelCase__      :					Optional[int]				       =							num_hidden_layers
   UpperCAmelCase__      :					Tuple				       =							intermediate_size
   UpperCAmelCase__      :					List[str]				       =							hidden_act
   UpperCAmelCase__      :					List[str]				       =							num_attention_heads
   UpperCAmelCase__      :					Any				       =							hidden_dropout
   UpperCAmelCase__      :					List[Any]				       =							attention_dropout
   UpperCAmelCase__      :					Optional[int]				       =							activation_dropout
   UpperCAmelCase__      :					Optional[Any]				       =							feat_proj_dropout
   UpperCAmelCase__      :					List[str]				       =							final_dropout
   UpperCAmelCase__      :					int				       =							layerdrop
   UpperCAmelCase__      :					int				       =							layer_norm_eps
   UpperCAmelCase__      :					Tuple				       =							initializer_range
   UpperCAmelCase__      :					Union[str, Any]				       =							num_ctc_classes
   UpperCAmelCase__      :					Any				       =							vocab_size
   UpperCAmelCase__      :					Dict				       =							do_stable_layer_norm
   UpperCAmelCase__      :					List[Any]				       =							use_weighted_layer_sum
   UpperCAmelCase__      :					Optional[Any]				       =							classifier_proj_size

   if (
       (len(self.conv_stride ) != self.num_feat_extract_layers)
       or (len(self.conv_kernel ) != self.num_feat_extract_layers)
       or (len(self.conv_dim ) != self.num_feat_extract_layers)
   ):
     raise ValueError(
         "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
         " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
         F""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
         F""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )

   # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
   UpperCAmelCase__      :					List[str]				       =							apply_spec_augment
   UpperCAmelCase__      :					Union[str, Any]				       =							mask_time_prob
   UpperCAmelCase__      :					List[Any]				       =							mask_time_length
   UpperCAmelCase__      :					List[str]				       =							mask_time_min_masks
   UpperCAmelCase__      :					List[str]				       =							mask_feature_prob
   UpperCAmelCase__      :					Dict				       =							mask_feature_length

   # parameters for pretraining with codevector quantized representations
   UpperCAmelCase__      :					Dict				       =							num_codevectors_per_group
   UpperCAmelCase__      :					Union[str, Any]				       =							num_codevector_groups
   UpperCAmelCase__      :					int				       =							contrastive_logits_temperature
   UpperCAmelCase__      :					List[str]				       =							num_negatives
   UpperCAmelCase__      :					List[str]				       =							codevector_dim
   UpperCAmelCase__      :					List[Any]				       =							proj_codevector_dim
   UpperCAmelCase__      :					List[Any]				       =							diversity_loss_weight

   # ctc loss
   UpperCAmelCase__      :					Tuple				       =							ctc_loss_reduction
   UpperCAmelCase__      :					Any				       =							ctc_zero_infinity

   # adapter
   UpperCAmelCase__      :					int				       =							add_adapter
   UpperCAmelCase__      :					List[Any]				       =							adapter_kernel_size
   UpperCAmelCase__      :					Dict				       =							adapter_stride
   UpperCAmelCase__      :					Dict				       =							num_adapter_layers
   UpperCAmelCase__      :					Union[str, Any]				       =							output_hidden_size or hidden_size

   # SequenceClassification-specific parameter. Feel free to ignore for other classes.
   UpperCAmelCase__      :					str				       =							classifier_proj_size

   # XVector-specific parameters. Feel free to ignore for other classes.
   UpperCAmelCase__      :					Any				       =							list(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							list(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							list(snake_case__ )
   UpperCAmelCase__      :					Dict				       =							xvector_output_dim







 @property
 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   return functools.reduce(operator.mul			,				self.conv_stride			,				1 )



