





import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput




class  __UpperCamelCase						(       nn.Module						):





     def __init__(    self							,       _UpperCamelCase							,       _UpperCamelCase=1.0				):
        super().__init__()

        _UpperCAmelCase											=      cut_size
        _UpperCAmelCase											=      cut_power





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase				):
        _UpperCAmelCase					,			_UpperCAmelCase											=      pixel_values.shape[2:4]
        _UpperCAmelCase											=      min(_UpperCamelCase							,       _UpperCamelCase				)
        _UpperCAmelCase											=      min(_UpperCamelCase							,       _UpperCamelCase							,       self.cut_size				)
        _UpperCAmelCase											=      []
        for _ in range(_UpperCamelCase				):
           _UpperCAmelCase											=      int(torch.rand([]				) ** self.cut_power * (max_size - min_size) + min_size				)
           _UpperCAmelCase											=      torch.randint(0							,       sideX - size + 1							,       ()				)
           _UpperCAmelCase											=      torch.randint(0							,       sideY - size + 1							,       ()				)
           _UpperCAmelCase											=      pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
           cutouts.append(F.adaptive_avg_poolad(_UpperCamelCase							,       self.cut_size				)				)
        return torch.cat(_UpperCamelCase				)




def     A__					(						SCREAMING_SNAKE_CASE_      :			Tuple							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase											=      F.normalize(SCREAMING_SNAKE_CASE_							,  dim=-1	)
   _UpperCAmelCase											=      F.normalize(SCREAMING_SNAKE_CASE_							,  dim=-1	)
   return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)
def     A__					(						SCREAMING_SNAKE_CASE_      :			Tuple							,  SCREAMING_SNAKE_CASE_      :			Optional[Any]	)     ->     str:






   """simple docstring"""







   for param in model.parameters():
      _UpperCAmelCase											=      value




class  __UpperCamelCase						(       A__						):





     def __init__(    self							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       ):
        super().__init__()
        self.register_modules(
            vae=_UpperCamelCase							,       text_encoder=_UpperCamelCase							,       clip_model=_UpperCamelCase							,       tokenizer=_UpperCamelCase							,       unet=_UpperCamelCase							,       scheduler=_UpperCamelCase							,       feature_extractor=_UpperCamelCase							,       )

        _UpperCAmelCase											=      transforms.Normalize(mean=feature_extractor.image_mean							,       std=feature_extractor.image_std				)
        _UpperCAmelCase											=      (
            feature_extractor.size
            if isinstance(feature_extractor.size							,       _UpperCamelCase				)
            else feature_extractor.size['''shortest_edge''']
        )
        _UpperCAmelCase											=      MakeCutouts(self.cut_out_size				)

        set_requires_grad(self.text_encoder							,       _UpperCamelCase				)
        set_requires_grad(self.clip_model							,       _UpperCamelCase				)





     def 		UpperCamelCase(    self							,       _UpperCamelCase = "auto"				):
        if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           _UpperCAmelCase											=      self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(_UpperCamelCase				)





     def 		UpperCamelCase(    self				):
        self.enable_attention_slicing(_UpperCamelCase				)





     def 		UpperCamelCase(    self				):
        set_requires_grad(self.vae							,       _UpperCamelCase				)





     def 		UpperCamelCase(    self				):
        set_requires_grad(self.vae							,       _UpperCamelCase				)





     def 		UpperCamelCase(    self				):
        set_requires_grad(self.unet							,       _UpperCamelCase				)





     def 		UpperCamelCase(    self				):
        set_requires_grad(self.unet							,       _UpperCamelCase				)





     @torch.enable_grad()
     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase=True							,       ):
        _UpperCAmelCase											=      latents.detach().requires_grad_()

        _UpperCAmelCase											=      self.scheduler.scale_model_input(_UpperCamelCase							,       _UpperCamelCase				)

        # predict the noise residual
        _UpperCAmelCase											=      self.unet(_UpperCamelCase							,       _UpperCamelCase							,       encoder_hidden_states=_UpperCamelCase				).sample

        if isinstance(self.scheduler							,       (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)				):
           _UpperCAmelCase											=      self.scheduler.alphas_cumprod[timestep]
           _UpperCAmelCase											=      1 - alpha_prod_t
           # compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           _UpperCAmelCase											=      (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

           _UpperCAmelCase											=      torch.sqrt(_UpperCamelCase				)
           _UpperCAmelCase											=      pred_original_sample * (fac) + latents * (1 - fac)
        elif isinstance(self.scheduler							,       _UpperCamelCase				):
           _UpperCAmelCase											=      self.scheduler.sigmas[index]
           _UpperCAmelCase											=      latents - sigma * noise_pred
        else:
           raise ValueError(f'''scheduler type {type(self.scheduler				)} not supported'''				)

        _UpperCAmelCase											=      1 / self.vae.config.scaling_factor * sample
        _UpperCAmelCase											=      self.vae.decode(_UpperCamelCase				).sample
        _UpperCAmelCase											=      (image / 2 + 0.5).clamp(0							,       1				)

        if use_cutouts:
           _UpperCAmelCase											=      self.make_cutouts(_UpperCamelCase							,       _UpperCamelCase				)
        else:
           _UpperCAmelCase											=      transforms.Resize(self.cut_out_size				)(_UpperCamelCase				)
        _UpperCAmelCase											=      self.normalize(_UpperCamelCase				).to(latents.dtype				)

        _UpperCAmelCase											=      self.clip_model.get_image_features(_UpperCamelCase				)
        _UpperCAmelCase											=      image_embeddings_clip / image_embeddings_clip.norm(p=2							,       dim=-1							,       keepdim=_UpperCamelCase				)

        if use_cutouts:
           _UpperCAmelCase											=      spherical_dist_loss(_UpperCamelCase							,       _UpperCamelCase				)
           _UpperCAmelCase											=      dists.view([num_cutouts, sample.shape[0], -1]				)
           _UpperCAmelCase											=      dists.sum(2				).mean(0				).sum() * clip_guidance_scale
        else:
           _UpperCAmelCase											=      spherical_dist_loss(_UpperCamelCase							,       _UpperCamelCase				).mean() * clip_guidance_scale

        _UpperCAmelCase											=      -torch.autograd.grad(_UpperCamelCase							,       _UpperCamelCase				)[0]

        if isinstance(self.scheduler							,       _UpperCamelCase				):
           _UpperCAmelCase											=      latents.detach() + grads * (sigma**2)
           _UpperCAmelCase											=      noise_pred_original
        else:
           _UpperCAmelCase											=      noise_pred_original - torch.sqrt(_UpperCamelCase				) * grads
        return noise_pred, latents





     @torch.no_grad()
     def __call__(    self							,       _UpperCamelCase							,       _UpperCamelCase = 512							,       _UpperCamelCase = 512							,       _UpperCamelCase = 50							,       _UpperCamelCase = 7.5							,       _UpperCamelCase = 1							,       _UpperCamelCase = 0.0							,       _UpperCamelCase = 100							,       _UpperCamelCase = None							,       _UpperCamelCase = 4							,       _UpperCamelCase = True							,       _UpperCamelCase = None							,       _UpperCamelCase = None							,       _UpperCamelCase = "pil"							,       _UpperCamelCase = True							,       ):
        if isinstance(_UpperCamelCase							,       _UpperCamelCase				):
           _UpperCAmelCase											=      1
        elif isinstance(_UpperCamelCase							,       _UpperCamelCase				):
           _UpperCAmelCase											=      len(_UpperCamelCase				)
        else:
           raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase				)}'''				)

        if height % 8 != 0 or width % 8 != 0:
           raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''				)

        # get prompt text embeddings
        _UpperCAmelCase											=      self.tokenizer(
            _UpperCamelCase							,       padding='''max_length'''							,       max_length=self.tokenizer.model_max_length							,       truncation=_UpperCamelCase							,       return_tensors='''pt'''							,       )
        _UpperCAmelCase											=      self.text_encoder(text_input.input_ids.to(self.device				)				)[0]
        # duplicate text embeddings for each generation per prompt
        _UpperCAmelCase											=      text_embeddings.repeat_interleave(_UpperCamelCase							,       dim=0				)

        if clip_guidance_scale > 0:
           if clip_prompt is not None:
              _UpperCAmelCase											=      self.tokenizer(
                  _UpperCamelCase							,       padding='''max_length'''							,       max_length=self.tokenizer.model_max_length							,       truncation=_UpperCamelCase							,       return_tensors='''pt'''							,       ).input_ids.to(self.device				)
           else:
              _UpperCAmelCase											=      text_input.input_ids.to(self.device				)
           _UpperCAmelCase											=      self.clip_model.get_text_features(_UpperCamelCase				)
           _UpperCAmelCase											=      text_embeddings_clip / text_embeddings_clip.norm(p=2							,       dim=-1							,       keepdim=_UpperCamelCase				)
           # duplicate text embeddings clip for each generation per prompt
           _UpperCAmelCase											=      text_embeddings_clip.repeat_interleave(_UpperCamelCase							,       dim=0				)

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        _UpperCAmelCase											=      guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
           _UpperCAmelCase											=      text_input.input_ids.shape[-1]
           _UpperCAmelCase											=      self.tokenizer(['''''']							,       padding='''max_length'''							,       max_length=_UpperCamelCase							,       return_tensors='''pt'''				)
           _UpperCAmelCase											=      self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
           # duplicate unconditional embeddings for each generation per prompt
           _UpperCAmelCase											=      uncond_embeddings.repeat_interleave(_UpperCamelCase							,       dim=0				)

           # For classifier free guidance, we need to do two forward passes.
           # Here we concatenate the unconditional and text embeddings into a single batch
           # to avoid doing two forward passes
           _UpperCAmelCase											=      torch.cat([uncond_embeddings, text_embeddings]				)

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        _UpperCAmelCase											=      (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        _UpperCAmelCase											=      text_embeddings.dtype
        if latents is None:
           if self.device.type == "mps":
              # randn does not work reproducibly on mps
              _UpperCAmelCase											=      torch.randn(_UpperCamelCase							,       generator=_UpperCamelCase							,       device='''cpu'''							,       dtype=_UpperCamelCase				).to(
                  self.device				)
           else:
              _UpperCAmelCase											=      torch.randn(_UpperCamelCase							,       generator=_UpperCamelCase							,       device=self.device							,       dtype=_UpperCamelCase				)
        else:
           if latents.shape != latents_shape:
              raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''				)
           _UpperCAmelCase											=      latents.to(self.device				)

        # set timesteps
        _UpperCAmelCase											=      '''offset''' in set(inspect.signature(self.scheduler.set_timesteps				).parameters.keys()				)
        _UpperCAmelCase											=      {}
        if accepts_offset:
           _UpperCAmelCase											=      1

        self.scheduler.set_timesteps(_UpperCamelCase							,       **_UpperCamelCase				)

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        _UpperCAmelCase											=      self.scheduler.timesteps.to(self.device				)

        # scale the initial noise by the standard deviation required by the scheduler
        _UpperCAmelCase											=      latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        _UpperCAmelCase											=      '''eta''' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
        _UpperCAmelCase											=      {}
        if accepts_eta:
           _UpperCAmelCase											=      eta

        # check if the scheduler accepts generator
        _UpperCAmelCase											=      '''generator''' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
        if accepts_generator:
           _UpperCAmelCase											=      generator

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

           # predict the noise residual
           _UpperCAmelCase											=      self.unet(_UpperCamelCase							,       _UpperCamelCase							,       encoder_hidden_states=_UpperCamelCase				).sample

           # perform classifier free guidance
           if do_classifier_free_guidance:
              _UpperCAmelCase					,			_UpperCAmelCase											=      noise_pred.chunk(2				)
              _UpperCAmelCase											=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

           # perform clip guidance
           if clip_guidance_scale > 0:
              _UpperCAmelCase											=      (
                  text_embeddings.chunk(2				)[1] if do_classifier_free_guidance else text_embeddings
              )
              _UpperCAmelCase					,			_UpperCAmelCase											=      self.cond_fn(
                  _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       )

           # compute the previous noisy sample x_t -> x_t-1
           _UpperCAmelCase											=      self.scheduler.step(_UpperCamelCase							,       _UpperCamelCase							,       _UpperCamelCase							,       **_UpperCamelCase				).prev_sample

        # scale and decode the image latents with vae
        _UpperCAmelCase											=      1 / self.vae.config.scaling_factor * latents
        _UpperCAmelCase											=      self.vae.decode(_UpperCamelCase				).sample

        _UpperCAmelCase											=      (image / 2 + 0.5).clamp(0							,       1				)
        _UpperCAmelCase											=      image.cpu().permute(0							,       2							,       3							,       1				).numpy()

        if output_type == "pil":
           _UpperCAmelCase											=      self.numpy_to_pil(_UpperCamelCase				)

        if not return_dict:
           return (image, None)

        return StableDiffusionPipelineOutput(images=_UpperCamelCase							,       nsfw_content_detected=_UpperCamelCase				)






import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_											=  logging.get_logger(__name__)
def     A__					(						SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			Tuple							,  SCREAMING_SNAKE_CASE_      :			Dict	)     ->     str:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''projector.weight''']
   _UpperCAmelCase											=      downstream_dict['''projector.bias''']
   _UpperCAmelCase											=      downstream_dict['''model.post_net.linear.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.post_net.linear.bias''']
   return model
def     A__					(						SCREAMING_SNAKE_CASE_      :			Optional[Any]							,  SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			List[Any]	)     ->     Optional[int]:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''model.linear.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.linear.bias''']
   return model
def     A__					(						SCREAMING_SNAKE_CASE_      :			Any							,  SCREAMING_SNAKE_CASE_      :			Tuple							,  SCREAMING_SNAKE_CASE_      :			int	)     ->     str:






   """simple docstring"""







   _UpperCAmelCase											=      WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE_							,  config=SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      downstream_dict['''connector.weight''']
   _UpperCAmelCase											=      downstream_dict['''connector.bias''']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
      _UpperCAmelCase											=      downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      _UpperCAmelCase											=      downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   _UpperCAmelCase											=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   _UpperCAmelCase											=      downstream_dict['''objective.W''']
   return model
@torch.no_grad()
def     A__					(						SCREAMING_SNAKE_CASE_      :			Dict							,  SCREAMING_SNAKE_CASE_      :			List[str]							,  SCREAMING_SNAKE_CASE_      :			int							,  SCREAMING_SNAKE_CASE_      :			List[str]	)     ->     Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase											=      torch.load(SCREAMING_SNAKE_CASE_							,  map_location='''cpu'''	)

   _UpperCAmelCase											=      checkpoint['''Downstream''']

   _UpperCAmelCase											=      WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
   _UpperCAmelCase											=      WavaVecaFeatureExtractor.from_pretrained(
       SCREAMING_SNAKE_CASE_							,  return_attention_mask=SCREAMING_SNAKE_CASE_							,  do_normalize=SCREAMING_SNAKE_CASE_	)

   _UpperCAmelCase											=      hf_config.architectures[0]
   if arch.endswith('''ForSequenceClassification'''	):
      _UpperCAmelCase											=      convert_classification(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   elif arch.endswith('''ForAudioFrameClassification'''	):
      _UpperCAmelCase											=      convert_diarization(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   elif arch.endswith('''ForXVector'''	):
      _UpperCAmelCase											=      convert_xvector(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
   else:
      raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''	)

   if hf_config.use_weighted_layer_sum:
      _UpperCAmelCase											=      checkpoint['''Featurizer''']['''weights''']

   hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_	)
   hf_model.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   UpperCAmelCase_											=  argparse.ArgumentParser()
   parser.add_argument(
       "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
   )
   parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
   parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
   parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
   UpperCAmelCase_											=  parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)