






'''simple docstring'''

import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

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



def 							snake_case					(     a_							: str    ,   a_							: str    ,   a_							: Optional[Any]	)       ->				List[str]:





      """simple docstring"""

      if isinstance(a_    ,   torch.Tensor	):
            return image
      elif isinstance(a_    ,   PIL.Image.Image	):
            UpperCamelCase_				:   Dict					    =							[image]

      if isinstance(image[0]    ,   PIL.Image.Image	):
            UpperCamelCase_				:   str					    =							[np.array(i.resize((w, h)    ,   resample=PIL_INTERPOLATION["""lanczos"""]	)	)[None, :] for i in image]
            UpperCamelCase_				:   str					    =							np.concatenate(a_    ,   axis=0	)
            UpperCamelCase_				:   Optional[Any]					    =							np.array(a_	).astype(np.floataa	) / 255.0
            UpperCamelCase_				:   Dict					    =							image.transpose(0    ,   3    ,   1    ,   2	)
            UpperCamelCase_				:   Optional[int]					    =							2.0 * image - 1.0
            UpperCamelCase_				:   str					    =							torch.from_numpy(a_	)
      elif isinstance(image[0]    ,   torch.Tensor	):
            UpperCamelCase_				:   Tuple					    =							torch.cat(a_    ,   dim=0	)
      return image



def 							snake_case					(     a_							: int    ,   a_							: Union[str, Any]    ,   a_							: Dict    ,   a_							: Union[str, Any]=0.9_995	)       ->				int:





      """simple docstring"""

      if not isinstance(a_    ,   np.ndarray	):
            UpperCamelCase_				:   List[str]					    =							True
            UpperCamelCase_				:   Union[str, Any]					    =							va.device
            UpperCamelCase_				:   Union[str, Any]					    =							va.cpu().numpy()
            UpperCamelCase_				:   Tuple					    =							va.cpu().numpy()

      UpperCamelCase_				:   int					    =							np.sum(va * va / (np.linalg.norm(a_	) * np.linalg.norm(a_	))	)
      if np.abs(a_	) > DOT_THRESHOLD:
            UpperCamelCase_				:   Dict					    =							(1 - t) * va + t * va
      else:
            UpperCamelCase_				:   Union[str, Any]					    =							np.arccos(a_	)
            UpperCamelCase_				:   Optional[Any]					    =							np.sin(a_	)
            UpperCamelCase_				:   int					    =							theta_a * t
            UpperCamelCase_				:   List[Any]					    =							np.sin(a_	)
            UpperCamelCase_				:   Dict					    =							np.sin(theta_a - theta_t	) / sin_theta_a
            UpperCamelCase_				:   Any					    =							sin_theta_t / sin_theta_a
            UpperCamelCase_				:   List[Any]					    =							sa * va + sa * va

      if inputs_are_torch:
            UpperCamelCase_				:   List[str]					    =							torch.from_numpy(a_	).to(a_	)

      return va



def 							snake_case					(     a_							: int    ,   a_							: Union[str, Any]	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							F.normalize(a_    ,   dim=-1	)
      UpperCamelCase_				:   List[str]					    =							F.normalize(a_    ,   dim=-1	)
      return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)



def 							snake_case					(     a_							: Optional[int]    ,   a_							: List[Any]	)       ->				Optional[int]:





      """simple docstring"""

      for param in model.parameters():
            UpperCamelCase_				:   List[Any]					    =							value




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     ):
       super().__init__()
       self.register_modules(
           vae=__lowerCAmelCase			,     text_encoder=__lowerCAmelCase			,     clip_model=__lowerCAmelCase			,     tokenizer=__lowerCAmelCase			,     unet=__lowerCAmelCase			,     scheduler=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     coca_model=__lowerCAmelCase			,     coca_tokenizer=__lowerCAmelCase			,     coca_transform=__lowerCAmelCase			,     )
       UpperCamelCase_				:   List[str]					    =							(
           feature_extractor.size
           if isinstance(feature_extractor.size			,     __lowerCAmelCase )
           else feature_extractor.size["""shortest_edge"""]
       )
       UpperCamelCase_				:   Tuple					    =							transforms.Normalize(mean=feature_extractor.image_mean			,     std=feature_extractor.image_std )
       set_requires_grad(self.text_encoder			,     __lowerCAmelCase )
       set_requires_grad(self.clip_model			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase = "auto" ):
       if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             UpperCamelCase_				:   Union[str, Any]					    =							self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       self.enable_attention_slicing(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       set_requires_grad(self.vae			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       set_requires_grad(self.vae			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       set_requires_grad(self.unet			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       set_requires_grad(self.unet			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       # get the original timestep using init_timestep
       UpperCamelCase_				:   Optional[Any]					    =							min(int(num_inference_steps * strength )			,     __lowerCAmelCase )

       UpperCamelCase_				:   str					    =							max(num_inference_steps - init_timestep			,     0 )
       UpperCamelCase_				:   Union[str, Any]					    =							self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=None ):
       if not isinstance(__lowerCAmelCase			,     torch.Tensor ):
             raise ValueError(F"`image` has to be of type `torch.Tensor` but is {type(__lowerCAmelCase )}" )

       UpperCamelCase_				:   Union[str, Any]					    =							image.to(device=__lowerCAmelCase			,     dtype=__lowerCAmelCase )

       if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   Dict					    =							[
                 self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(__lowerCAmelCase )
             ]
             UpperCamelCase_				:   Optional[Any]					    =							torch.cat(__lowerCAmelCase			,     dim=0 )
       else:
             UpperCamelCase_				:   int					    =							self.vae.encode(__lowerCAmelCase ).latent_dist.sample(__lowerCAmelCase )

       # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       UpperCamelCase_				:   Any					    =							0.1_82_15 * init_latents
       UpperCamelCase_				:   List[Any]					    =							init_latents.repeat_interleave(__lowerCAmelCase			,     dim=0 )

       UpperCamelCase_				:   str					    =							randn_tensor(init_latents.shape			,     generator=__lowerCAmelCase			,     device=__lowerCAmelCase			,     dtype=__lowerCAmelCase )

       # get latents
       UpperCamelCase_				:   Dict					    =							self.scheduler.add_noise(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
       UpperCamelCase_				:   Optional[Any]					    =							init_latents

       return latents
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							self.coca_transform(__lowerCAmelCase ).unsqueeze(0 )
       with torch.no_grad(), torch.cuda.amp.autocast():
             UpperCamelCase_				:   int					    =							self.coca_model.generate(transformed_image.to(device=self.device			,     dtype=self.coca_model.dtype ) )
       UpperCamelCase_				:   int					    =							self.coca_tokenizer.decode(generated[0].cpu().numpy() )
       return generated.split("""<end_of_text>""" )[0].replace("""<start_of_text>"""			,     """""" ).rstrip(""" .,""" )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Tuple					    =							self.feature_extractor.preprocess(__lowerCAmelCase )
       UpperCamelCase_				:   str					    =							torch.from_numpy(clip_image_input["""pixel_values"""][0] ).unsqueeze(0 ).to(self.device ).half()
       UpperCamelCase_				:   Any					    =							self.clip_model.get_image_features(__lowerCAmelCase )
       UpperCamelCase_				:   Optional[Any]					    =							image_embeddings_clip / image_embeddings_clip.norm(p=2			,     dim=-1			,     keepdim=__lowerCAmelCase )
       UpperCamelCase_				:   int					    =							image_embeddings_clip.repeat_interleave(__lowerCAmelCase			,     dim=0 )
       return image_embeddings_clip
 @torch.enable_grad()
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     ):
       UpperCamelCase_				:   str					    =							latents.detach().requires_grad_()

       UpperCamelCase_				:   Dict					    =							self.scheduler.scale_model_input(__lowerCAmelCase			,     __lowerCAmelCase )

       # predict the noise residual
       UpperCamelCase_				:   Optional[Any]					    =							self.unet(__lowerCAmelCase			,     __lowerCAmelCase			,     encoder_hidden_states=__lowerCAmelCase ).sample

       if isinstance(self.scheduler			,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
             UpperCamelCase_				:   List[Any]					    =							self.scheduler.alphas_cumprod[timestep]
             UpperCamelCase_				:   Optional[Any]					    =							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_				:   str					    =							(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

             UpperCamelCase_				:   List[str]					    =							torch.sqrt(__lowerCAmelCase )
             UpperCamelCase_				:   Any					    =							pred_original_sample * (fac) + latents * (1 - fac)
       elif isinstance(self.scheduler			,     __lowerCAmelCase ):
             UpperCamelCase_				:   int					    =							self.scheduler.sigmas[index]
             UpperCamelCase_				:   Optional[int]					    =							latents - sigma * noise_pred
       else:
             raise ValueError(F"scheduler type {type(self.scheduler )} not supported" )

       # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       UpperCamelCase_				:   Optional[Any]					    =							1 / 0.1_82_15 * sample
       UpperCamelCase_				:   Dict					    =							self.vae.decode(__lowerCAmelCase ).sample
       UpperCamelCase_				:   Any					    =							(image / 2 + 0.5).clamp(0			,     1 )

       UpperCamelCase_				:   Optional[int]					    =							transforms.Resize(self.feature_extractor_size )(__lowerCAmelCase )
       UpperCamelCase_				:   Optional[Any]					    =							self.normalize(__lowerCAmelCase ).to(latents.dtype )

       UpperCamelCase_				:   Union[str, Any]					    =							self.clip_model.get_image_features(__lowerCAmelCase )
       UpperCamelCase_				:   Optional[int]					    =							image_embeddings_clip / image_embeddings_clip.norm(p=2			,     dim=-1			,     keepdim=__lowerCAmelCase )

       UpperCamelCase_				:   Optional[int]					    =							spherical_dist_loss(__lowerCAmelCase			,     __lowerCAmelCase ).mean() * clip_guidance_scale

       UpperCamelCase_				:   str					    =							-torch.autograd.grad(__lowerCAmelCase			,     __lowerCAmelCase )[0]

       if isinstance(self.scheduler			,     __lowerCAmelCase ):
             UpperCamelCase_				:   Optional[int]					    =							latents.detach() + grads * (sigma**2)
             UpperCamelCase_				:   Optional[int]					    =							noise_pred_original
       else:
             UpperCamelCase_				:   Any					    =							noise_pred_original - torch.sqrt(__lowerCAmelCase ) * grads
       return noise_pred, latents





 @torch.no_grad()
 def __call__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = 5_12			,     __lowerCAmelCase = 5_12			,     __lowerCAmelCase = 0.6			,     __lowerCAmelCase = 50			,     __lowerCAmelCase = 7.5			,     __lowerCAmelCase = 1			,     __lowerCAmelCase = 0.0			,     __lowerCAmelCase = 1_00			,     __lowerCAmelCase = None			,     __lowerCAmelCase = "pil"			,     __lowerCAmelCase = True			,     __lowerCAmelCase = 0.8			,     __lowerCAmelCase = 0.1			,     __lowerCAmelCase = 0.1			,     ):
       if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ) and len(__lowerCAmelCase ) != batch_size:
             raise ValueError(F"You have passed {batch_size} batch_size, but only {len(__lowerCAmelCase )} generators." )

       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}." )

       if isinstance(__lowerCAmelCase			,     torch.Generator ) and batch_size > 1:
             UpperCamelCase_				:   str					    =							[generator] + [None] * (batch_size - 1)

       UpperCamelCase_				:   List[str]					    =							[
           ("""model""", self.coca_model is None),
           ("""tokenizer""", self.coca_tokenizer is None),
           ("""transform""", self.coca_transform is None),
       ]
       UpperCamelCase_				:   Optional[int]					    =							[x[0] for x in coca_is_none if x[1]]
       UpperCamelCase_				:   Tuple					    =							""", """.join(__lowerCAmelCase )
       # generate prompts with coca model if prompt is None
       if content_prompt is None:
             if len(__lowerCAmelCase ):
                   raise ValueError(
                       F"Content prompt is None and CoCa [{coca_is_none_str}] is None."
                       F"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
             UpperCamelCase_				:   Optional[int]					    =							self.get_image_description(__lowerCAmelCase )
       if style_prompt is None:
             if len(__lowerCAmelCase ):
                   raise ValueError(
                       F"Style prompt is None and CoCa [{coca_is_none_str}] is None."
                       F" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
             UpperCamelCase_				:   List[str]					    =							self.get_image_description(__lowerCAmelCase )

       # get prompt text embeddings for content and style
       UpperCamelCase_				:   Dict					    =							self.tokenizer(
           __lowerCAmelCase			,     padding="""max_length"""			,     max_length=self.tokenizer.model_max_length			,     truncation=__lowerCAmelCase			,     return_tensors="""pt"""			,     )
       UpperCamelCase_				:   Dict					    =							self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]

       UpperCamelCase_				:   Optional[Any]					    =							self.tokenizer(
           __lowerCAmelCase			,     padding="""max_length"""			,     max_length=self.tokenizer.model_max_length			,     truncation=__lowerCAmelCase			,     return_tensors="""pt"""			,     )
       UpperCamelCase_				:   Union[str, Any]					    =							self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]

       UpperCamelCase_				:   Dict					    =							slerp(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )

       # duplicate text embeddings for each generation per prompt
       UpperCamelCase_				:   Optional[Any]					    =							text_embeddings.repeat_interleave(__lowerCAmelCase			,     dim=0 )

       # set timesteps
       UpperCamelCase_				:   Union[str, Any]					    =							"""offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
       UpperCamelCase_				:   str					    =							{}
       if accepts_offset:
             UpperCamelCase_				:   int					    =							1

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

       UpperCamelCase_    ,			UpperCamelCase_				:   Optional[Any]					    =							self.get_timesteps(__lowerCAmelCase			,     __lowerCAmelCase			,     self.device )
       UpperCamelCase_				:   Optional[Any]					    =							timesteps[:1].repeat(__lowerCAmelCase )

       # Preprocess image
       UpperCamelCase_				:   List[Any]					    =							preprocess(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
       UpperCamelCase_				:   Optional[Any]					    =							self.prepare_latents(
           __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     text_embeddings.dtype			,     self.device			,     __lowerCAmelCase )

       UpperCamelCase_				:   Dict					    =							preprocess(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
       UpperCamelCase_				:   Optional[int]					    =							self.prepare_latents(
           __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     text_embeddings.dtype			,     self.device			,     __lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							slerp(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )

       if clip_guidance_scale > 0:
             UpperCamelCase_				:   Union[str, Any]					    =							self.get_clip_image_embeddings(__lowerCAmelCase			,     __lowerCAmelCase )
             UpperCamelCase_				:   Any					    =							self.get_clip_image_embeddings(__lowerCAmelCase			,     __lowerCAmelCase )
             UpperCamelCase_				:   Dict					    =							slerp(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )

       # 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_				:   Dict					    =							guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
             UpperCamelCase_				:   int					    =							content_text_input.input_ids.shape[-1]
             UpperCamelCase_				:   Union[str, Any]					    =							self.tokenizer([""""""]			,     padding="""max_length"""			,     max_length=__lowerCAmelCase			,     return_tensors="""pt""" )
             UpperCamelCase_				:   List[Any]					    =							self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
             # duplicate unconditional embeddings for each generation per prompt
             UpperCamelCase_				:   Union[str, Any]					    =							uncond_embeddings.repeat_interleave(__lowerCAmelCase			,     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_				:   Optional[Any]					    =							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_				:   int					    =							(batch_size, self.unet.config.in_channels, height // 8, width // 8)
       UpperCamelCase_				:   Optional[int]					    =							text_embeddings.dtype
       if latents is None:
             if self.device.type == "mps":
                   # randn does not work reproducibly on mps
                   UpperCamelCase_				:   Dict					    =							torch.randn(__lowerCAmelCase			,     generator=__lowerCAmelCase			,     device="""cpu"""			,     dtype=__lowerCAmelCase ).to(
                       self.device )
             else:
                   UpperCamelCase_				:   Dict					    =							torch.randn(__lowerCAmelCase			,     generator=__lowerCAmelCase			,     device=self.device			,     dtype=__lowerCAmelCase )
       else:
             if latents.shape != latents_shape:
                   raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
             UpperCamelCase_				:   str					    =							latents.to(self.device )

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCamelCase_				:   Any					    =							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_				:   Union[str, Any]					    =							"""eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
       UpperCamelCase_				:   List[str]					    =							{}
       if accepts_eta:
             UpperCamelCase_				:   Optional[Any]					    =							eta

       # check if the scheduler accepts generator
       UpperCamelCase_				:   Optional[int]					    =							"""generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
       if accepts_generator:
             UpperCamelCase_				:   Dict					    =							generator

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

                   # predict the noise residual
                   UpperCamelCase_				:   List[str]					    =							self.unet(__lowerCAmelCase			,     __lowerCAmelCase			,     encoder_hidden_states=__lowerCAmelCase ).sample

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

                   # perform clip guidance
                   if clip_guidance_scale > 0:
                         UpperCamelCase_				:   int					    =							(
                             text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
                         )
                         UpperCamelCase_    ,			UpperCamelCase_				:   Optional[Any]					    =							self.cond_fn(
                             __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     )

                   # compute the previous noisy sample x_t -> x_t-1
                   UpperCamelCase_				:   List[str]					    =							self.scheduler.step(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     **__lowerCAmelCase ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       UpperCamelCase_				:   str					    =							1 / 0.1_82_15 * latents
       UpperCamelCase_				:   Optional[Any]					    =							self.vae.decode(__lowerCAmelCase ).sample

       UpperCamelCase_				:   int					    =							(image / 2 + 0.5).clamp(0			,     1 )
       UpperCamelCase_				:   List[str]					    =							image.cpu().permute(0			,     2			,     3			,     1 ).numpy()

       if output_type == "pil":
             UpperCamelCase_				:   str					    =							self.numpy_to_pil(__lowerCAmelCase )

       if not return_dict:
             return (image, None)

       return StableDiffusionPipelineOutput(images=__lowerCAmelCase			,     nsfw_content_detected=__lowerCAmelCase )














'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase							=logging.get_logger(__name__)

UpperCamelCase							={
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
UpperCamelCase							=[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 							snake_case					(     a_							: str    ,   a_							: Any    ,   a_							: Dict    ,   a_							: Optional[int]    ,   a_							: Optional[int]	)       ->				int:





      """simple docstring"""

      for attribute in key.split("""."""	):
            UpperCamelCase_				:   Union[str, Any]					    =							getattr(a_    ,   a_	)

      if weight_type is not None:
            UpperCamelCase_				:   Dict					    =							getattr(a_    ,   a_	).shape
      else:
            UpperCamelCase_				:   int					    =							hf_pointer.shape

      assert hf_shape == value.shape, (
          f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
          f" {value.shape} for {full_name}"
      )

      if weight_type == "weight":
            UpperCamelCase_				:   str					    =							value
      elif weight_type == "weight_g":
            UpperCamelCase_				:   str					    =							value
      elif weight_type == "weight_v":
            UpperCamelCase_				:   Optional[int]					    =							value
      elif weight_type == "bias":
            UpperCamelCase_				:   Any					    =							value
      else:
            UpperCamelCase_				:   Dict					    =							value

      logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."	)



def 							snake_case					(     a_							: Optional[int]    ,   a_							: int	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							[]
      UpperCamelCase_				:   Dict					    =							fairseq_model.state_dict()

      UpperCamelCase_				:   Union[str, Any]					    =							hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      UpperCamelCase_				:   Optional[int]					    =							None

      for name, value in fairseq_dict.items():
            UpperCamelCase_				:   Optional[int]					    =							False
            if "conv_layers" in name:
                  load_conv_layer(
                      a_    ,   a_    ,   a_    ,   a_    ,   hf_model.config.feat_extract_norm == """group"""    ,   )
                  UpperCamelCase_				:   Any					    =							True
            elif name.split("""."""	)[0] == "proj":
                  UpperCamelCase_				:   Tuple					    =							fairseq_model.proj
                  UpperCamelCase_				:   Any					    =							True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
                              UpperCamelCase_				:   Dict					    =							True
                              if "*" in mapped_key:
                                    UpperCamelCase_				:   Optional[int]					    =							name.split(a_	)[0].split("""."""	)[-2]
                                    UpperCamelCase_				:   Any					    =							mapped_key.replace("""*"""    ,   a_	)
                              if "weight_g" in name:
                                    UpperCamelCase_				:   Tuple					    =							"""weight_g"""
                              elif "weight_v" in name:
                                    UpperCamelCase_				:   Union[str, Any]					    =							"""weight_v"""
                              elif "bias" in name:
                                    UpperCamelCase_				:   int					    =							"""bias"""
                              elif "weight" in name:
                                    UpperCamelCase_				:   Optional[int]					    =							"""weight"""
                              else:
                                    UpperCamelCase_				:   int					    =							None
                              set_recursively(a_    ,   a_    ,   a_    ,   a_    ,   a_	)
                        continue
            if not is_used:
                  unused_weights.append(a_	)

      logger.warning(f"Unused weights: {unused_weights}"	)

      return proj_weight



def 							snake_case					(     a_							: Any    ,   a_							: Any    ,   a_							: Union[str, Any]    ,   a_							: Dict    ,   a_							: Tuple	)       ->				List[str]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							full_name.split("""conv_layers."""	)[-1]
      UpperCamelCase_				:   Optional[Any]					    =							name.split("""."""	)
      UpperCamelCase_				:   List[Any]					    =							int(items[0]	)
      UpperCamelCase_				:   Optional[Any]					    =							int(items[1]	)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
                  )
                  UpperCamelCase_				:   List[Any]					    =							value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."	)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
                  )
                  UpperCamelCase_				:   Dict					    =							value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."	)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                      " found."
                  )
                  UpperCamelCase_				:   List[str]					    =							value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."	)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
                  )
                  UpperCamelCase_				:   str					    =							value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."	)
      else:
            unused_weights.append(a_	)



def 							snake_case					(     a_							: str	)       ->				Union[str, Any]:





      """simple docstring"""

      UpperCamelCase_    ,			UpperCamelCase_				:   List[str]					    =							emb.weight.shape
      UpperCamelCase_				:   str					    =							nn.Linear(a_    ,   a_    ,   bias=a_	)
      UpperCamelCase_				:   Optional[int]					    =							emb.weight.data
      return lin_layer



def 							snake_case					(     a_							: Any	)       ->				Tuple:





      """simple docstring"""

      with open(a_    ,   """r"""    ,   encoding="""utf-8"""	) as f:
            UpperCamelCase_				:   Optional[int]					    =							f.readlines()
            UpperCamelCase_				:   Union[str, Any]					    =							[line.split(""" """	)[0] for line in lines]

      UpperCamelCase_				:   List[Any]					    =							len(a_	)

      UpperCamelCase_				:   Union[str, Any]					    =							{
          """<s>""": 0,
          """<pad>""": 1,
          """</s>""": 2,
          """<unk>""": 3,
      }

      vocab_dict.update(dict(zip(a_    ,   range(4    ,   num_words + 4	)	)	)	)
      return vocab_dict



@torch.no_grad()
def 							snake_case					(     a_							: List[Any]    ,   a_							: str    ,   a_							: int    ,   a_							: int    ,   a_							: Optional[int]    ,   a_							: Any    ,   a_							: Tuple    ,   )       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							WavaVecaConfig.from_pretrained(a_	)
      UpperCamelCase_				:   Any					    =							SpeechaTextaConfig.from_pretrained(
          a_    ,   vocab_size=a_    ,   decoder_layers=a_    ,   do_stable_layer_norm=a_	)

      UpperCamelCase_				:   Optional[Any]					    =							WavaVecaFeatureExtractor(
          feature_size=1    ,   sampling_rate=16_000    ,   padding_value=0    ,   do_normalize=a_    ,   return_attention_mask=a_    ,   )

      UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]    ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
      UpperCamelCase_				:   Union[str, Any]					    =							model[0].eval()

      # set weights for wav2vec2 encoder
      UpperCamelCase_				:   Tuple					    =							WavaVecaModel(a_	)
      UpperCamelCase_				:   Any					    =							recursively_load_weights_wavaveca(model.encoder    ,   a_	)

      UpperCamelCase_				:   Any					    =							SpeechaTextaForCausalLM(a_	)
      UpperCamelCase_    ,			UpperCamelCase_				:   Optional[Any]					    =							hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,   strict=a_	)

      # set output linear layer
      unexpected_keys.remove("""embed_out"""	)
      UpperCamelCase_				:   Tuple					    =							nn.Parameter(model.decoder.embed_out.detach()	)

      # layer norm is init to identity matrix so leaving it is fine
      logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}"	)
      logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"	)

      UpperCamelCase_				:   Optional[Any]					    =							SpeechEncoderDecoderModel(encoder=a_    ,   decoder=a_	)
      UpperCamelCase_				:   Dict					    =							False

      # add projection layer
      UpperCamelCase_				:   Any					    =							nn.Parameter(projection_layer.weight	)
      UpperCamelCase_				:   Optional[Any]					    =							nn.Parameter(projection_layer.bias	)

      UpperCamelCase_				:   Dict					    =							create_vocab_dict(a_	)

      with open(os.path.join(a_    ,   """vocab.json"""	)    ,   """w"""	) as fp:
            json.dump(a_    ,   a_	)

      UpperCamelCase_				:   Optional[int]					    =							SpeechaTextaTokenizer(os.path.join(a_    ,   """vocab.json"""	)	)
      tokenizer.save_pretrained(a_	)

      UpperCamelCase_				:   Optional[int]					    =							hf_wavavec.config.to_dict()
      UpperCamelCase_				:   Union[str, Any]					    =							tokenizer.pad_token_id
      UpperCamelCase_				:   List[Any]					    =							tokenizer.bos_token_id
      UpperCamelCase_				:   str					    =							tokenizer.eos_token_id
      UpperCamelCase_				:   Dict					    =							"""speech_to_text_2"""
      UpperCamelCase_				:   Optional[Any]					    =							"""wav2vec2"""

      UpperCamelCase_				:   Dict					    =							SpeechEncoderDecoderConfig.from_dict(a_	)

      hf_wavavec.save_pretrained(a_	)
      feature_extractor.save_pretrained(a_	)


if __name__ == "__main__":
      UpperCamelCase							=argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
      parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
      parser.add_argument(
          "--encoder_config_path",
          default="facebook/wav2vec2-large-lv60",
          type=str,
          help="Path to hf encoder wav2vec2 checkpoint config",
      )
      parser.add_argument(
          "--decoder_config_path",
          default="facebook/s2t-small-mustc-en-fr-st",
          type=str,
          help="Path to hf decoder s2t checkpoint config",
      )
      parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
      parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

      UpperCamelCase							=parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.dict_path,
          encoder_config_path=args.encoder_config_path,
          decoder_config_path=args.decoder_config_path,
          vocab_size=args.vocab_size,
          num_decoder_layers=args.num_decoder_layers,
      )






