



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 		_snake_case      (			nn.Module				):





 """simple docstring"""



 def __init__(						self	,    a	,    a=1.0					)	->							List[Any]:




   """simple docstring"""







   super().__init__()

   _A           =    cut_size
   _A           =    cut_power



 def lowercase_		(						self	,    a	,    a					)	->							Any:




   """simple docstring"""







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







def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> Optional[Any]:
  _A           =    F.normalize(__snake_case						,				dim=-1							)
  _A           =    F.normalize(__snake_case						,				dim=-1							)
  return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)




def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> Union[str, Any]:
  for param in model.parameters():
    _A           =    value



class 		_snake_case      (			lowerCamelCase				):





 """simple docstring"""



 def __init__(						self	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    )	->							Tuple:




   """simple docstring"""







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

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

   set_requires_grad(self.text_encoder	,    a					)
   set_requires_grad(self.clip_model	,    a					)
 def lowercase_		(						self	,    a = "auto"					)	->							Union[str, Any]:




   """simple docstring"""







   if slice_size == "auto":
     # half the attention head size is usually a good trade-off between
     # speed and memory
     _A           =    self.unet.config.attention_head_dim // 2
   self.unet.set_attention_slice(a					)
 def lowercase_		(						self					)	->							Optional[int]:




   """simple docstring"""







   self.enable_attention_slicing(a					)
 def lowercase_		(						self					)	->							Optional[Any]:




   """simple docstring"""







   set_requires_grad(self.vae	,    a					)
 def lowercase_		(						self					)	->							Optional[Any]:




   """simple docstring"""







   set_requires_grad(self.vae	,    a					)
 def lowercase_		(						self					)	->							Tuple:




   """simple docstring"""







   set_requires_grad(self.unet	,    a					)
 def lowercase_		(						self					)	->							int:




   """simple docstring"""







   set_requires_grad(self.unet	,    a					)
 @torch.enable_grad()
 def lowercase_		(						self	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    a=True	,    )	->							int:




   """simple docstring"""







   _A           =    latents.detach().requires_grad_()

   _A           =    self.scheduler.scale_model_input(a	,    a					)

   # predict the noise residual
   _A           =    self.unet(a	,    a	,    encoder_hidden_states=a					).sample

   if isinstance(self.scheduler	,    (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)					):
     _A           =    self.scheduler.alphas_cumprod[timestep]
     _A           =    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
     _A           =    (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

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

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

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

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

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

   _A           =    -torch.autograd.grad(a	,    a					)[0]

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



 @torch.no_grad()
 def __call__(						self	,    a	,    a = 5_1_2	,    a = 5_1_2	,    a = 5_0	,    a = 7.5	,    a = 1	,    a = 0.0	,    a = 1_0_0	,    a = None	,    a = 4	,    a = True	,    a = None	,    a = None	,    a = "pil"	,    a = True	,    )	->							Optional[Any]:




   """simple docstring"""







   if isinstance(a	,    a					):
     _A           =    1
   elif isinstance(a	,    a					):
     _A           =    len(a					)
   else:
     raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(a					)}'''					)

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

   if clip_guidance_scale > 0:
     if clip_prompt is not None:
       _A           =    self.tokenizer(
           a	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    truncation=a	,    return_tensors='''pt'''	,    ).input_ids.to(self.device					)
     else:
       _A           =    text_input.input_ids.to(self.device					)
     _A           =    self.clip_model.get_text_features(a					)
     _A           =    text_embeddings_clip / text_embeddings_clip.norm(p=2	,    dim=-1	,    keepdim=a					)
     # duplicate text embeddings clip for each generation per prompt
     _A           =    text_embeddings_clip.repeat_interleave(a	,    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.
   _A           =    guidance_scale > 1.0
   # get unconditional embeddings for classifier free guidance
   if do_classifier_free_guidance:
     _A           =    text_input.input_ids.shape[-1]
     _A           =    self.tokenizer(['''''']	,    padding='''max_length'''	,    max_length=a	,    return_tensors='''pt'''					)
     _A           =    self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]
     # duplicate unconditional embeddings for each generation per prompt
     _A           =    uncond_embeddings.repeat_interleave(a	,    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
     _A           =    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`.
   _A           =    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
   _A           =    text_embeddings.dtype
   if latents is None:
     if self.device.type == "mps":
       # randn does not work reproducibly on mps
       _A           =    torch.randn(a	,    generator=a	,    device='''cpu'''	,    dtype=a					).to(
           self.device					)
     else:
       _A           =    torch.randn(a	,    generator=a	,    device=self.device	,    dtype=a					)
   else:
     if latents.shape != latents_shape:
       raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''					)
     _A           =    latents.to(self.device					)

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

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

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

   # scale the initial noise by the standard deviation required by the scheduler
   _A           =    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]
   _A           =    '''eta''' in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
   _A           =    {}
   if accepts_eta:
     _A           =    eta

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

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

     # predict the noise residual
     _A           =    self.unet(a	,    a	,    encoder_hidden_states=a					).sample

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

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

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

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

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

   if output_type == "pil":
     _A           =    self.numpy_to_pil(a					)

   if not return_dict:
     return (image, None)

   return StableDiffusionPipelineOutput(images=a	,    nsfw_content_detected=a					)




import copy

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


__lowerCamelCase  				=					logging.get_logger(__name__)

__lowerCamelCase  				=					{}



class 		_snake_case      (			lowerCamelCase				):





 """simple docstring"""



 lowerCamelCase_          =			'''fsmt_decoder'''
 def __init__(						self	,    a=0	,    a=0					)	->							str:




   """simple docstring"""







   super().__init__()
   _A           =    vocab_size
   _A           =    bos_token_id







class 		_snake_case      (			lowerCamelCase				):





 """simple docstring"""



 lowerCamelCase_          =			'''fsmt'''
 lowerCamelCase_          =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
 def __init__(						self	,    a=["en", "de"]	,    a=4_2_0_2_4	,    a=4_2_0_2_4	,    a="relu"	,    a=1_0_2_4	,    a=2_0_0	,    a=1_0_2_4	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=0.0	,    a=0.1	,    a=0.0	,    a=0.02	,    a=2	,    a=True	,    a=True	,    a=False	,    a=5	,    a=1.0	,    a=False	,    a=True	,    a=1	,    a=0	,    a=2	,    a=2	,    **a	,    )	->							Union[str, Any]:




   """simple docstring"""







   _A           =    langs
   _A           =    src_vocab_size
   _A           =    tgt_vocab_size
   _A           =    d_model  # encoder_embed_dim and decoder_embed_dim

   _A           =    encoder_ffn_dim
   _A           =    _A           =    encoder_layers
   _A           =    encoder_attention_heads
   _A           =    encoder_layerdrop
   _A           =    decoder_layerdrop
   _A           =    decoder_ffn_dim
   _A           =    decoder_layers
   _A           =    decoder_attention_heads
   _A           =    max_position_embeddings
   _A           =    init_std  # Normal(0, this parameter)
   _A           =    activation_function

   _A           =    DecoderConfig(vocab_size=a	,    bos_token_id=a					)
   if "decoder" in common_kwargs:
     del common_kwargs["decoder"]

   _A           =    scale_embedding  # scale factor will be sqrt(d_model) if True

   # 3 Types of Dropout
   _A           =    attention_dropout
   _A           =    activation_dropout
   _A           =    dropout

   _A           =    use_cache
   super().__init__(
       pad_token_id=a	,    bos_token_id=a	,    eos_token_id=a	,    decoder_start_token_id=a	,    is_encoder_decoder=a	,    tie_word_embeddings=a	,    forced_eos_token_id=a	,    max_length=a	,    num_beams=a	,    length_penalty=a	,    early_stopping=a	,    **a	,    )



 def lowercase_		(						self					)	->							Any:




   """simple docstring"""







   _A           =    copy.deepcopy(self.__dict__					)
   _A           =    self.decoder.to_dict()
   _A           =    self.__class__.model_type
   return output