import functools
import operator

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


lowerCAmelCase__      		=					logging.get_logger(__name__)

lowerCAmelCase__      		=					{
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_      					=   'hubert'




 def __init__(     self   :    Any     ,       lowercase__   :    Optional[int]=32     ,       lowercase__   :    Optional[Any]=768     ,       lowercase__   :    Tuple=12     ,       lowercase__   :    List[str]=12     ,       lowercase__   :    Union[str, Any]=3_072     ,       lowercase__   :    Any="gelu"     ,       lowercase__   :    List[str]=0.1     ,       lowercase__   :    Tuple=0.1     ,       lowercase__   :    Optional[int]=0.1     ,       lowercase__   :    Tuple=True     ,       lowercase__   :    Union[str, Any]=0.0     ,       lowercase__   :    str=0.1     ,       lowercase__   :    List[Any]=0.1     ,       lowercase__   :    str=0.02     ,       lowercase__   :    Tuple=1e-5     ,       lowercase__   :    Any="group"     ,       lowercase__   :    List[Any]="gelu"     ,       lowercase__   :    str=(512, 512, 512, 512, 512, 512, 512)     ,       lowercase__   :    Optional[int]=(5, 2, 2, 2, 2, 2, 2)     ,       lowercase__   :    int=(10, 3, 3, 3, 3, 2, 2)     ,       lowercase__   :    List[Any]=False     ,       lowercase__   :    List[str]=128     ,       lowercase__   :    Any=16     ,       lowercase__   :    Optional[Any]=False     ,       lowercase__   :    str=True     ,       lowercase__   :    str=0.05     ,       lowercase__   :    List[str]=10     ,       lowercase__   :    Tuple=2     ,       lowercase__   :    Union[str, Any]=0.0     ,       lowercase__   :    List[str]=10     ,       lowercase__   :    str=0     ,       lowercase__   :    List[Any]="sum"     ,       lowercase__   :    Dict=False     ,       lowercase__   :    Union[str, Any]=False     ,       lowercase__   :    int=256     ,       lowercase__   :    str=0     ,       lowercase__   :    Union[str, Any]=1     ,       lowercase__   :    str=2     ,       **lowercase__   :    Tuple     ,       ):







        '''simple docstring'''



        super().__init__(**lowercase__     ,       pad_token_id=lowercase__     ,       bos_token_id=lowercase__     ,       eos_token_id=lowercase__)
        lowerCAmelCase__	      =   hidden_size
        lowerCAmelCase__	      =   feat_extract_norm
        lowerCAmelCase__	      =   feat_extract_activation
        lowerCAmelCase__	      =   list(lowercase__)
        lowerCAmelCase__	      =   list(lowercase__)
        lowerCAmelCase__	      =   list(lowercase__)
        lowerCAmelCase__	      =   conv_bias
        lowerCAmelCase__	      =   num_conv_pos_embeddings
        lowerCAmelCase__	      =   num_conv_pos_embedding_groups
        lowerCAmelCase__	      =   len(self.conv_dim)
        lowerCAmelCase__	      =   num_hidden_layers
        lowerCAmelCase__	      =   intermediate_size
        lowerCAmelCase__	      =   hidden_act
        lowerCAmelCase__	      =   num_attention_heads
        lowerCAmelCase__	      =   hidden_dropout
        lowerCAmelCase__	      =   attention_dropout
        lowerCAmelCase__	      =   activation_dropout
        lowerCAmelCase__	      =   feat_proj_layer_norm
        lowerCAmelCase__	      =   feat_proj_dropout
        lowerCAmelCase__	      =   final_dropout
        lowerCAmelCase__	      =   layerdrop
        lowerCAmelCase__	      =   layer_norm_eps
        lowerCAmelCase__	      =   initializer_range
        lowerCAmelCase__	      =   vocab_size
        lowerCAmelCase__	      =   do_stable_layer_norm
        lowerCAmelCase__	      =   use_weighted_layer_sum
        lowerCAmelCase__	      =   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
        lowerCAmelCase__	      =   apply_spec_augment
        lowerCAmelCase__	      =   mask_time_prob
        lowerCAmelCase__	      =   mask_time_length
        lowerCAmelCase__	      =   mask_time_min_masks
        lowerCAmelCase__	      =   mask_feature_prob
        lowerCAmelCase__	      =   mask_feature_length
        lowerCAmelCase__	      =   mask_feature_min_masks

        # ctc loss
        lowerCAmelCase__	      =   ctc_loss_reduction
        lowerCAmelCase__	      =   ctc_zero_infinity




 @property
 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



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

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 def __init__(     self   :    List[Any]     ,       lowercase__   :    AutoencoderKL     ,       lowercase__   :    CLIPTextModel     ,       lowercase__   :    CLIPTokenizer     ,       lowercase__   :    UNetaDConditionModel     ,       lowercase__   :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]     ,       ):







        '''simple docstring'''



        super().__init__()

        self.register_modules(vae=lowercase__     ,       text_encoder=lowercase__     ,       tokenizer=lowercase__     ,       unet=lowercase__     ,       scheduler=lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    str):







        '''simple docstring'''



        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.encode(tfms.ToTensor()(lowercase__).unsqueeze(0).to(self.device) * 2 - 1)
               lowerCAmelCase__	      =   0.18_215 * latent.latent_dist.sample()
        return latent




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   (1 / 0.18_215) * latent
        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.decode(lowercase__).sample
        lowerCAmelCase__	      =   (img / 2 + 0.5).clamp(0     ,       1)
        lowerCAmelCase__	      =   img.detach().cpu().permute(0     ,       2     ,       3     ,       1).numpy()
        lowerCAmelCase__	      =   (img * 255).round().astype('uint8')
        return Image.fromarray(img[0])




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.tokenizer(
            lowercase__     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(text_input.input_ids.to(self.device))[0]

        lowerCAmelCase__	      =   self.tokenizer(
            ''     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])




 def __call__(     self   :    List[Any]     ,       lowercase__   :    Image.Image     ,       lowercase__   :    str     ,       lowercase__   :    float = 0.3     ,       lowercase__   :    float = 0.6     ,       lowercase__   :    float = 0.5     ,       lowercase__   :    int = 42     ,       lowercase__   :    int = 50     ,       lowercase__   :    float = 7.5     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   steps - int(kmin * steps)
        lowerCAmelCase__	      =   steps - int(kmax * steps)

        lowerCAmelCase__	      =   self.prep_text(lowercase__)

        self.scheduler.set_timesteps(lowercase__)

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   img.size
        lowerCAmelCase__	      =   self.encode(lowercase__)

        torch.manual_seed(lowercase__)
        lowerCAmelCase__	      =   torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)     ,       ).to(self.device)

        lowerCAmelCase__	      =   self.scheduler.add_noise(
            lowercase__     ,       lowercase__     ,       timesteps=self.scheduler.timesteps[tmax]     ,       )

        lowerCAmelCase__	      =   torch.cat([latents] * 2)

        lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       self.scheduler.timesteps[tmax])

        with torch.no_grad():
               lowerCAmelCase__	      =   self.unet(
                   lowercase__     ,       self.scheduler.timesteps[tmax]     ,       encoder_hidden_states=lowercase__     ,       ).sample

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
        lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       self.scheduler.timesteps[tmax]     ,       lowercase__).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
               if i > tmax:
                      if i < tmin:  # layout generation phase
                             lowerCAmelCase__	      =   self.scheduler.add_noise(
                                 lowercase__     ,       lowercase__     ,       timesteps=lowercase__     ,       )

                             lowerCAmelCase__	      =   (mix_factor * latents) + (
                                 1 - mix_factor
                             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                             lowerCAmelCase__	      =   torch.cat([input] * 2)

                      else:  # content generation phase
                             lowerCAmelCase__	      =   torch.cat([latents] * 2)

                      lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       lowercase__)

                      with torch.no_grad():
                             lowerCAmelCase__	      =   self.unet(
                                 lowercase__     ,       lowercase__     ,       encoder_hidden_states=lowercase__     ,       ).sample

                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
                      lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

                      lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       lowercase__     ,       lowercase__).prev_sample

        return self.decode(lowercase__)
