




"""simple docstring"""


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


A_						=				logging.get_logger(__name__)  # pylint: disable=invalid-name






class 					lowercase(     UpperCAmelCase__ ):







 '''simple docstring'''





 def __init__(				self:		List[Any],      a_:		WhisperForConditionalGeneration,      a_:		WhisperProcessor,      a_:		AutoencoderKL,      a_:		CLIPTextModel,      a_:		CLIPTokenizer,      a_:		UNetaDConditionModel,      a_:		Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],      a_:		StableDiffusionSafetyChecker,      a_:		CLIPImageProcessor,      ):




   '''simple docstring'''



   super().__init__()

   if safety_checker is None:
     logger.warning(
         f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
         """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
         """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
         """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
         """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
         """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""		)

   self.register_modules(
       speech_model=a_,      speech_processor=a_,      vae=a_,      text_encoder=a_,      tokenizer=a_,      unet=a_,      scheduler=a_,      feature_extractor=a_,      )


 def  UpperCamelCase_   (				self:		str,      a_:		Optional[Union[str, int]] = "auto"		):




   '''simple docstring'''



   if slice_size == "auto":
     _snake_case							:		int								=  self.unet.config.attention_head_dim // 2
   self.unet.set_attention_slice(a_		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   self.enable_attention_slicing(a_		)


 @torch.no_grad()
 def __call__(				self:		List[Any],      a_:		int,      a_:		List[str]=16_000,      a_:		int = 512,      a_:		int = 512,      a_:		int = 50,      a_:		float = 7.5,      a_:		Optional[Union[str, List[str]]] = None,      a_:		Optional[int] = 1,      a_:		float = 0.0,      a_:		Optional[torch.Generator] = None,      a_:		Optional[torch.FloatTensor] = None,      a_:		Optional[str] = "pil",      a_:		bool = True,      a_:		Optional[Callable[[int, int, torch.FloatTensor], None]] = None,      a_:		int = 1,      **a_:		int,      ):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.speech_processor.feature_extractor(
       a_,      return_tensors="""pt""",      sampling_rate=a_		).input_features.to(self.device		)
   _snake_case							:		Any								=  self.speech_model.generate(a_,      max_length=480_000		)

   _snake_case							:		Dict								=  self.speech_processor.tokenizer.batch_decode(a_,      skip_special_tokens=a_,      normalize=a_		)[
       0
   ]

   if isinstance(a_,      a_		):
     _snake_case							:		List[Any]								=  1
   elif isinstance(a_,      a_		):
     _snake_case							:		Union[str, Any]								=  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}."		)

   if (callback_steps is None) or (
       callback_steps is not None and (not isinstance(a_,      a_		) or callback_steps <= 0)
   ):
     raise ValueError(
         f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
         f" {type(a_		)}."		)

   # get prompt text embeddings
   _snake_case							:		Dict								=  self.tokenizer(
       a_,      padding="""max_length""",      max_length=self.tokenizer.model_max_length,      return_tensors="""pt""",      )
   _snake_case							:		Union[str, Any]								=  text_inputs.input_ids

   if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
     _snake_case							:		Union[str, Any]								=  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
     logger.warning(
         """The following part of your input was truncated because CLIP can only handle sequences up to"""
         f" {self.tokenizer.model_max_length} tokens: {removed_text}"		)
     _snake_case							:		str								=  text_input_ids[:, : self.tokenizer.model_max_length]
   _snake_case							:		Dict								=  self.text_encoder(text_input_ids.to(self.device		)		)[0]

   # duplicate text embeddings for each generation per prompt, using mps friendly method
   _snake_case							:		Any								=  text_embeddings.shape
   _snake_case							:		str								=  text_embeddings.repeat(1,      a_,      1		)
   _snake_case							:		List[Any]								=  text_embeddings.view(bs_embed * num_images_per_prompt,      a_,      -1		)

   # 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.
   _snake_case							:		List[Any]								=  guidance_scale > 1.0
   # get unconditional embeddings for classifier free guidance
   if do_classifier_free_guidance:
     _snake_case							:		List[str]
     if negative_prompt is None:
       _snake_case							:		str								=  [""""""] * batch_size
     elif type(a_		) is not type(a_		):
       raise TypeError(
           f"`negative_prompt` should be the same type to `prompt`, but got {type(a_		)} !="
           f" {type(a_		)}."		)
     elif isinstance(a_,      a_		):
       _snake_case							:		List[Any]								=  [negative_prompt]
     elif batch_size != len(a_		):
       raise ValueError(
           f"`negative_prompt`: {negative_prompt} has batch size {len(a_		)}, but `prompt`:"
           f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
           """ the batch size of `prompt`."""		)
     else:
       _snake_case							:		int								=  negative_prompt

     _snake_case							:		Tuple								=  text_input_ids.shape[-1]
     _snake_case							:		List[Any]								=  self.tokenizer(
         a_,      padding="""max_length""",      max_length=a_,      truncation=a_,      return_tensors="""pt""",      )
     _snake_case							:		Tuple								=  self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
     _snake_case							:		Dict								=  uncond_embeddings.shape[1]
     _snake_case							:		Tuple								=  uncond_embeddings.repeat(1,      a_,      1		)
     _snake_case							:		Tuple								=  uncond_embeddings.view(batch_size * num_images_per_prompt,      a_,      -1		)

     # 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
     _snake_case							:		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`.
   _snake_case							:		List[Any]								=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
   _snake_case							:		Tuple								=  text_embeddings.dtype
   if latents is None:
     if self.device.type == "mps":
       # randn does not exist on mps
       _snake_case							:		List[Any]								=  torch.randn(a_,      generator=a_,      device="""cpu""",      dtype=a_		).to(
           self.device		)
     else:
       _snake_case							:		str								=  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}"		)
     _snake_case							:		Tuple								=  latents.to(self.device		)

   # set timesteps
   self.scheduler.set_timesteps(a_		)

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

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

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

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

     # perform guidance
     if do_classifier_free_guidance:
       _snake_case							:		Tuple								=  noise_pred.chunk(2		)
       _snake_case							:		Optional[Any]								=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

     # compute the previous noisy sample x_t -> x_t-1
     _snake_case							:		Union[str, Any]								=  self.scheduler.step(a_,      a_,      a_,      **a_		).prev_sample

     # call the callback, if provided
     if callback is not None and i % callback_steps == 0:
       callback(a_,      a_,      a_		)

   _snake_case							:		Tuple								=  1 / 0.18_215 * latents
   _snake_case							:		Union[str, Any]								=  self.vae.decode(a_		).sample

   _snake_case							:		int								=  (image / 2 + 0.5).clamp(0,      1		)

   # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
   _snake_case							:		List[str]								=  image.cpu().permute(0,      2,      3,      1		).float().numpy()

   if output_type == "pil":
     _snake_case							:		Optional[Any]								=  self.numpy_to_pil(a_		)

   if not return_dict:
     return image

   return StableDiffusionPipelineOutput(images=a_,      nsfw_content_detected=a_		)






'''simple docstring'''







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


A__:    Dict					    =       logging.get_logger(__name__)

A__:    Tuple					    =       {
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  Tuple	       =							"roc_bert"






      def __init__( self			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:Tuple=3_0_5_2_2     ,						SCREAMING_SNAKE_CASE			:List[str]=7_6_8     ,						SCREAMING_SNAKE_CASE			:Dict=1_2     ,						SCREAMING_SNAKE_CASE			:List[str]=1_2     ,						SCREAMING_SNAKE_CASE			:Tuple=3_0_7_2     ,						SCREAMING_SNAKE_CASE			:List[Any]="gelu"     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.1     ,						SCREAMING_SNAKE_CASE			:List[Any]=0.1     ,						SCREAMING_SNAKE_CASE			:int=5_1_2     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=2     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.02     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=1e-12     ,						SCREAMING_SNAKE_CASE			:Any=True     ,						SCREAMING_SNAKE_CASE			:List[Any]=0     ,						SCREAMING_SNAKE_CASE			:Optional[int]="absolute"     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=None     ,						SCREAMING_SNAKE_CASE			:List[Any]=True     ,						SCREAMING_SNAKE_CASE			:int=True     ,						SCREAMING_SNAKE_CASE			:Optional[int]=7_6_8     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=9_1_0     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=5_1_2     ,						SCREAMING_SNAKE_CASE			:str=2_4_8_5_8     ,						SCREAMING_SNAKE_CASE			:List[Any]=True     ,						**SCREAMING_SNAKE_CASE			:Tuple     ,						)       ->					Optional[int]:




             '''simple docstring'''
             _a    : List[str]     					=vocab_size
             _a    : List[str]     					=max_position_embeddings
             _a    : Optional[Any]     					=hidden_size
             _a    : List[Any]     					=num_hidden_layers
             _a    : List[str]     					=num_attention_heads
             _a    : int     					=intermediate_size
             _a    : Any     					=hidden_act
             _a    : Dict     					=hidden_dropout_prob
             _a    : int     					=attention_probs_dropout_prob
             _a    : str     					=initializer_range
             _a    : Optional[int]     					=type_vocab_size
             _a    : Any     					=layer_norm_eps
             _a    : Any     					=use_cache
             _a    : Optional[int]     					=enable_pronunciation
             _a    : Optional[Any]     					=enable_shape
             _a    : Optional[Any]     					=pronunciation_embed_dim
             _a    : Tuple     					=pronunciation_vocab_size
             _a    : Union[str, Any]     					=shape_embed_dim
             _a    : Any     					=shape_vocab_size
             _a    : Tuple     					=concat_input
             _a    : List[str]     					=position_embedding_type
             _a    : List[str]     					=classifier_dropout
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)


