




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


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







class 						A(		_a     ):



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						Optional[Any]		,							A_      :						Union[str, Any]		,							A_      :						Optional[int]		,							A_      :						str		,							A_      :						List[Any]		,							A_      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						Union[str, Any]		,							A_      :						Tuple		,							)     ->			List[Any]:

       """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        a__				(       self      :						Optional[Any]		,							A_      :						Union[str, Any] = "auto"						)     ->			Optional[int]:

       """simple docstring"""






       if slice_size == "auto":
             lowerCamelCase_	  =					self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(_A						)

 def        a__				(       self      :						Any						)     ->			List[Any]:

       """simple docstring"""






       self.enable_attention_slicing(_A						)





 @torch.no_grad()
 def __call__(       self      :						List[Any]		,							A_      :						List[Any]		,							A_      :						Tuple=16000		,							A_      :						Optional[Any] = 512		,							A_      :						str = 512		,							A_      :						List[Any] = 50		,							A_      :						Any = 7.5		,							A_      :						List[str] = None		,							A_      :						Optional[int] = 1		,							A_      :						Dict = 0.0		,							A_      :						Dict = None		,							A_      :						Optional[int] = None		,							A_      :						str = "pil"		,							A_      :						str = True		,							A_      :						Optional[int] = None		,							A_      :						Optional[Any] = 1		,							**A_      :						Dict		,							)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.speech_processor.feature_extractor(
           _A		,							return_tensors='pt'		,							sampling_rate=_A						).input_features.to(self.device						)
       lowerCamelCase_	  =					self.speech_model.generate(_A		,							max_length=480000						)

       lowerCamelCase_	  =					self.speech_processor.tokenizer.batch_decode(_A		,							skip_special_tokens=_A		,							normalize=_A						)[
           0
       ]

       if isinstance(_A		,							_A						):
             lowerCamelCase_	  =					1
       elif isinstance(_A		,							_A						):
             lowerCamelCase_	  =					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
       lowerCamelCase_	  =					self.tokenizer(
           _A		,							padding='max_length'		,							max_length=self.tokenizer.model_max_length		,							return_tensors='pt'		,							)
       lowerCamelCase_	  =					text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             lowerCamelCase_	  =					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}"""						)
             lowerCamelCase_	  =					text_input_ids[:, : self.tokenizer.model_max_length]
       lowerCamelCase_	  =					self.text_encoder(text_input_ids.to(self.device						)						)[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					text_embeddings.shape
       lowerCamelCase_	  =					text_embeddings.repeat(1		,							_A		,							1						)
       lowerCamelCase_	  =					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.
       lowerCamelCase_	  =					guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
             lowerCamelCase_	  =					42
             if negative_prompt is None:
                   lowerCamelCase_	  =					[''] * 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						):
                   lowerCamelCase_	  =					[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:
                   lowerCamelCase_	  =					negative_prompt

             lowerCamelCase_	  =					text_input_ids.shape[-1]
             lowerCamelCase_	  =					self.tokenizer(
                 _A		,							padding='max_length'		,							max_length=_A		,							truncation=_A		,							return_tensors='pt'		,							)
             lowerCamelCase_	  =					self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             lowerCamelCase_	  =					uncond_embeddings.shape[1]
             lowerCamelCase_	  =					uncond_embeddings.repeat(1		,							_A		,							1						)
             lowerCamelCase_	  =					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
             lowerCamelCase_	  =					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`.
       lowerCamelCase_	  =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       lowerCamelCase_	  =					text_embeddings.dtype
       if latents is None:
             if self.device.type == "mps":
                   # randn does not exist on mps
                   lowerCamelCase_	  =					torch.randn(_A		,							generator=_A		,							device='cpu'		,							dtype=_A						).to(
                       self.device						)
             else:
                   lowerCamelCase_	  =					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}"""						)
             lowerCamelCase_	  =					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
       lowerCamelCase_	  =					self.scheduler.timesteps.to(self.device						)

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

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

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

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

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase_	  =					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						)

       lowerCamelCase_	  =					1 / 0.18215 * latents
       lowerCamelCase_	  =					self.vae.decode(_A						).sample

       lowerCamelCase_	  =					(image / 2 + 0.5).clamp(0		,							1						)

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

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

       if not return_dict:
             return image

       return StableDiffusionPipelineOutput(images=_A		,							nsfw_content_detected=_A						)









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase     )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		field(default='''language-modeling'''							,   metadata={'''include_in_asdict_even_if_is_default''': True}     )

 UpperCamelCase		     =		Features({'''text''': Value('''string'''     )}     )
 UpperCamelCase		     =		Features({}     )
 UpperCamelCase		     =		"text"

 @property
 def        a__				(       self      :						List[Any]						)     ->			Dict[str, str]:

       """simple docstring"""






       return {self.text_column: "text"}



