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      :			Optional[int]							       =		logging.get_logger(__name__)  # pylint: disable=invalid-name







class __snake_case    (						lowerCamelCase_		):




   def __init__(   self      :    Union[str, Any]	,     _lowercase      :    WhisperForConditionalGeneration	,     _lowercase      :    WhisperProcessor	,     _lowercase      :    AutoencoderKL	,     _lowercase      :    CLIPTextModel	,     _lowercase      :    CLIPTokenizer	,     _lowercase      :    UNetaDConditionModel	,     _lowercase      :    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]	,     _lowercase      :    StableDiffusionSafetyChecker	,     _lowercase      :    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=_lowercase	,     speech_processor=_lowercase	,     vae=_lowercase	,     text_encoder=_lowercase	,     tokenizer=_lowercase	,     unet=_lowercase	,     scheduler=_lowercase	,     feature_extractor=_lowercase	,     )




   def      __a						(   self      :    List[str]	,     _lowercase      :    Optional[Union[str, int]] = "auto"				):



     """simple docstring"""






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




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     self.enable_attention_slicing(_lowercase				)




   @torch.no_grad()
   def __call__(   self      :    Optional[int]	,     _lowercase      :    Dict	,     _lowercase      :    List[str]=1_60_00	,     _lowercase      :    int = 5_12	,     _lowercase      :    int = 5_12	,     _lowercase      :    int = 50	,     _lowercase      :    float = 7.5	,     _lowercase      :    Optional[Union[str, List[str]]] = None	,     _lowercase      :    Optional[int] = 1	,     _lowercase      :    float = 0.0	,     _lowercase      :    Optional[torch.Generator] = None	,     _lowercase      :    Optional[torch.FloatTensor] = None	,     _lowercase      :    Optional[str] = "pil"	,     _lowercase      :    bool = True	,     _lowercase      :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,     _lowercase      :    int = 1	,     **_lowercase      :    List[str]	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.speech_processor.feature_extractor(
         _lowercase	,     return_tensors="""pt"""	,     sampling_rate=_lowercase				).input_features.to(self.device				)
     SCREAMING_SNAKE_CASE__      							=				self.speech_model.generate(_lowercase	,     max_length=48_00_00				)

     SCREAMING_SNAKE_CASE__      							=				self.speech_processor.tokenizer.batch_decode(_lowercase	,     skip_special_tokens=_lowercase	,     normalize=_lowercase				)[
         0
     ]

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

     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(_lowercase	,     _lowercase				) or callback_steps <= 0)
     ):
       raise ValueError(
           f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
           f""" {type(_lowercase				)}."""				)

     # get prompt text embeddings
     SCREAMING_SNAKE_CASE__      							=				self.tokenizer(
         _lowercase	,     padding="""max_length"""	,     max_length=self.tokenizer.model_max_length	,     return_tensors="""pt"""	,     )
     SCREAMING_SNAKE_CASE__      							=				text_inputs.input_ids

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

     # duplicate text embeddings for each generation per prompt, using mps friendly method
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				text_embeddings.shape
     SCREAMING_SNAKE_CASE__      							=				text_embeddings.repeat(1	,     _lowercase	,     1				)
     SCREAMING_SNAKE_CASE__      							=				text_embeddings.view(bs_embed * num_images_per_prompt	,     _lowercase	,     -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.
     SCREAMING_SNAKE_CASE__      							=				guidance_scale > 1.0
     # get unconditional embeddings for classifier free guidance
     if do_classifier_free_guidance:
       SCREAMING_SNAKE_CASE__      							=				42
       if negative_prompt is None:
         SCREAMING_SNAKE_CASE__      							=				[""""""] * batch_size
       elif type(_lowercase				) is not type(_lowercase				):
         raise TypeError(
             f"""`negative_prompt` should be the same type to `prompt`, but got {type(_lowercase				)} !="""
             f""" {type(_lowercase				)}."""				)
       elif isinstance(_lowercase	,     _lowercase				):
         SCREAMING_SNAKE_CASE__      							=				[negative_prompt]
       elif batch_size != len(_lowercase				):
         raise ValueError(
             f"""`negative_prompt`: {negative_prompt} has batch size {len(_lowercase				)}, but `prompt`:"""
             f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
             """ the batch size of `prompt`."""				)
       else:
         SCREAMING_SNAKE_CASE__      							=				negative_prompt

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

       # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
       SCREAMING_SNAKE_CASE__      							=				uncond_embeddings.shape[1]
       SCREAMING_SNAKE_CASE__      							=				uncond_embeddings.repeat(1	,     _lowercase	,     1				)
       SCREAMING_SNAKE_CASE__      							=				uncond_embeddings.view(batch_size * num_images_per_prompt	,     _lowercase	,     -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
       SCREAMING_SNAKE_CASE__      							=				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`.
     SCREAMING_SNAKE_CASE__      							=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
     SCREAMING_SNAKE_CASE__      							=				text_embeddings.dtype
     if latents is None:
       if self.device.type == "mps":
         # randn does not exist on mps
         SCREAMING_SNAKE_CASE__      							=				torch.randn(_lowercase	,     generator=_lowercase	,     device="""cpu"""	,     dtype=_lowercase				).to(
             self.device				)
       else:
         SCREAMING_SNAKE_CASE__      							=				torch.randn(_lowercase	,     generator=_lowercase	,     device=self.device	,     dtype=_lowercase				)
     else:
       if latents.shape != latents_shape:
         raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""				)
       SCREAMING_SNAKE_CASE__      							=				latents.to(self.device				)

     # set timesteps
     self.scheduler.set_timesteps(_lowercase				)

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

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

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

       # predict the noise residual
       SCREAMING_SNAKE_CASE__      							=				self.unet(_lowercase	,     _lowercase	,     encoder_hidden_states=_lowercase				).sample

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

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

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

     SCREAMING_SNAKE_CASE__      							=				1 / 0.1_82_15 * latents
     SCREAMING_SNAKE_CASE__      							=				self.vae.decode(_lowercase				).sample

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

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

     if output_type == "pil":
       SCREAMING_SNAKE_CASE__      							=				self.numpy_to_pil(_lowercase				)

     if not return_dict:
       return image

     return StableDiffusionPipelineOutput(images=_lowercase	,     nsfw_content_detected=_lowercase				)

import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


__lowerCamelCase      :			str							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):




   def __init__(   self      :    List[str]	,     *_lowercase      :    Union[str, Any]	,     **_lowercase      :    Union[str, Any]				):



     """simple docstring"""






     warnings.warn(
         """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use DonutImageProcessor instead."""	,     _lowercase	,     )
     super().__init__(*_lowercase	,     **_lowercase				)
