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




class    _UpperCamelCase      (						lowercase_ ):


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   )     ->					List[str]:

							'''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=__UpperCamelCase      ,   speech_processor=__UpperCamelCase      ,   vae=__UpperCamelCase      ,   text_encoder=__UpperCamelCase      ,   tokenizer=__UpperCamelCase      ,   unet=__UpperCamelCase      ,   scheduler=__UpperCamelCase      ,   feature_extractor=__UpperCamelCase      ,   )





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__ = "auto"						)     ->					int:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							self.enable_attention_slicing(__UpperCamelCase						)





			@torch.no_grad()
			def __call__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=1_60_00      ,   lowerCAmelCase__ = 5_12      ,   lowerCAmelCase__ = 5_12      ,   lowerCAmelCase__ = 50      ,   lowerCAmelCase__ = 7.5      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = 1      ,   lowerCAmelCase__ = 0.0      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = "pil"      ,   lowerCAmelCase__ = True      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = 1      ,   **lowerCAmelCase__      ,   )     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							self.speech_processor.feature_extractor(
							    __UpperCamelCase      ,   return_tensors='''pt'''      ,   sampling_rate=__UpperCamelCase						).input_features.to(self.device						)
							__lowercase 			=							self.speech_model.generate(__UpperCamelCase      ,   max_length=48_00_00						)

							__lowercase 			=							self.speech_processor.tokenizer.batch_decode(__UpperCamelCase      ,   skip_special_tokens=__UpperCamelCase      ,   normalize=__UpperCamelCase						)[
							    0
							]

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

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

							# get prompt text embeddings
							__lowercase 			=							self.tokenizer(
							    __UpperCamelCase      ,   padding='''max_length'''      ,   max_length=self.tokenizer.model_max_length      ,   return_tensors='''pt'''      ,   )
							__lowercase 			=							text_inputs.input_ids

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

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

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

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

							# set timesteps
							self.scheduler.set_timesteps(__UpperCamelCase						)

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

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

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

											# predict the noise residual
											__lowercase 			=							self.unet(__UpperCamelCase      ,   __UpperCamelCase      ,   encoder_hidden_states=__UpperCamelCase						).sample

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

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

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

							__lowercase 			=							1 / 0.1_8215 * latents
							__lowercase 			=							self.vae.decode(__UpperCamelCase						).sample

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

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

							if output_type == "pil":
											__lowercase 			=							self.numpy_to_pil(__UpperCamelCase						)

							if not return_dict:
											return image

							return StableDiffusionPipelineOutput(images=__UpperCamelCase      ,   nsfw_content_detected=__UpperCamelCase						)
"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   def __init__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      None:


        '''simple docstring'''


        warnings.warn(
            'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use BeitImageProcessor instead.'     ,__UpperCamelCase     ,)
        super().__init__(*__UpperCamelCase     ,**__UpperCamelCase			)

