






"""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


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



class      SCREAMING_SNAKE_CASE  (				UpperCamelCase__						):





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     ):
		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    lowerCamelCase(self     ,     lowerCAmelCase_ = "auto"							):
		if slice_size == "auto":
			A_		:						Any	    =   self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(_a							)





	def    lowerCamelCase(self							):
		self.enable_attention_slicing(_a							)





	@torch.no_grad()
	def __call__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=16000     ,     lowerCAmelCase_ = 512     ,     lowerCAmelCase_ = 512     ,     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_     ,     ):
		A_		:						Dict	    =   self.speech_processor.feature_extractor(
		    _a     ,     return_tensors="""pt"""     ,     sampling_rate=_a							).input_features.to(self.device							)
		A_		:						Optional[int]	    =   self.speech_model.generate(_a     ,     max_length=480000							)

		A_		:						Union[str, Any]	    =   self.speech_processor.tokenizer.batch_decode(_a     ,     skip_special_tokens=_a     ,     normalize=_a							)[
		    0
		]

		if isinstance(_a     ,     _a							):
			A_		:						Any	    =   1
		elif isinstance(_a     ,     _a							):
			A_		:						Optional[int]	    =   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
		A_		:						List[Any]	    =   self.tokenizer(
		    _a     ,     padding="""max_length"""     ,     max_length=self.tokenizer.model_max_length     ,     return_tensors="""pt"""     ,     )
		A_		:						Union[str, Any]	    =   text_inputs.input_ids

		if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
			A_		:						List[str]	    =   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}"""							)
			A_		:						str	    =   text_input_ids[:, : self.tokenizer.model_max_length]
		A_		:						Optional[int]	    =   self.text_encoder(text_input_ids.to(self.device							)							)[0]

		# duplicate text embeddings for each generation per prompt, using mps friendly method
		A_		:						List[Any]	    =   text_embeddings.shape
		A_		:						Tuple	    =   text_embeddings.repeat(1     ,     _a     ,     1							)
		A_		:						Union[str, 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.
		A_		:						Any	    =   guidance_scale > 1.0
		# get unconditional embeddings for classifier free guidance
		if do_classifier_free_guidance:
			A_		:						List[str]
			if negative_prompt is None:
				A_		:						List[Any]	    =   [""""""] * 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							):
				A_		:						str	    =   [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:
				A_		:						Optional[int]	    =   negative_prompt

			A_		:						Tuple	    =   text_input_ids.shape[-1]
			A_		:						Dict	    =   self.tokenizer(
			    _a     ,     padding="""max_length"""     ,     max_length=_a     ,     truncation=_a     ,     return_tensors="""pt"""     ,     )
			A_		:						int	    =   self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

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

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

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

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

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

			# compute the previous noisy sample x_t -> x_t-1
			A_		:						str	    =   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							)

		A_		:						List[str]	    =   1 / 0.18215 * latents
		A_		:						List[str]	    =   self.vae.decode(_a							).sample

		A_		:						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
		A_		:						int	    =   image.cpu().permute(0     ,     2     ,     3     ,     1							).float().numpy()

		if output_type == "pil":
			A_		:						Dict	    =   self.numpy_to_pil(_a							)

		if not return_dict:
			return image

		return StableDiffusionPipelineOutput(images=_a     ,     nsfw_content_detected=_a							)









"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				str    =				"""AutoImageProcessor"""
	_A       :				Union[str, Any]    =				"""AutoTokenizer"""





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		super().__init__(lowerCAmelCase_     ,     lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   self.image_processor





	def __call__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):

		if text is None and images is None:
			raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

		if text is not None:
			A_		:						Optional[int]	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						List[Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if text is not None and images is not None:
			A_		:						int	    =   image_features.pixel_values
			return encoding
		elif text is not None:
			return encoding
		else:
			return BatchEncoding(data=dict(**lowerCAmelCase_							)     ,     tensor_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return ["input_ids", "attention_mask", "pixel_values"]

