


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







class 			_lowercase (							__lowerCamelCase):


       """simple docstring"""




       def __init__(   self							:			List[Any]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			Dict					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			Dict					,      ):


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





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			int = "auto"       ):


             '''simple docstring'''



             if slice_size == "auto":
                   lowerCamelCase__		:				Optional[int]    =  self.unet.config.attention_head_dim // 2
             self.unet.set_attention_slice(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             self.enable_attention_slicing(__lowerCamelCase       )





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


             '''simple docstring'''



             lowerCamelCase__		:				Optional[int]    =  self.speech_processor.feature_extractor(
                 __lowerCamelCase					,      return_tensors="pt"					,      sampling_rate=__lowerCamelCase       ).input_features.to(self.device       )
             lowerCamelCase__		:				Optional[int]    =  self.speech_model.generate(__lowerCamelCase					,      max_length=480000       )

             lowerCamelCase__		:				List[str]    =  self.speech_processor.tokenizer.batch_decode(__lowerCamelCase					,      skip_special_tokens=__lowerCamelCase					,      normalize=__lowerCamelCase       )[
                 0
             ]

             if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   lowerCamelCase__		:				Optional[Any]    =  1
             elif isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   lowerCamelCase__		:				Optional[int]    =  len(__lowerCamelCase       )
             else:
                   raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__lowerCamelCase       )}"       )

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

             # get prompt text embeddings
             lowerCamelCase__		:				int    =  self.tokenizer(
                 __lowerCamelCase					,      padding="max_length"					,      max_length=self.tokenizer.model_max_length					,      return_tensors="pt"					,      )
             lowerCamelCase__		:				int    =  text_inputs.input_ids

             if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                   lowerCamelCase__		:				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}"       )
                   lowerCamelCase__		:				Dict    =  text_input_ids[:, : self.tokenizer.model_max_length]
             lowerCamelCase__		:				List[str]    =  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__		:				List[str]    =  text_embeddings.shape
             lowerCamelCase__		:				str    =  text_embeddings.repeat(1					,      __lowerCamelCase					,      1       )
             lowerCamelCase__		:				List[Any]    =  text_embeddings.view(bs_embed * num_images_per_prompt					,      __lowerCamelCase					,      -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__		:				Dict    =  guidance_scale > 1.0
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
                   lowerCamelCase__		:				List[str]    =  42
                   if negative_prompt is None:
                         lowerCamelCase__		:				int    =  [""] * batch_size
                   elif type(__lowerCamelCase       ) is not type(__lowerCamelCase       ):
                         raise TypeError(
                             f"`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCamelCase       )} !="
                             f" {type(__lowerCamelCase       )}."       )
                   elif isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                         lowerCamelCase__		:				Union[str, Any]    =  [negative_prompt]
                   elif batch_size != len(__lowerCamelCase       ):
                         raise ValueError(
                             f"`negative_prompt`: {negative_prompt} has batch size {len(__lowerCamelCase       )}, but `prompt`:"
                             f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                             " the batch size of `prompt`."       )
                   else:
                         lowerCamelCase__		:				Optional[int]    =  negative_prompt

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

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

             # set timesteps
             self.scheduler.set_timesteps(__lowerCamelCase       )

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

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

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

                   # predict the noise residual
                   lowerCamelCase__		:				Union[str, Any]    =  self.unet(__lowerCamelCase					,      __lowerCamelCase					,      encoder_hidden_states=__lowerCamelCase       ).sample

                   # perform guidance
                   if do_classifier_free_guidance:
                         lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  noise_pred.chunk(2       )
                         lowerCamelCase__		:				List[Any]    =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   # compute the previous noisy sample x_t -> x_t-1
                   lowerCamelCase__		:				Optional[Any]    =  self.scheduler.step(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      **__lowerCamelCase       ).prev_sample

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

             lowerCamelCase__		:				Optional[Any]    =  1 / 0.1_8_2_1_5 * latents
             lowerCamelCase__		:				Optional[Any]    =  self.vae.decode(__lowerCamelCase       ).sample

             lowerCamelCase__		:				Optional[Any]    =  (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__		:				int    =  image.cpu().permute(0					,      2					,      3					,      1       ).float().numpy()

             if output_type == "pil":
                   lowerCamelCase__		:				str    =  self.numpy_to_pil(__lowerCamelCase       )

             if not return_dict:
                   return image

             return StableDiffusionPipelineOutput(images=__lowerCamelCase					,      nsfw_content_detected=__lowerCamelCase       )




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


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







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    ):
		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=snake_case		,    speech_processor=snake_case		,    vae=snake_case		,    text_encoder=snake_case		,    tokenizer=snake_case		,    unet=snake_case		,    scheduler=snake_case		,    feature_extractor=snake_case		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case = "auto"       ):
		if slice_size == "auto":
			lowercase       =    self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self.enable_attention_slicing(snake_case       )





	@torch.no_grad()
	def __call__(       self		,    snake_case		,    snake_case=1_6000		,    snake_case = 512		,    snake_case = 512		,    snake_case = 50		,    snake_case = 7.5		,    snake_case = None		,    snake_case = 1		,    snake_case = 0.0		,    snake_case = None		,    snake_case = None		,    snake_case = "pil"		,    snake_case = True		,    snake_case = None		,    snake_case = 1		,    **snake_case		,    ):
		lowercase       =    self.speech_processor.feature_extractor(
		    snake_case		,    return_tensors='pt'		,    sampling_rate=snake_case       ).input_features.to(self.device       )
		lowercase       =    self.speech_model.generate(snake_case		,    max_length=48_0000       )

		lowercase       =    self.speech_processor.tokenizer.batch_decode(snake_case		,    skip_special_tokens=snake_case		,    normalize=snake_case       )[
		    0
		]

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

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

		# get prompt text embeddings
		lowercase       =    self.tokenizer(
		    snake_case		,    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  ,					lowercase  ,					lowercase       =    text_embeddings.shape
		lowercase       =    text_embeddings.repeat(1		,    snake_case		,    1       )
		lowercase       =    text_embeddings.view(bs_embed * num_images_per_prompt		,    snake_case		,    -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(snake_case       ) is not type(snake_case       ):
				raise TypeError(
				    F'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case       )} !='''
				    F''' {type(snake_case       )}.'''       )
			elif isinstance(snake_case		,    snake_case       ):
				lowercase       =    [negative_prompt]
			elif batch_size != len(snake_case       ):
				raise ValueError(
				    F'''`negative_prompt`: {negative_prompt} has batch size {len(snake_case       )}, 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(
			    snake_case		,    padding='max_length'		,    max_length=snake_case		,    truncation=snake_case		,    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		,    snake_case		,    1       )
			lowercase       =    uncond_embeddings.view(batch_size * num_images_per_prompt		,    snake_case		,    -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(snake_case		,    generator=snake_case		,    device='cpu'		,    dtype=snake_case       ).to(
				    self.device       )
			else:
				lowercase       =    torch.randn(snake_case		,    generator=snake_case		,    device=self.device		,    dtype=snake_case       )
		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(snake_case       )

		# 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(snake_case       )       ):
			# 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(snake_case		,    snake_case       )

			# predict the noise residual
			lowercase       =    self.unet(snake_case		,    snake_case		,    encoder_hidden_states=snake_case       ).sample

			# perform guidance
			if do_classifier_free_guidance:
				lowercase  ,					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(snake_case		,    snake_case		,    snake_case		,    **snake_case       ).prev_sample

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

		lowercase       =    1 / 0.18_215 * latents
		lowercase       =    self.vae.decode(snake_case       ).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(snake_case       )

		if not return_dict:
			return image

		return StableDiffusionPipelineOutput(images=snake_case		,    nsfw_content_detected=snake_case       )

