



"""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


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






					'''simple docstring'''





					def __init__(       self:							Tuple						,     snake_case:							WhisperForConditionalGeneration						,     snake_case:							WhisperProcessor						,     snake_case:							AutoencoderKL						,     snake_case:							CLIPTextModel						,     snake_case:							CLIPTokenizer						,     snake_case:							UNetaDConditionModel						,     snake_case:							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,     snake_case:							StableDiffusionSafetyChecker						,     snake_case:							CLIPImageProcessor						,     )   ->	List[Any]:
											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    lowerCAmelCase_				(       self:							Dict						,     snake_case:							Optional[Union[str, int]] = "auto"						)   ->	Any:
											if slice_size == "auto":
																	snake_case_ :Optional[int]						=			self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(snake_case						)






					def    lowerCAmelCase_				(       self:							Any						)   ->	Union[str, Any]:
											self.enable_attention_slicing(snake_case						)






					@torch.no_grad()
					def __call__(       self:							List[str]						,     snake_case:							Dict						,     snake_case:							Tuple=16_000						,     snake_case:							int = 512						,     snake_case:							int = 512						,     snake_case:							int = 50						,     snake_case:							float = 7.5						,     snake_case:							Optional[Union[str, List[str]]] = None						,     snake_case:							Optional[int] = 1						,     snake_case:							float = 0.0						,     snake_case:							Optional[torch.Generator] = None						,     snake_case:							Optional[torch.FloatTensor] = None						,     snake_case:							Optional[str] = "pil"						,     snake_case:							bool = True						,     snake_case:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,     snake_case:							int = 1						,     **snake_case:							str						,     )   ->	Tuple:
											snake_case_ :List[Any]						=			self.speech_processor.feature_extractor(
											    snake_case						,     return_tensors="""pt"""						,     sampling_rate=snake_case						).input_features.to(self.device						)
											snake_case_ :Dict						=			self.speech_model.generate(snake_case						,     max_length=480_000						)

											snake_case_ :Optional[int]						=			self.speech_processor.tokenizer.batch_decode(snake_case						,     skip_special_tokens=snake_case						,     normalize=snake_case						)[
											    0
											]

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

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

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

																	snake_case_ :List[str]						=			text_input_ids.shape[-1]
																	snake_case_ :Any						=			self.tokenizer(
																	    snake_case						,     padding="""max_length"""						,     max_length=snake_case						,     truncation=snake_case						,     return_tensors="""pt"""						,     )
																	snake_case_ :int						=			self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

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

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

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

																	# predict the noise residual
																	snake_case_ :str						=			self.unet(snake_case						,     snake_case						,     encoder_hidden_states=snake_case						).sample

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

																	# compute the previous noisy sample x_t -> x_t-1
																	snake_case_ :Optional[Any]						=			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						)

											snake_case_ :Tuple						=			1 / 0.1_8_2_1_5 * latents
											snake_case_ :int						=			self.vae.decode(snake_case						).sample

											snake_case_ :Optional[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
											snake_case_ :List[Any]						=			image.cpu().permute(0						,     2						,     3						,     1						).float().numpy()

											if output_type == "pil":
																	snake_case_ :Optional[int]						=			self.numpy_to_pil(snake_case						)

											if not return_dict:
																	return image

											return StableDiffusionPipelineOutput(images=snake_case						,     nsfw_content_detected=snake_case						)


import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= []
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=UpperCAmelCase_       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= FlaxTransformeraDModel(
											    in_channels=self.out_channels       ,   n_heads=self.num_attention_heads       ,   d_head=self.out_channels // self.num_attention_heads       ,   depth=1       ,   use_linear_projection=self.use_linear_projection       ,   only_cross_attention=self.only_cross_attention       ,   use_memory_efficient_attention=self.use_memory_efficient_attention       ,   dtype=self.dtype       ,   )
											attentions.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets
						SCREAMING_SNAKE_CASE__													= attentions

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= FlaxDownsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   int       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   int=True							):
						SCREAMING_SNAKE_CASE__													= ()

						for resnet, attn in zip(self.resnets       ,   self.attentions							):
											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= attn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											output_states += (hidden_states,)

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= self.downsamplers_a(UpperCAmelCase_							)
											output_states += (hidden_states,)

						return hidden_states, output_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=UpperCAmelCase_       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= resnets

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= FlaxDownsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   List[str]       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   List[Any]=True							):
						SCREAMING_SNAKE_CASE__													= ()

						for resnet in self.resnets:
											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											output_states += (hidden_states,)

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= self.downsamplers_a(UpperCAmelCase_							)
											output_states += (hidden_states,)

						return hidden_states, output_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= []
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if (i == self.num_layers - 1) else self.out_channels
											SCREAMING_SNAKE_CASE__													= self.prev_output_channel if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= FlaxTransformeraDModel(
											    in_channels=self.out_channels       ,   n_heads=self.num_attention_heads       ,   d_head=self.out_channels // self.num_attention_heads       ,   depth=1       ,   use_linear_projection=self.use_linear_projection       ,   only_cross_attention=self.only_cross_attention       ,   use_memory_efficient_attention=self.use_memory_efficient_attention       ,   dtype=self.dtype       ,   )
											attentions.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets
						SCREAMING_SNAKE_CASE__													= attentions

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= FlaxUpsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   str       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Union[str, Any]=True							):
						for resnet, attn in zip(self.resnets       ,   self.attentions							):
											# pop res hidden states
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[-1]
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[:-1]
											SCREAMING_SNAKE_CASE__													= jnp.concatenate((hidden_states, res_hidden_states)       ,   axis=-1							)

											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= attn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= self.upsamplers_a(UpperCAmelCase_							)

						return hidden_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if (i == self.num_layers - 1) else self.out_channels
											SCREAMING_SNAKE_CASE__													= self.prev_output_channel if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= FlaxUpsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   Tuple       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Dict=True							):
						for resnet in self.resnets:
											# pop res hidden states
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[-1]
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[:-1]
											SCREAMING_SNAKE_CASE__													= jnp.concatenate((hidden_states, res_hidden_states)       ,   axis=-1							)

											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= self.upsamplers_a(UpperCAmelCase_							)

						return hidden_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   int				       =1
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   Optional[int]							):
						# there is always at least one resnet
						SCREAMING_SNAKE_CASE__													= [
						    FlaxResnetBlockaD(
						        in_channels=self.in_channels       ,   out_channels=self.in_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
						]

						SCREAMING_SNAKE_CASE__													= []

						for _ in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= FlaxTransformeraDModel(
											    in_channels=self.in_channels       ,   n_heads=self.num_attention_heads       ,   d_head=self.in_channels // self.num_attention_heads       ,   depth=1       ,   use_linear_projection=self.use_linear_projection       ,   use_memory_efficient_attention=self.use_memory_efficient_attention       ,   dtype=self.dtype       ,   )
											attentions.append(UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=self.in_channels       ,   out_channels=self.in_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets
						SCREAMING_SNAKE_CASE__													= attentions





	def __call__(			self							:   str       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   Optional[int]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   Any=True							):
						SCREAMING_SNAKE_CASE__													= self.resnets[0](UpperCAmelCase_       ,   UpperCAmelCase_							)
						for attn, resnet in zip(self.attentions       ,   self.resnets[1:]							):
											SCREAMING_SNAKE_CASE__													= attn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)

						return hidden_states





