





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     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



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



 def 				UpperCamelCase			(							self,__lowerCamelCase = "auto" ):
        if slice_size == "auto":
               A__											=						self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.enable_attention_slicing(__lowerCamelCase )




 @torch.no_grad()
 def __call__(							self,__lowerCamelCase,__lowerCamelCase=1_6000,__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__											=						self.speech_processor.feature_extractor(
            __lowerCamelCase,return_tensors='''pt''',sampling_rate=__lowerCamelCase ).input_features.to(self.device )
        A__											=						self.speech_model.generate(__lowerCamelCase,max_length=48_0000 )

        A__											=						self.speech_processor.tokenizer.batch_decode(__lowerCamelCase,skip_special_tokens=__lowerCamelCase,normalize=__lowerCamelCase )[
            0
        ]

        if isinstance(__lowerCamelCase,__lowerCamelCase ):
               A__											=						1
        elif isinstance(__lowerCamelCase,__lowerCamelCase ):
               A__											=						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
        A__											=						self.tokenizer(
            __lowerCamelCase,padding='''max_length''',max_length=self.tokenizer.model_max_length,return_tensors='''pt''',)
        A__											=						text_inputs.input_ids

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

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        A__					,	A__					,	A__											=						text_embeddings.shape
        A__											=						text_embeddings.repeat(1,__lowerCamelCase,1 )
        A__											=						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.
        A__											=						guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
               A__											=						42
               if negative_prompt is None:
                      A__											=						[''''''] * 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 ):
                      A__											=						[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:
                      A__											=						negative_prompt

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

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

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

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

               # predict the noise residual
               A__											=						self.unet(__lowerCamelCase,__lowerCamelCase,encoder_hidden_states=__lowerCamelCase ).sample

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

               # compute the previous noisy sample x_t -> x_t-1
               A__											=						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 )

        A__											=						1 / 0.18215 * latents
        A__											=						self.vae.decode(__lowerCamelCase ).sample

        A__											=						(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__											=						image.cpu().permute(0,2,3,1 ).float().numpy()

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

        if not return_dict:
               return image

        return StableDiffusionPipelineOutput(images=__lowerCamelCase,nsfw_content_detected=__lowerCamelCase )












import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DDIMParallelScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''eta''', 0.0), ('''num_inference_steps''', 50))



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter

        scheduler.set_timesteps(__lowerCamelCase )

        for t in scheduler.timesteps:
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(steps_offset=1 )
        A__											=						scheduler_class(**__lowerCamelCase )
        scheduler.set_timesteps(5 )
        assert torch.equal(scheduler.timesteps,torch.LongTensor([801, 601, 401, 201, 1] ) )



 def 				UpperCamelCase			(							self ):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],[0.002, 0.02, 0.2, 2] ):
               self.check_over_configs(beta_start=__lowerCamelCase,beta_end=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,)



 def 				UpperCamelCase			(							self ):
        for t in [1, 10, 49]:
               self.check_over_forward(time_step=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, num_inference_steps in zip([1, 10, 50],[10, 50, 500] ):
               self.check_over_forward(time_step=__lowerCamelCase,num_inference_steps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, eta in zip([1, 10, 49],[0.0, 0.5, 1.0] ):
               self.check_over_forward(time_step=__lowerCamelCase,eta=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,400 ) - 0.14771 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,960 ) - 0.32460 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,486 ) - 0.00979 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,998 ) - 0.02 ) ) < 1E-5



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0
        scheduler.set_timesteps(__lowerCamelCase )

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        A__											=						self.dummy_sample_deter + 0.1
        A__											=						self.dummy_sample_deter - 0.1

        A__											=						samplea.shape[0]
        A__											=						torch.stack([samplea, samplea, samplea],dim=0 )
        A__											=						torch.arange(__lowerCamelCase )[0:3, None].repeat(1,__lowerCamelCase )

        A__											=						model(samples.flatten(0,1 ),timesteps.flatten(0,1 ) )
        A__											=						scheduler.batch_step_no_noise(__lowerCamelCase,timesteps.flatten(0,1 ),samples.flatten(0,1 ),__lowerCamelCase )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 1147.7904 ) < 1E-2
        assert abs(result_mean.item() - 0.4982 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 172.0067 ) < 1E-2
        assert abs(result_mean.item() - 0.223967 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 52.5302 ) < 1E-2
        assert abs(result_mean.item() - 0.0684 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.8295 ) < 1E-2
        assert abs(result_mean.item() - 0.1951 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.0784 ) < 1E-2
        assert abs(result_mean.item() - 0.1941 ) < 1E-3





