

"""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


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



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: Union[str, Any]    ,						UpperCamelCase: WhisperForConditionalGeneration    ,						UpperCamelCase: WhisperProcessor    ,						UpperCamelCase: AutoencoderKL    ,						UpperCamelCase: CLIPTextModel    ,						UpperCamelCase: CLIPTokenizer    ,						UpperCamelCase: UNetaDConditionModel    ,						UpperCamelCase: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]    ,						UpperCamelCase: StableDiffusionSafetyChecker    ,						UpperCamelCase: CLIPImageProcessor    ,						):






         """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 				UpperCamelCase	(     self: str    ,						UpperCamelCase: Optional[Union[str, int]] = "auto"       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         self.enable_attention_slicing(UpperCamelCase       )





    @torch.no_grad()
    def __call__(     self: Dict    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: int=1_60_00    ,						UpperCamelCase: int = 5_12    ,						UpperCamelCase: int = 5_12    ,						UpperCamelCase: int = 50    ,						UpperCamelCase: float = 7.5    ,						UpperCamelCase: Optional[Union[str, List[str]]] = None    ,						UpperCamelCase: Optional[int] = 1    ,						UpperCamelCase: float = 0.0    ,						UpperCamelCase: Optional[torch.Generator] = None    ,						UpperCamelCase: Optional[torch.FloatTensor] = None    ,						UpperCamelCase: Optional[str] = "pil"    ,						UpperCamelCase: bool = True    ,						UpperCamelCase: Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,						UpperCamelCase: int = 1    ,						**UpperCamelCase: Union[str, Any]    ,						):






         """simple docstring"""


         A__										=  self.speech_processor.feature_extractor(
             UpperCamelCase    ,						return_tensors="""pt"""    ,						sampling_rate=UpperCamelCase       ).input_features.to(self.device       )
         A__										=  self.speech_model.generate(UpperCamelCase    ,						max_length=48_00_00       )

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

         if isinstance(UpperCamelCase    ,						UpperCamelCase       ):
              A__										=  1
         elif isinstance(UpperCamelCase    ,						UpperCamelCase       ):
              A__										=  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
         A__										=  self.tokenizer(
             UpperCamelCase    ,						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    ,						UpperCamelCase    ,						1       )
         A__										=  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.
         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(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       ):
                   A__										=  [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:
                   A__										=  negative_prompt

              A__										=  text_input_ids.shape[-1]
              A__										=  self.tokenizer(
                  UpperCamelCase    ,						padding="""max_length"""    ,						max_length=UpperCamelCase    ,						truncation=UpperCamelCase    ,						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    ,						UpperCamelCase    ,						1       )
              A__										=  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
              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(UpperCamelCase    ,						generator=UpperCamelCase    ,						device="""cpu"""    ,						dtype=UpperCamelCase       ).to(
                       self.device       )
              else:
                   A__										=  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}"""       )
              A__										=  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
         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(UpperCamelCase       )       ):
              # 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(UpperCamelCase    ,						UpperCamelCase       )

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

         A__										=  1 / 0.18_215 * latents
         A__										=  self.vae.decode(UpperCamelCase       ).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(UpperCamelCase       )

         if not return_dict:
              return image

         return StableDiffusionPipelineOutput(images=UpperCamelCase    ,						nsfw_content_detected=UpperCamelCase       )









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         torch.manual_seed(0       )
         A__										=  UNetaDModel(
             block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,						)
         return model





    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  self.dummy_uncond_unet
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""       ).images

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""    ,						return_dict=UpperCamelCase       )[0]

         A__										=  image[0, -3:, -3:, -1]
         A__										=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2



@slow
@require_torch
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  """google/ddpm-cifar10-32"""

         A__										=  UNetaDModel.from_pretrained(UpperCamelCase       )
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )
         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						output_type="""numpy"""       ).images

         A__										=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2






