

"""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 						__UpperCamelCase     (     __snake_case    ):

  def __init__(		self     ,_A     ,_A     ,_A     ,_A     ,_A     ,_A     ,_A     ,_A     ,_A     ,):
      '''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     ,_A = "auto"   ):
      '''simple docstring'''

      if slice_size == "auto":
          _lowerCAmelCase							: str	=     self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(__lowerCamelCase   )

  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      self.enable_attention_slicing(__lowerCamelCase   )







  @torch.no_grad()
  def __call__(		self     ,_A     ,_A=1_6000     ,_A = 512     ,_A = 512     ,_A = 50     ,_A = 7.5     ,_A = None     ,_A = 1     ,_A = 0.0     ,_A = None     ,_A = None     ,_A = "pil"     ,_A = True     ,_A = None     ,_A = 1     ,**_A     ,):
      '''simple docstring'''

      _lowerCAmelCase							: List[str]	=     self.speech_processor.feature_extractor(
          __lowerCamelCase     ,return_tensors='pt'     ,sampling_rate=__lowerCamelCase   ).input_features.to(self.device   )
      _lowerCAmelCase							: int	=     self.speech_model.generate(__lowerCamelCase     ,max_length=48_0000   )

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

      if isinstance(__lowerCamelCase     ,__lowerCamelCase   ):
          _lowerCAmelCase							: List[Any]	=     1
      elif isinstance(__lowerCamelCase     ,__lowerCamelCase   ):
          _lowerCAmelCase							: List[str]	=     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							: Optional[Any]	=     self.tokenizer(
          __lowerCamelCase     ,padding='max_length'     ,max_length=self.tokenizer.model_max_length     ,return_tensors='pt'     ,)
      _lowerCAmelCase							: List[Any]	=     text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          _lowerCAmelCase							: List[Any]	=     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							: Any	=     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							: str	=     text_embeddings.shape
      _lowerCAmelCase							: int	=     text_embeddings.repeat(1     ,__lowerCamelCase     ,1   )
      _lowerCAmelCase							: Union[str, 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							: Any	=     guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
          _lowerCAmelCase							: Optional[int]	=     42
          if negative_prompt is None:
              _lowerCAmelCase							: List[Any]	=     [''] * 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							: Dict	=     [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							: Union[str, Any]	=     negative_prompt

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

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

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

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

          # predict the noise residual
          _lowerCAmelCase							: Any	=     self.unet(__lowerCamelCase     ,__lowerCamelCase     ,encoder_hidden_states=__lowerCamelCase   ).sample

          # perform guidance
          if do_classifier_free_guidance:
              _lowerCAmelCase,       _lowerCAmelCase							: Optional[int]	=     noise_pred.chunk(2   )
              _lowerCAmelCase							: Optional[int]	=     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							: Tuple	=     1 / 0.1_8_2_1_5 * latents
      _lowerCAmelCase							: str	=     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							: List[Any]	=     image.cpu().permute(0     ,2     ,3     ,1   ).float().numpy()

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

      if not return_dict:
          return image

      return StableDiffusionPipelineOutput(images=__lowerCamelCase     ,nsfw_content_detected=__lowerCamelCase   )








import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch





@require_torch
@require_optimum
@slow
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer("This is me"     , return_tensors="pt"			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       SCREAMING_SNAKE_CASE										=   model.generate(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()

       self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

           self.assertFalse(
               any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

           SCREAMING_SNAKE_CASE										=   model_reloaded.generate(**__lowerCamelCase			)
           self.assertTrue(torch.allclose(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__lowerCamelCase			):
               model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()
           model.save_pretrained(__lowerCamelCase			)