





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


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

class   A							(						__lowercase    ):



      def __init__(						self:    Any		,	_lowerCAmelCase:    WhisperForConditionalGeneration		,	_lowerCAmelCase:    WhisperProcessor		,	_lowerCAmelCase:    AutoencoderKL		,	_lowerCAmelCase:    CLIPTextModel		,	_lowerCAmelCase:    CLIPTokenizer		,	_lowerCAmelCase:    UNetaDConditionModel		,	_lowerCAmelCase:    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		,	_lowerCAmelCase:    StableDiffusionSafetyChecker		,	_lowerCAmelCase:    CLIPImageProcessor		,	)    ->     Union[str, Any]:
         '''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:    int		,	_lowerCAmelCase:    Optional[Union[str, int]] = "auto"							)    ->     Optional[int]:
         '''simple docstring'''

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



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Dict:
         '''simple docstring'''

         self.enable_attention_slicing(_lowerCAmelCase							)






      @torch.no_grad()
      def __call__(						self:    str		,	_lowerCAmelCase:    Dict		,	_lowerCAmelCase:    Optional[int]=1_6000		,	_lowerCAmelCase:    int = 512		,	_lowerCAmelCase:    int = 512		,	_lowerCAmelCase:    int = 50		,	_lowerCAmelCase:    float = 7.5		,	_lowerCAmelCase:    Optional[Union[str, List[str]]] = None		,	_lowerCAmelCase:    Optional[int] = 1		,	_lowerCAmelCase:    float = 0.0		,	_lowerCAmelCase:    Optional[torch.Generator] = None		,	_lowerCAmelCase:    Optional[torch.FloatTensor] = None		,	_lowerCAmelCase:    Optional[str] = "pil"		,	_lowerCAmelCase:    bool = True		,	_lowerCAmelCase:    Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,	_lowerCAmelCase:    int = 1		,	**_lowerCAmelCase:    int		,	)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.speech_processor.feature_extractor(
             _lowerCAmelCase		,	return_tensors="pt"		,	sampling_rate=_lowerCAmelCase							).input_features.to(self.device							)
         UpperCAmelCase_           =self.speech_model.generate(_lowerCAmelCase		,	max_length=48_0000							)

         UpperCAmelCase_           =self.speech_processor.tokenizer.batch_decode(_lowerCAmelCase		,	skip_special_tokens=_lowerCAmelCase		,	normalize=_lowerCAmelCase							)[
             0
         ]

         if isinstance(_lowerCAmelCase		,	_lowerCAmelCase							):
            UpperCAmelCase_           =1
         elif isinstance(_lowerCAmelCase		,	_lowerCAmelCase							):
            UpperCAmelCase_           =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
         UpperCAmelCase_           =self.tokenizer(
             _lowerCAmelCase		,	padding="max_length"		,	max_length=self.tokenizer.model_max_length		,	return_tensors="pt"		,	)
         UpperCAmelCase_           =text_inputs.input_ids

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

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

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

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

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

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

            # predict the noise residual
            UpperCAmelCase_           =self.unet(_lowerCAmelCase		,	_lowerCAmelCase		,	encoder_hidden_states=_lowerCAmelCase							).sample

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

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

         UpperCAmelCase_           =1 / 0.1_82_15 * latents
         UpperCAmelCase_           =self.vae.decode(_lowerCAmelCase							).sample

         UpperCAmelCase_           =(image / 2 + 0.5).clamp(0		,	1							)

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         UpperCAmelCase_           =image.cpu().permute(0		,	2		,	3		,	1							).float().numpy()

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

         if not return_dict:
            return image

         return StableDiffusionPipelineOutput(images=_lowerCAmelCase		,	nsfw_content_detected=_lowerCAmelCase							)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a	 =      {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      ["""ReformerTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      ["""ReformerTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ReformerAttention""",
     """ReformerForMaskedLM""",
     """ReformerForQuestionAnswering""",
     """ReformerForSequenceClassification""",
     """ReformerLayer""",
     """ReformerModel""",
     """ReformerModelWithLMHead""",
     """ReformerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer import ReformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer_fast import ReformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_reformer import (
      REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ReformerAttention,
      ReformerForMaskedLM,
      ReformerForQuestionAnswering,
      ReformerForSequenceClassification,
      ReformerLayer,
      ReformerModel,
      ReformerModelWithLMHead,
      ReformerPreTrainedModel,
  )

else:
 import sys

 __a	 =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



