



'''simple docstring'''





from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCamelCase__    :     List[str]			  =   logging.get_logger(__name__)  # pylint: disable=invalid-name


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     ['''safety_checker''', '''feature_extractor''']







 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase = True					,    )    ->    Optional[int]:
    super().__init__()
    if hasattr(scheduler.config					,    """steps_offset"""  ) and scheduler.config.steps_offset != 1:
       A_  :		int						=					(
           F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
           F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
           """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
           """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
           """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
           """ file"""
       )
       deprecate("""steps_offset!=1"""					,    """1.0.0"""					,    _lowerCamelCase					,    standard_warn=_lowerCamelCase  )
       A_  :		Any						=					dict(scheduler.config  )
       A_  :		Optional[int]						=					1
       A_  :		Tuple						=					FrozenDict(_lowerCamelCase  )

    self.register_modules(
        vae=_lowerCamelCase					,    text_encoder=_lowerCamelCase					,    tokenizer=_lowerCamelCase					,    unet=_lowerCamelCase					,    scheduler=_lowerCamelCase					,    safety_checker=_lowerCamelCase					,    feature_extractor=_lowerCamelCase					,    )
    self.register_to_config(requires_safety_checker=_lowerCamelCase  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Dict[str, Any]:
    return {k: getattr(self					,    _lowerCamelCase  ) for k in self.config.keys() if not k.startswith("""_"""  )}







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase = "auto"  )    ->    Optional[int]:
    if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       A_  :		List[str]						=					self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    self.enable_attention_slicing(_lowerCamelCase  )







 @torch.no_grad()
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase = 0.8					,    _lowerCamelCase = 50					,    _lowerCamelCase = 7.5					,    _lowerCamelCase = None					,    _lowerCamelCase = 1					,    _lowerCamelCase = 0.0					,    _lowerCamelCase = None					,    _lowerCamelCase = "pil"					,    _lowerCamelCase = True					,    _lowerCamelCase = None					,    _lowerCamelCase = 1					,    )    ->    List[Any]:
    # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
    return StableDiffusionInpaintPipelineLegacy(**self.components  )(
        prompt=_lowerCamelCase					,    image=_lowerCamelCase					,    mask_image=_lowerCamelCase					,    strength=_lowerCamelCase					,    num_inference_steps=_lowerCamelCase					,    guidance_scale=_lowerCamelCase					,    negative_prompt=_lowerCamelCase					,    num_images_per_prompt=_lowerCamelCase					,    eta=_lowerCamelCase					,    generator=_lowerCamelCase					,    output_type=_lowerCamelCase					,    return_dict=_lowerCamelCase					,    callback=_lowerCamelCase					,    )







 @torch.no_grad()
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase = 0.8					,    _lowerCamelCase = 50					,    _lowerCamelCase = 7.5					,    _lowerCamelCase = None					,    _lowerCamelCase = 1					,    _lowerCamelCase = 0.0					,    _lowerCamelCase = None					,    _lowerCamelCase = "pil"					,    _lowerCamelCase = True					,    _lowerCamelCase = None					,    _lowerCamelCase = 1					,    **_lowerCamelCase					,    )    ->    Optional[int]:
    # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
    return StableDiffusionImgaImgPipeline(**self.components  )(
        prompt=_lowerCamelCase					,    image=_lowerCamelCase					,    strength=_lowerCamelCase					,    num_inference_steps=_lowerCamelCase					,    guidance_scale=_lowerCamelCase					,    negative_prompt=_lowerCamelCase					,    num_images_per_prompt=_lowerCamelCase					,    eta=_lowerCamelCase					,    generator=_lowerCamelCase					,    output_type=_lowerCamelCase					,    return_dict=_lowerCamelCase					,    callback=_lowerCamelCase					,    callback_steps=_lowerCamelCase					,    )







 @torch.no_grad()
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _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					,    )    ->    int:
    # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
    return StableDiffusionPipeline(**self.components  )(
        prompt=_lowerCamelCase					,    height=_lowerCamelCase					,    width=_lowerCamelCase					,    num_inference_steps=_lowerCamelCase					,    guidance_scale=_lowerCamelCase					,    negative_prompt=_lowerCamelCase					,    num_images_per_prompt=_lowerCamelCase					,    eta=_lowerCamelCase					,    generator=_lowerCamelCase					,    latents=_lowerCamelCase					,    output_type=_lowerCamelCase					,    return_dict=_lowerCamelCase					,    callback=_lowerCamelCase					,    callback_steps=_lowerCamelCase					,    )





'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
