


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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




class A  (   __A     ):






      def __init__(					self						:     Optional[Any]			,   lowercase_						:     AutoencoderKL			,   lowercase_						:     CLIPTextModel			,   lowercase_						:     CLIPTokenizer			,   lowercase_						:     UNetaDConditionModel			,   lowercase_						:     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,   lowercase_						:     StableDiffusionSafetyChecker			,   lowercase_						:     CLIPImageProcessor			,   )					->						Optional[int]:
             """simple docstring"""




             super().__init__()
             self.register_modules(
                 vae=lowercase_			,   text_encoder=lowercase_			,   tokenizer=lowercase_			,   unet=lowercase_			,   scheduler=lowercase_			,   safety_checker=lowercase_			,   feature_extractor=lowercase_			,   )






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     Optional[Union[str, int]] = "auto" )					->						Optional[int]:
             """simple docstring"""




             if slice_size == "auto":
                    # half the attention head size is usually a good trade-off between
                    # speed and memory
                    _lowerCamelCase       :		str		=self.unet.config.attention_head_dim // 2
             self.unet.set_attention_slice(lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						Tuple:
             """simple docstring"""




             self.enable_attention_slicing(lowercase_ )






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




             if isinstance(lowercase_			,   lowercase_ ):
                    _lowerCamelCase       :		Optional[int]		=1
             elif isinstance(lowercase_			,   lowercase_ ):
                    _lowerCamelCase       :		List[Any]		=len(lowercase_ )
             else:
                    raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowercase_ )}''' )

             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(lowercase_			,   lowercase_ ) or callback_steps <= 0)
             ):
                    raise ValueError(
                        F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                        F''' {type(lowercase_ )}.''' )

             # get prompt text embeddings
             _lowerCamelCase       :		List[str]		=self.tokenizer(
                 lowercase_			,   padding='max_length'			,   max_length=self.tokenizer.model_max_length			,   return_tensors='pt'			,   )
             _lowerCamelCase       :		str		=text_inputs.input_ids

             if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                    _lowerCamelCase       :		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       :		str		=text_input_ids[:, : self.tokenizer.model_max_length]

             if text_embeddings is None:
                    _lowerCamelCase       :		str		=self.text_encoder(text_input_ids.to(self.device ) )[0]

             # duplicate text embeddings for each generation per prompt, using mps friendly method
             _lowerCamelCase       :		List[Any]		=text_embeddings.shape
             _lowerCamelCase       :		Union[str, Any]		=text_embeddings.repeat(1			,   lowercase_			,   1 )
             _lowerCamelCase       :		Dict		=text_embeddings.view(bs_embed * num_images_per_prompt			,   lowercase_			,   -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       :		Tuple		=guidance_scale > 1.0
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
                    _lowerCamelCase       :		List[str]
                    if negative_prompt is None:
                           _lowerCamelCase       :		int		=[""]
                    elif type(lowercase_ ) is not type(lowercase_ ):
                           raise TypeError(
                               F'''`negative_prompt` should be the same type to `prompt`, but got {type(lowercase_ )} !='''
                               F''' {type(lowercase_ )}.''' )
                    elif isinstance(lowercase_			,   lowercase_ ):
                           _lowerCamelCase       :		str		=[negative_prompt]
                    elif batch_size != len(lowercase_ ):
                           raise ValueError(
                               F'''`negative_prompt`: {negative_prompt} has batch size {len(lowercase_ )}, 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       :		Any		=self.tokenizer(
                        lowercase_			,   padding='max_length'			,   max_length=lowercase_			,   truncation=lowercase_			,   return_tensors='pt'			,   )
                    _lowerCamelCase       :		Optional[int]		=self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

                    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                    _lowerCamelCase       :		Union[str, Any]		=uncond_embeddings.shape[1]
                    _lowerCamelCase       :		str		=uncond_embeddings.repeat(lowercase_			,   lowercase_			,   1 )
                    _lowerCamelCase       :		Optional[int]		=uncond_embeddings.view(batch_size * num_images_per_prompt			,   lowercase_			,   -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       :		Optional[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       :		int		=(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
             _lowerCamelCase       :		Optional[Any]		=(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
             _lowerCamelCase       :		Union[str, Any]		=text_embeddings.dtype
             if latents is None:
                    if self.device.type == "mps":
                           # randn does not exist on mps
                           _lowerCamelCase       :		Tuple		=torch.randn(
                               lowercase_			,   generator=lowercase_			,   device='cpu'			,   dtype=lowercase_ ).to(self.device )
                           _lowerCamelCase       :		Union[str, Any]		=torch.randn(lowercase_			,   generator=lowercase_			,   device='cpu'			,   dtype=lowercase_ ).to(
                               self.device )
                    else:
                           _lowerCamelCase       :		Any		=torch.randn(
                               lowercase_			,   generator=lowercase_			,   device=self.device			,   dtype=lowercase_ )
                           _lowerCamelCase       :		Tuple		=torch.randn(lowercase_			,   generator=lowercase_			,   device=self.device			,   dtype=lowercase_ )
             else:
                    if latents_reference.shape != latents_shape:
                           raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
                    _lowerCamelCase       :		Dict		=latents_reference.to(self.device )
                    _lowerCamelCase       :		str		=latents.to(self.device )

             # This is the key part of the pipeline where we
             # try to ensure that the generated images w/ the same seed
             # but different sizes actually result in similar images
             _lowerCamelCase       :		Union[str, Any]		=(latents_shape[3] - latents_shape_reference[3]) // 2
             _lowerCamelCase       :		str		=(latents_shape[2] - latents_shape_reference[2]) // 2
             _lowerCamelCase       :		List[Any]		=latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
             _lowerCamelCase       :		int		=latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
             _lowerCamelCase       :		Any		=0 if dx < 0 else dx
             _lowerCamelCase       :		Optional[Any]		=0 if dy < 0 else dy
             _lowerCamelCase       :		List[Any]		=max(-dx			,   0 )
             _lowerCamelCase       :		str		=max(-dy			,   0 )
             # import pdb
             # pdb.set_trace()
             _lowerCamelCase       :		Any		=latents_reference[:, :, dy : dy + h, dx : dx + w]

             # set timesteps
             self.scheduler.set_timesteps(lowercase_ )

             # Some schedulers like PNDM have timesteps as arrays
             # It's more optimized to move all timesteps to correct device beforehand
             _lowerCamelCase       :		int		=self.scheduler.timesteps.to(self.device )

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

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

                    # predict the noise residual
                    _lowerCamelCase       :		Optional[int]		=self.unet(lowercase_			,   lowercase_			,   encoder_hidden_states=lowercase_ ).sample

                    # perform guidance
                    if do_classifier_free_guidance:
                           _lowerCamelCase       :		List[str]		=noise_pred.chunk(2 )
                           _lowerCamelCase       :		Dict		=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                    # compute the previous noisy sample x_t -> x_t-1
                    _lowerCamelCase       :		Optional[int]		=self.scheduler.step(lowercase_			,   lowercase_			,   lowercase_			,   **lowercase_ ).prev_sample

                    # call the callback, if provided
                    if callback is not None and i % callback_steps == 0:
                           callback(lowercase_			,   lowercase_			,   lowercase_ )

             _lowerCamelCase       :		int		=1 / 0.18215 * latents
             _lowerCamelCase       :		Dict		=self.vae.decode(lowercase_ ).sample

             _lowerCamelCase       :		Tuple		=(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       :		int		=image.cpu().permute(0			,   2			,   3			,   1 ).float().numpy()

             if self.safety_checker is not None:
                    _lowerCamelCase       :		List[str]		=self.feature_extractor(self.numpy_to_pil(lowercase_ )			,   return_tensors='pt' ).to(
                        self.device )
                    _lowerCamelCase       :		int		=self.safety_checker(
                        images=lowercase_			,   clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
             else:
                    _lowerCamelCase       :		List[str]		=None

             if output_type == "pil":
                    _lowerCamelCase       :		List[str]		=self.numpy_to_pil(lowercase_ )

             if not return_dict:
                    return (image, has_nsfw_concept)

             return StableDiffusionPipelineOutput(images=lowercase_			,   nsfw_content_detected=lowercase_ )



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase  							=						logging.get_logger(__name__)


if is_vision_available():
      import PIL



class   snake_case_       (		__A       ):
   __A	:		str         =				["pixel_values"]






   def __init__(      self				:     int	,						lowercase_				:     bool = True	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     PILImageResampling = PILImageResampling.BICUBIC	,						lowercase_				:     bool = True	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     bool = True	,						lowercase_				:     Union[int, float] = 1 / 2_55	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     bool = True	,						**lowercase_				:     Union[str, Any]	,						)					->						None:
       super().__init__(**lowercase_  )
       lowercase__ :   Tuple    					=				size if size is not None else {"shortest_edge": 2_24}
       lowercase__ :   Tuple    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       lowercase__ :   List[str]    					=				crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
       lowercase__ :   Tuple    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_	,						param_name="crop_size"  )

       lowercase__ :   Dict    					=				do_resize
       lowercase__ :   List[Any]    					=				size
       lowercase__ :   int    					=				resample
       lowercase__ :   Union[str, Any]    					=				do_center_crop
       lowercase__ :   Optional[int]    					=				crop_size
       lowercase__ :   List[str]    					=				do_rescale
       lowercase__ :   int    					=				rescale_factor
       lowercase__ :   List[Any]    					=				do_normalize
       lowercase__ :   Union[str, Any]    					=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowercase__ :   str    					=				image_std if image_std is not None else OPENAI_CLIP_STD
       lowercase__ :   Dict    					=				do_convert_rgb






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     PILImageResampling = PILImageResampling.BICUBIC	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Union[str, Any]	,						)					->						np.ndarray:
       lowercase__ :   str    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
       lowercase__ :   Dict    					=				get_resize_output_image_size(lowercase_	,						size=size["shortest_edge"]	,						default_to_square=lowercase_  )
       return resize(lowercase_	,						size=lowercase_	,						resample=lowercase_	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     int	,						)					->						np.ndarray:
       lowercase__ :   Optional[Any]    					=				get_size_dict(lowercase_  )
       if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''  )
       return center_crop(lowercase_	,						size=(size["height"], size["width"])	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     np.ndarray	,						lowercase_				:     Union[int, float]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Optional[Any]	,						)					->						Any:
       return rescale(lowercase_	,						scale=lowercase_	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     str	,						lowercase_				:     np.ndarray	,						lowercase_				:     Union[float, List[float]]	,						lowercase_				:     Union[float, List[float]]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     str	,						)					->						np.ndarray:
       return normalize(lowercase_	,						mean=lowercase_	,						std=lowercase_	,						data_format=lowercase_	,						**lowercase_  )







   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     ImageInput	,						lowercase_				:     bool = None	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     PILImageResampling = None	,						lowercase_				:     bool = None	,						lowercase_				:     int = None	,						lowercase_				:     bool = None	,						lowercase_				:     float = None	,						lowercase_				:     bool = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     bool = None	,						lowercase_				:     Optional[Union[str, TensorType]] = None	,						lowercase_				:     Optional[ChannelDimension] = ChannelDimension.FIRST	,						**lowercase_				:     Union[str, Any]	,						)					->						PIL.Image.Image:
       lowercase__ :   int    					=				do_resize if do_resize is not None else self.do_resize
       lowercase__ :   Dict    					=				size if size is not None else self.size
       lowercase__ :   List[Any]    					=				get_size_dict(lowercase_	,						param_name="size"	,						default_to_square=lowercase_  )
       lowercase__ :   Dict    					=				resample if resample is not None else self.resample
       lowercase__ :   int    					=				do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase__ :   Dict    					=				crop_size if crop_size is not None else self.crop_size
       lowercase__ :   List[str]    					=				get_size_dict(lowercase_	,						param_name="crop_size"	,						default_to_square=lowercase_  )
       lowercase__ :   int    					=				do_rescale if do_rescale is not None else self.do_rescale
       lowercase__ :   Dict    					=				rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase__ :   Union[str, Any]    					=				do_normalize if do_normalize is not None else self.do_normalize
       lowercase__ :   int    					=				image_mean if image_mean is not None else self.image_mean
       lowercase__ :   List[str]    					=				image_std if image_std is not None else self.image_std
       lowercase__ :   Union[str, Any]    					=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowercase__ :   Union[str, Any]    					=				make_list_of_images(lowercase_  )

       if not valid_images(lowercase_  ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."  )

       if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."  )

       if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."  )

       if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."  )

       if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."  )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
           lowercase__ :   Dict    					=				[convert_to_rgb(lowercase_  ) for image in images]

       # All transformations expect numpy arrays.
       lowercase__ :   Optional[Any]    					=				[to_numpy_array(lowercase_  ) for image in images]

       if do_resize:
           lowercase__ :   List[Any]    					=				[self.resize(image=lowercase_	,						size=lowercase_	,						resample=lowercase_  ) for image in images]

       if do_center_crop:
           lowercase__ :   int    					=				[self.center_crop(image=lowercase_	,						size=lowercase_  ) for image in images]

       if do_rescale:
           lowercase__ :   str    					=				[self.rescale(image=lowercase_	,						scale=lowercase_  ) for image in images]

       if do_normalize:
           lowercase__ :   Optional[int]    					=				[self.normalize(image=lowercase_	,						mean=lowercase_	,						std=lowercase_  ) for image in images]

       lowercase__ :   Optional[Any]    					=				[to_channel_dimension_format(lowercase_	,						lowercase_  ) for image in images]

       lowercase__ :   List[str]    					=				{"pixel_values": images}
       return BatchFeature(data=lowercase_	,						tensor_type=lowercase_  )



