
"""simple docstring"""
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


_a   :      Optional[int]       =				logging.get_logger(__name__)






class   _UpperCAmelCase					(       _UpperCamelCase):
    __lowercase				:			Tuple	=       ["""pixel_values"""]
    def __init__(     self    ,     snake_case_ = True    ,     snake_case_ = None    ,     snake_case_ = PIL.Image.NEAREST    ,     snake_case_ = False    ,     snake_case_ = None    ,     snake_case_ = 1 / 2_55    ,     snake_case_ = False    ,     snake_case_ = True    ,     snake_case_ = True    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = True    ,     **snake_case_    ,     ):
      super().__init__(**a__  )
      _snake_case  :    Tuple            = size if size is not None else {"height": 3_46, "width": 3_46}
      _snake_case  :    str            = get_size_dict(a__  )
      _snake_case  :    Optional[int]            = crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
      _snake_case  :    int            = get_size_dict(a__    ,     param_name="crop_size"  )

      _snake_case  :    Optional[int]            = do_resize
      _snake_case  :    Optional[int]            = size
      _snake_case  :    List[str]            = resample
      _snake_case  :    List[Any]            = do_center_crop
      _snake_case  :    Any            = crop_size
      _snake_case  :    Tuple            = do_rescale
      _snake_case  :    Dict            = rescale_factor
      _snake_case  :    Tuple            = rescale_offset
      _snake_case  :    Union[str, Any]            = do_normalize
      _snake_case  :    Tuple            = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _snake_case  :    Optional[Any]            = image_std if image_std is not None else IMAGENET_STANDARD_STD
      _snake_case  :    Dict            = include_top
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = PIL.Image.NEAREST    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      _snake_case  :    int            = get_size_dict(a__  )
      if "height" not in size or "width" not in size:
        raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'  )
      return resize(
          a__    ,     size=(size["height"], size["width"])    ,     resample=a__    ,     data_format=a__    ,     **a__  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      _snake_case  :    Tuple            = get_size_dict(a__  )
      if "height" not in size or "width" not in size:
        raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'  )
      return center_crop(a__    ,     size=(size["height"], size["width"])    ,     data_format=a__    ,     **a__  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = True    ,     snake_case_ = None    ,     **snake_case_    ,     ):







      if offset:
        _snake_case  :    List[str]            = (image - 1_27.5) * scale
        if data_format is not None:
          _snake_case  :    int            = to_channel_dimension_format(a__    ,     a__  )
        _snake_case  :    int            = rescaled_image.astype(np.floataa  )
      else:
        _snake_case  :    Tuple            = rescale(a__    ,     scale=a__    ,     data_format=a__    ,     **a__  )
      return rescaled_image
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      return normalize(a__    ,     mean=a__    ,     std=a__    ,     data_format=a__    ,     **a__  )


    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_=None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = ChannelDimension.FIRST    ,     **snake_case_    ,     ):
      _snake_case  :    Tuple            = do_resize if do_resize is not None else self.do_resize
      _snake_case  :    Optional[int]            = resample if resample is not None else self.resample
      _snake_case  :    Union[str, Any]            = do_center_crop if do_center_crop is not None else self.do_center_crop
      _snake_case  :    Optional[Any]            = do_rescale if do_rescale is not None else self.do_rescale
      _snake_case  :    Dict            = rescale_factor if rescale_factor is not None else self.rescale_factor
      _snake_case  :    List[Any]            = rescale_offset if rescale_offset is not None else self.rescale_offset
      _snake_case  :    List[str]            = do_normalize if do_normalize is not None else self.do_normalize
      _snake_case  :    int            = image_mean if image_mean is not None else self.image_mean
      _snake_case  :    Tuple            = image_std if image_std is not None else self.image_std
      _snake_case  :    List[Any]            = include_top if include_top is not None else self.include_top

      _snake_case  :    Any            = size if size is not None else self.size
      _snake_case  :    List[str]            = get_size_dict(a__  )
      _snake_case  :    Any            = crop_size if crop_size is not None else self.crop_size
      _snake_case  :    Optional[int]            = get_size_dict(a__    ,     param_name="crop_size"  )

      _snake_case  :    Tuple            = make_list_of_images(a__  )

      if not valid_images(a__  ):
        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 or resample is None:
        raise ValueError("Size and resample 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."  )

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

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

      if do_center_crop:
        _snake_case  :    Optional[int]            = [self.center_crop(image=a__    ,     size=a__  ) for image in images]

      if do_rescale:
        _snake_case  :    List[Any]            = [self.rescale(image=a__    ,     scale=a__    ,     offset=a__  ) for image in images]

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

      if include_top:
        _snake_case  :    Optional[Any]            = [self.normalize(image=a__    ,     mean=[0, 0, 0]    ,     std=a__  ) for image in images]

      _snake_case  :    Union[str, Any]            = [to_channel_dimension_format(a__    ,     a__  ) for image in images]

      _snake_case  :    Optional[int]            = {"pixel_values": images}
      return BatchFeature(data=a__    ,     tensor_type=a__  )





"""simple docstring"""


import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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






def   a__			(					self  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      image.to(device=__lowercase )
				_A       	=      image  # take image as image_embeddings
				_A       	=      image_embeddings.unsqueeze(1 )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				_A	,		_A	,		_A       	=      image_embeddings.shape
				_A       	=      image_embeddings.repeat(1  ,      __lowercase  ,      1 )
				_A       	=      image_embeddings.view(bs_embed * num_images_per_prompt  ,      __lowercase  ,      -1 )

				if do_classifier_free_guidance:
								_A       	=      torch.zeros_like(__lowercase )

								# 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, image_embeddings] )

				return image_embeddings




class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Dict     , a__   :							PriorTransformer     , a__   :							CLIPTokenizer     , a__   :							CLIPTextModelWithProjection     , a__   :							UnCLIPScheduler     , a__   :							Optional[dict] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						super().__init__()

						_A       	=      {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

						_A       	=      decoder_pipe_kwargs.get("torch_dtype"     , a__		) or prior.dtype

						_A       	=      StableDiffusionImageVariationPipeline.from_pretrained(
						    "lambdalabs/sd-image-variations-diffusers"     , **a__		)

						# replace `_encode_image` method
						_A       	=      types.MethodType(_encode_image     , self.decoder_pipe		)

						self.register_modules(
						    prior=a__     , tokenizer=a__     , text_encoder=a__     , prior_scheduler=a__     , )







		def 			a_			(       self   :							Optional[Any]     , a__   :							str     , a__   :							Optional[Any]     , a__   :							int     , a__   :							Optional[int]     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						if text_model_output is None:
										_A       	=      len(a__		) if isinstance(a__     , a__		) else 1
										# get prompt text embeddings
										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , return_tensors="pt"     , )
										_A       	=      text_inputs.input_ids
										_A       	=      text_inputs.attention_mask.bool().to(a__		)

										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(a__		)		)

										_A       	=      text_encoder_output.text_embeds
										_A       	=      text_encoder_output.last_hidden_state

						else:
										_A       	=      text_model_output[0].shape[0]
										_A	,		_A       	=      text_model_output[0], text_model_output[1]
										_A       	=      text_attention_mask

						_A       	=      text_embeddings.repeat_interleave(a__     , dim=0		)
						_A       	=      text_encoder_hidden_states.repeat_interleave(a__     , dim=0		)
						_A       	=      text_mask.repeat_interleave(a__     , dim=0		)

						if do_classifier_free_guidance:
										_A       	=      [""] * batch_size

										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , truncation=a__     , return_tensors="pt"     , )
										_A       	=      uncond_input.attention_mask.bool().to(a__		)
										_A       	=      self.text_encoder(uncond_input.input_ids.to(a__		)		)

										_A       	=      uncond_embeddings_text_encoder_output.text_embeds
										_A       	=      uncond_embeddings_text_encoder_output.last_hidden_state

										# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

										_A       	=      uncond_embeddings.shape[1]
										_A       	=      uncond_embeddings.repeat(1     , a__		)
										_A       	=      uncond_embeddings.view(batch_size * num_images_per_prompt     , a__		)

										_A       	=      uncond_text_encoder_hidden_states.shape[1]
										_A       	=      uncond_text_encoder_hidden_states.repeat(1     , a__     , 1		)
										_A       	=      uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt     , a__     , -1		)
										_A       	=      uncond_text_mask.repeat_interleave(a__     , dim=0		)

										# done duplicates

										# 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]		)
										_A       	=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

										_A       	=      torch.cat([uncond_text_mask, text_mask]		)

						return text_embeddings, text_encoder_hidden_states, text_mask







		@property
		def 			a_			(       self   :							int		)							->    Tuple:





						'''simple docstring'''







						if self.device != torch.device("meta"		) or not hasattr(self.prior     , "_hf_hook"		):
										return self.device
						for module in self.prior.modules():
										if (
										    hasattr(a__     , "_hf_hook"		)
										    and hasattr(module._hf_hook     , "execution_device"		)
										    and module._hf_hook.execution_device is not None
										):
														return torch.device(module._hf_hook.execution_device		)
						return self.device







		def 			a_			(       self   :							str     , a__   :							int     , a__   :							str     , a__   :							List[str]     , a__   :							Dict     , a__   :							Optional[Any]     , a__   :							Dict		)							->    int:





						'''simple docstring'''







						if latents is None:
										_A       	=      randn_tensor(a__     , generator=a__     , device=a__     , dtype=a__		)
						else:
										if latents.shape != shape:
														raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
										_A       	=      latents.to(a__		)

						_A       	=      latents * scheduler.init_noise_sigma
						return latents







		def 			a_			(       self   :							List[str]     , a__   :							Optional[Union[str, torch.device]] = None		)							->    Any:





						'''simple docstring'''







						self.decoder_pipe.to(a__		)
						super().to(a__		)







		@torch.no_grad()
		def __call__(       self   :							List[Any]     , a__   :							Optional[Union[str, List[str]]] = None     , a__   :							Optional[int] = None     , a__   :							Optional[int] = None     , a__   :							int = 1     , a__   :							int = 25     , a__   :							Optional[torch.Generator] = None     , a__   :							Optional[torch.FloatTensor] = None     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , a__   :							float = 4.0     , a__   :							float = 8.0     , a__   :							int = 50     , a__   :							Optional[int] = 1     , a__   :							float = 0.0     , a__   :							Optional[str] = "pil"     , a__   :							bool = True     , )							->    str:





						'''simple docstring'''







						if prompt is not None:
										if isinstance(a__     , a__		):
														_A       	=      1
										elif isinstance(a__     , a__		):
														_A       	=      len(a__		)
										else:
														raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(a__		)}"""		)
						else:
										_A       	=      text_model_output[0].shape[0]

						_A       	=      self._execution_device

						_A       	=      batch_size * num_images_per_prompt

						_A       	=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

						_A	,		_A	,		_A       	=      self._encode_prompt(
						    a__     , a__     , a__     , a__     , a__     , a__		)

						# prior

						self.prior_scheduler.set_timesteps(a__     , device=a__		)
						_A       	=      self.prior_scheduler.timesteps

						_A       	=      self.prior.config.embedding_dim

						_A       	=      self.prepare_latents(
						    (batch_size, embedding_dim)     , text_embeddings.dtype     , a__     , a__     , a__     , self.prior_scheduler     , )

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

										_A       	=      self.prior(
										    a__     , timestep=a__     , proj_embedding=a__     , encoder_hidden_states=a__     , attention_mask=a__     , ).predicted_image_embedding

										if do_classifier_free_guidance:
														_A	,		_A       	=      predicted_image_embedding.chunk(2		)
														_A       	=      predicted_image_embedding_uncond + prior_guidance_scale * (
														    predicted_image_embedding_text - predicted_image_embedding_uncond
														)

										if i + 1 == prior_timesteps_tensor.shape[0]:
														_A       	=      None
										else:
														_A       	=      prior_timesteps_tensor[i + 1]

										_A       	=      self.prior_scheduler.step(
										    a__     , timestep=a__     , sample=a__     , generator=a__     , prev_timestep=a__     , ).prev_sample

						_A       	=      self.prior.post_process_latents(a__		)

						_A       	=      prior_latents

						_A       	=      self.decoder_pipe(
						    image=a__     , height=a__     , width=a__     , num_inference_steps=a__     , guidance_scale=a__     , generator=a__     , output_type=a__     , return_dict=a__     , num_images_per_prompt=a__     , eta=a__     , )
						return output