
'''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


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



def        lowerCamelCase_  (   self ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  image.to(device=__UpperCamelCase     )
				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 ,						__UpperCamelCase ,						1     )
				A_       	=  image_embeddings.view(bs_embed * num_images_per_prompt ,						__UpperCamelCase ,						-1     )

				if do_classifier_free_guidance:
								A_       	=  torch.zeros_like(__UpperCamelCase     )

								# 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 					__lowercase		(					__lowerCamelCase       ):


					def __init__(	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__ = None       ,       )		->							List[str]:





									'''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        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__ = None       ,       a__ = None       ,       )		->							Dict:





									'''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        lowerCAmelCase_    (	self						)		->							str:





									'''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        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__						)		->							Optional[Any]:





									'''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        lowerCAmelCase_    (	self       ,       a__ = None						)		->							List[str]:





									'''simple docstring'''
									self.decoder_pipe.to(a__						)
									super().to(a__						)


					@torch.no_grad()
					def __call__(	self       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = 1       ,       a__ = 2_5       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = 4.0       ,       a__ = 8.0       ,       a__ = 5_0       ,       a__ = 1       ,       a__ = 0.0       ,       a__ = "pil"       ,       a__ = True       ,       )		->							List[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

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


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






class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	['''pixel_values''']


					def __init__(	self       ,       a__ = True       ,       a__ = None       ,       a__ = True       ,       a__ = None       ,       a__ = PILImageResampling.BICUBIC       ,       a__ = True       ,       a__ = 1 / 2_5_5       ,       a__ = True       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							None:





									'''simple docstring'''
									super().__init__(**a__						)
									A_       	=  crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
									A_       	=  get_size_dict(a__						)

									A_       	=  do_center_crop
									A_       	=  crop_size
									A_       	=  do_resize
									A_       	=  size
									A_       	=  resample
									A_       	=  do_rescale
									A_       	=  rescale_factor
									A_       	=  do_normalize
									A_       	=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									A_       	=  image_std if image_std is not None else IMAGENET_DEFAULT_STD


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  self.size if size is None else size
									A_       	=  get_size_dict(a__						)
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)

									A_	,					A_       	=  get_image_size(a__						)
									A_       	=  min(a__       ,       a__						)
									A_       	=  (size['''height'''] / crop_size['''height''']) * min_dim
									A_       	=  (size['''width'''] / crop_size['''width''']) * min_dim
									return center_crop(a__       ,       size=(cropped_height, cropped_width)       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = PIL.Image.BICUBIC       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  get_size_dict(a__						)
									if "height" not in size or "width" not in size:
													raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"						)
									return resize(
									    a__       ,       size=(size['''height'''], size['''width'''])       ,       resample=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							Union[str, Any]:





									'''simple docstring'''
									return rescale(a__       ,       scale=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									return normalize(a__       ,       mean=a__       ,       std=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = ChannelDimension.FIRST       ,       **a__       ,       )		->							PIL.Image.Image:





									'''simple docstring'''
									A_       	=  do_center_crop if do_center_crop is not None else self.do_center_crop
									A_       	=  crop_size if crop_size is not None else self.crop_size
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  do_resize if do_resize is not None else self.do_resize
									A_       	=  size if size is not None else self.size
									A_       	=  get_size_dict(a__						)
									A_       	=  resample if resample is not None else self.resample
									A_       	=  do_rescale if do_rescale is not None else self.do_rescale
									A_       	=  rescale_factor if rescale_factor is not None else self.rescale_factor
									A_       	=  do_normalize if do_normalize is not None else self.do_normalize
									A_       	=  image_mean if image_mean is not None else self.image_mean
									A_       	=  image_std if image_std is not None else self.image_std

									A_       	=  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_center_crop and crop_size is None:
													raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''						)

									if do_resize and size is None:
													raise ValueError('''Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'''						)

									# All transformations expect numpy arrays.
									A_       	=  [to_numpy_array(a__						) for image in images]

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

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

									if do_rescale:
													A_       	=  [self.rescale(image=a__       ,       scale=a__						) for image in images]

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

									A_       	=  [to_channel_dimension_format(a__       ,       a__						) for image in images]

									A_       	=  {'''pixel_values''': images}
									return BatchFeature(data=a__       ,       tensor_type=a__						)