


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


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


def 			SCREAMING_SNAKE_CASE__     (					self    :   Union[str, Any]      ,						_lowercase    :   str      ,						_lowercase    :   List[str]      ,						_lowercase    :   Dict      ,						_lowercase    :   Optional[Any] )      ->       Optional[int]:




		'''simple docstring'''
		lowercase__				:			Any            =		image.to(device=_lowercase )
		lowercase__				:			List[Any]            =		image  # take image as image_embeddings
		lowercase__				:			Optional[int]            =		image_embeddings.unsqueeze(1 )

		# duplicate image embeddings for each generation per prompt, using mps friendly method
		lowercase__				:			Optional[Any]            =		image_embeddings.shape
		lowercase__				:			int            =		image_embeddings.repeat(1      ,						_lowercase      ,						1 )
		lowercase__				:			int            =		image_embeddings.view(bs_embed * num_images_per_prompt      ,						_lowercase      ,						-1 )

		if do_classifier_free_guidance:
				lowercase__				:			Optional[int]            =		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
				lowercase__				:			Tuple            =		torch.cat([uncond_embeddings, image_embeddings] )

		return image_embeddings





class 			__lowerCAmelCase				(					a__			):


	'''simple docstring'''





	def __init__(	self:	str,				lowerCamelCase_:	int,				lowerCamelCase_:	int,				lowerCamelCase_:	Optional[int],				lowerCamelCase_:	int,				lowerCamelCase_:	List[str] = None,				):
			super().__init__()

			lowercase__				:			str            =		{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

			lowercase__				:			List[str]            =		decoder_pipe_kwargs.get('torch_dtype',				__A ) or prior.dtype

			lowercase__				:			Optional[int]            =		StableDiffusionImageVariationPipeline.from_pretrained(
			    'lambdalabs/sd-image-variations-diffusers',				**__A )

			# replace `_encode_image` method
			lowercase__				:			Any            =		types.MethodType(_encode_image,				self.decoder_pipe )

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

	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	str,				lowerCamelCase_:	List[str],				lowerCamelCase_:	List[str],				lowerCamelCase_:	str,				lowerCamelCase_:	Dict = None,				lowerCamelCase_:	Dict = None,				):







			if text_model_output is None:
					lowercase__				:			int            =		len(__A ) if isinstance(__A,				__A ) else 1
					# get prompt text embeddings
					lowercase__				:			Any            =		self.tokenizer(
					    __A,				padding='max_length',				max_length=self.tokenizer.model_max_length,				return_tensors='pt',				)
					lowercase__				:			str            =		text_inputs.input_ids
					lowercase__				:			List[str]            =		text_inputs.attention_mask.bool().to(__A )

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							lowercase__				:			List[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}""" )
							lowercase__				:			Optional[Any]            =		text_input_ids[:, : self.tokenizer.model_max_length]

					lowercase__				:			Optional[int]            =		self.text_encoder(text_input_ids.to(__A ) )

					lowercase__				:			List[str]            =		text_encoder_output.text_embeds
					lowercase__				:			Tuple            =		text_encoder_output.last_hidden_state

			else:
					lowercase__				:			Optional[Any]            =		text_model_output[0].shape[0]
					lowercase__				:			Any            =		text_model_output[0], text_model_output[1]
					lowercase__				:			str            =		text_attention_mask

			lowercase__				:			Dict            =		text_embeddings.repeat_interleave(__A,				dim=0 )
			lowercase__				:			Any            =		text_encoder_hidden_states.repeat_interleave(__A,				dim=0 )
			lowercase__				:			List[str]            =		text_mask.repeat_interleave(__A,				dim=0 )

			if do_classifier_free_guidance:
					lowercase__				:			List[Any]            =		[""] * batch_size

					lowercase__				:			Optional[Any]            =		self.tokenizer(
					    __A,				padding='max_length',				max_length=self.tokenizer.model_max_length,				truncation=__A,				return_tensors='pt',				)
					lowercase__				:			Union[str, Any]            =		uncond_input.attention_mask.bool().to(__A )
					lowercase__				:			int            =		self.text_encoder(uncond_input.input_ids.to(__A ) )

					lowercase__				:			int            =		uncond_embeddings_text_encoder_output.text_embeds
					lowercase__				:			Union[str, Any]            =		uncond_embeddings_text_encoder_output.last_hidden_state

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

					lowercase__				:			Optional[int]            =		uncond_embeddings.shape[1]
					lowercase__				:			Union[str, Any]            =		uncond_embeddings.repeat(1,				__A )
					lowercase__				:			Dict            =		uncond_embeddings.view(batch_size * num_images_per_prompt,				__A )

					lowercase__				:			Optional[int]            =		uncond_text_encoder_hidden_states.shape[1]
					lowercase__				:			Dict            =		uncond_text_encoder_hidden_states.repeat(1,				__A,				1 )
					lowercase__				:			Dict            =		uncond_text_encoder_hidden_states.view(
					    batch_size * num_images_per_prompt,				__A,				-1 )
					lowercase__				:			Any            =		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
					lowercase__				:			Optional[Any]            =		torch.cat([uncond_embeddings, text_embeddings] )
					lowercase__				:			List[str]            =		torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )

					lowercase__				:			Optional[int]            =		torch.cat([uncond_text_mask, text_mask] )

			return text_embeddings, text_encoder_hidden_states, text_mask

	@property
	def       snake_case__(	self:	int ):







			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       snake_case__(	self:	Optional[int],				lowerCamelCase_:	int,				lowerCamelCase_:	int,				lowerCamelCase_:	Any,				lowerCamelCase_:	str,				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	Union[str, Any] ):







			if latents is None:
					lowercase__				:			Optional[Any]            =		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}""" )
					lowercase__				:			Dict            =		latents.to(__A )

			lowercase__				:			List[Any]            =		latents * scheduler.init_noise_sigma
			return latents

	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	Dict = None ):
			self.decoder_pipe.to(__A )
			super().to(__A )





	@torch.no_grad()
	def __call__(	self:	Optional[Any],				lowerCamelCase_:	Dict = None,				lowerCamelCase_:	List[str] = None,				lowerCamelCase_:	List[Any] = None,				lowerCamelCase_:	Optional[int] = 1,				lowerCamelCase_:	str = 25,				lowerCamelCase_:	Dict = None,				lowerCamelCase_:	Dict = None,				lowerCamelCase_:	Dict = None,				lowerCamelCase_:	Any = None,				lowerCamelCase_:	Optional[Any] = 4.0,				lowerCamelCase_:	Any = 8.0,				lowerCamelCase_:	Any = 50,				lowerCamelCase_:	Union[str, Any] = 1,				lowerCamelCase_:	Optional[int] = 0.0,				lowerCamelCase_:	Dict = "pil",				lowerCamelCase_:	List[str] = True,				):







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

			lowercase__				:			Dict            =		self._execution_device

			lowercase__				:			str            =		batch_size * num_images_per_prompt

			lowercase__				:			Tuple            =		prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

			lowercase__				:			Dict            =		self._encode_prompt(
			    __A,				__A,				__A,				__A,				__A,				__A )

			# prior

			self.prior_scheduler.set_timesteps(__A,				device=__A )
			lowercase__				:			List[str]            =		self.prior_scheduler.timesteps

			lowercase__				:			Tuple            =		self.prior.config.embedding_dim

			lowercase__				:			int            =		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
					lowercase__				:			Dict            =		torch.cat([prior_latents] * 2 ) if do_classifier_free_guidance else prior_latents

					lowercase__				:			List[Any]            =		self.prior(
					    __A,				timestep=__A,				proj_embedding=__A,				encoder_hidden_states=__A,				attention_mask=__A,				).predicted_image_embedding

					if do_classifier_free_guidance:
							lowercase__				:			List[Any]            =		predicted_image_embedding.chunk(2 )
							lowercase__				:			Union[str, Any]            =		predicted_image_embedding_uncond + prior_guidance_scale * (
							    predicted_image_embedding_text - predicted_image_embedding_uncond
							)

					if i + 1 == prior_timesteps_tensor.shape[0]:
							lowercase__				:			Dict            =		None
					else:
							lowercase__				:			Optional[int]            =		prior_timesteps_tensor[i + 1]

					lowercase__				:			int            =		self.prior_scheduler.step(
					    __A,				timestep=__A,				sample=__A,				generator=__A,				prev_timestep=__A,				).prev_sample

			lowercase__				:			List[Any]            =		self.prior.post_process_latents(__A )

			lowercase__				:			List[str]            =		prior_latents

			lowercase__				:			Any            =		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 List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase			):


	'''simple docstring'''





	def __init__(	self:	Union[str, Any] ):
			super().__init__()

			self.register_buffer('watermark_image',				torch.zeros((62, 62, 4) ) )
			lowercase__				:			int            =		None





	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	List[PIL.Image.Image],				lowerCamelCase_:	Dict=None ):
			# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

			lowercase__				:			str            =		images[0].height
			lowercase__				:			Optional[Any]            =		images[0].width

			lowercase__				:			List[Any]            =		sample_size or h

			lowercase__				:			Any            =		min(h / sample_size,				w / sample_size )
			lowercase__      ,							lowercase__				:			Optional[int]            =		(int(h / coef ), int(w / coef )) if coef < 1 else (h, w)

			lowercase__      ,							lowercase__				:			Any            =		1024**2, img_w * img_h
			lowercase__				:			Union[str, Any]            =		(Sa / Sa) ** 0.5
			lowercase__      ,							lowercase__      ,							lowercase__				:			Optional[int]            =		int(K * 62 ), img_w - int(14 * K ), img_h - int(14 * K )

			if self.watermark_image_as_pil is None:
					lowercase__				:			Tuple            =		self.watermark_image.to(torch.uinta ).cpu().numpy()
					lowercase__				:			str            =		Image.fromarray(lowerCamelCase_,				mode='RGBA' )
					lowercase__				:			Tuple            =		watermark_image

			lowercase__				:			Optional[int]            =		self.watermark_image_as_pil.resize(
			    (wm_size, wm_size),				PIL_INTERPOLATION['bicubic'],				reducing_gap=lowerCamelCase_ )

			for pil_img in images:
					pil_img.paste(lowerCamelCase_,				box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y),				mask=wm_img.split()[-1] )

			return images


