




'''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


_snake_case      :      Tuple   							=		logging.get_logger(__name__)  # pylint: disable=invalid-name
def 		snake_case_    (self       :  Union[str, Any] ,						UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  Tuple ,						UpperCamelCase       :  Dict ,						UpperCamelCase       :  Union[str, Any]      ):





							'''simple docstring'''


							_a	     =				image.to(device=SCREAMING_SNAKE_CASE_      )
							_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 ,						SCREAMING_SNAKE_CASE_ ,						1      )
							_a	     =				image_embeddings.view(bs_embed * num_images_per_prompt ,						SCREAMING_SNAKE_CASE_ ,						-1      )

							if do_classifier_free_guidance:
														_a	     =				torch.zeros_like(SCREAMING_SNAKE_CASE_      )

														# 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 			A    (		_a    ):







							def __init__(  self       :  Optional[Any]							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Optional[Any]							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Dict							,			lowerCAmelCase_       :  Dict = None							,			)			->							List[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'''							,			lowercase__					) or prior.dtype

														_a	     =				StableDiffusionImageVariationPipeline.from_pretrained(
														    '''lambdalabs/sd-image-variations-diffusers'''							,			**lowercase__					)

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

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







							def    __lowerCAmelCase			(  self       :  List[str]							,			lowerCAmelCase_       :  Optional[int]							,			lowerCAmelCase_       :  Optional[int]							,			lowerCAmelCase_       :  Optional[Any]							,			lowerCAmelCase_       :  List[str]							,			lowerCAmelCase_       :  Optional[int] = None							,			lowerCAmelCase_       :  Optional[int] = None							,			)			->							Tuple:


														"""simple docstring"""


														if text_model_output is None:
																					_a	     =				len(lowercase__					) if isinstance(lowercase__							,			lowercase__					) else 1
																					# get prompt text embeddings
																					_a	     =				self.tokenizer(
																					    lowercase__							,			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(lowercase__					)

																					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(lowercase__					)					)

																					_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(lowercase__							,			dim=0					)
														_a	     =				text_encoder_hidden_states.repeat_interleave(lowercase__							,			dim=0					)
														_a	     =				text_mask.repeat_interleave(lowercase__							,			dim=0					)

														if do_classifier_free_guidance:
																					_a	     =				[''''''] * batch_size

																					_a	     =				self.tokenizer(
																					    lowercase__							,			padding='''max_length'''							,			max_length=self.tokenizer.model_max_length							,			truncation=lowercase__							,			return_tensors='''pt'''							,			)
																					_a	     =				uncond_input.attention_mask.bool().to(lowercase__					)
																					_a	     =				self.text_encoder(uncond_input.input_ids.to(lowercase__					)					)

																					_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							,			lowercase__					)
																					_a	     =				uncond_embeddings.view(batch_size * num_images_per_prompt							,			lowercase__					)

																					_a	     =				uncond_text_encoder_hidden_states.shape[1]
																					_a	     =				uncond_text_encoder_hidden_states.repeat(1							,			lowercase__							,			1					)
																					_a	     =				uncond_text_encoder_hidden_states.view(
																					    batch_size * num_images_per_prompt							,			lowercase__							,			-1					)
																					_a	     =				uncond_text_mask.repeat_interleave(lowercase__							,			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       :  int					)			->							int:


														"""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(lowercase__							,			'''_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       :  str							,			lowerCAmelCase_       :  Any							,			lowerCAmelCase_       :  List[Any]							,			lowerCAmelCase_       :  List[Any]							,			lowerCAmelCase_       :  Dict							,			lowerCAmelCase_       :  List[str]							,			lowerCAmelCase_       :  Tuple					)			->							str:


														"""simple docstring"""


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

														_a	     =				latents * scheduler.init_noise_sigma
														return latents







							def    __lowerCAmelCase			(  self       :  List[str]							,			lowerCAmelCase_       :  Optional[Any] = None					)			->							Optional[Any]:


														"""simple docstring"""


														self.decoder_pipe.to(lowercase__					)
														super().to(lowercase__					)







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


														"""simple docstring"""


														if prompt is not None:
																					if isinstance(lowercase__							,			lowercase__					):
																												_a	     =				1
																					elif isinstance(lowercase__							,			lowercase__					):
																												_a	     =				len(lowercase__					)
																					else:
																												raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowercase__					)}'					)
														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(
														    lowercase__							,			lowercase__							,			lowercase__							,			lowercase__							,			lowercase__							,			lowercase__					)

														# prior

														self.prior_scheduler.set_timesteps(lowercase__							,			device=lowercase__					)
														_a	     =				self.prior_scheduler.timesteps

														_a	     =				self.prior.config.embedding_dim

														_a	     =				self.prepare_latents(
														    (batch_size, embedding_dim)							,			text_embeddings.dtype							,			lowercase__							,			lowercase__							,			lowercase__							,			self.prior_scheduler							,			)

														for i, t in enumerate(self.progress_bar(lowercase__					)					):
																					# 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(
																					    lowercase__							,			timestep=lowercase__							,			proj_embedding=lowercase__							,			encoder_hidden_states=lowercase__							,			attention_mask=lowercase__							,			).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(
																					    lowercase__							,			timestep=lowercase__							,			sample=lowercase__							,			generator=lowercase__							,			prev_timestep=lowercase__							,			).prev_sample

														_a	     =				self.prior.post_process_latents(lowercase__					)

														_a	     =				prior_latents

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



"""simple docstring"""




def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										SCREAMING_SNAKE_CASE				=	(
										    'operation can not be conducted on a object of type '
										    f'''{type(SCREAMING_SNAKE_CASE_			).__name__}'''
										)
										raise TypeError(SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	''
					for _ in range(0,     3_2			):
										bit_string += str(number % 2			)
										SCREAMING_SNAKE_CASE				=	number >> 1
					return bit_string

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if number < 0:
										raise ValueError('the value of input must be positive'			)
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('Input value must be a \'int\' type'			)
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''			)
					SCREAMING_SNAKE_CASE				=	0
					# iterator over [1 to 32],since we are dealing with 32 bit integer
					for _ in range(1,     3_3			):
										# left shift the bits by unity
										SCREAMING_SNAKE_CASE				=	result << 1
										# get the end bit
										SCREAMING_SNAKE_CASE				=	number % 2
										# right shift the bits by unity
										SCREAMING_SNAKE_CASE				=	number >> 1
										# add that bit to our ans
										SCREAMING_SNAKE_CASE				=	result | end_bit
					return get_reverse_bit_string(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
