





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







def 			_SCREAMING_SNAKE_CASE    (       self ,						a ,						a ,						a ,						a  )				->				Optional[int]:
					__A     :						int          =    image.to(device=a  )
					__A     :						str          =    image  # take image as image_embeddings
					__A     :						List[str]          =    image_embeddings.unsqueeze(1  )

					# duplicate image embeddings for each generation per prompt, using mps friendly method
					__A   ,    __A   ,    __A     :						List[str]          =    image_embeddings.shape
					__A     :						Dict          =    image_embeddings.repeat(1 ,						a ,						1  )
					__A     :						int          =    image_embeddings.view(bs_embed * num_images_per_prompt ,						a ,						-1  )

					if do_classifier_free_guidance:
										__A     :						str          =    torch.zeros_like(a  )

										# 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     :						List[Any]          =    torch.cat([uncond_embeddings, image_embeddings]  )

					return image_embeddings





class 						_A( snake_case__       ):


				"""simple docstring"""


				def __init__(      self					, _A					, _A					, _A					, _A					, _A = None					, ):
									super().__init__()

									__A     :						Union[str, Any]          =    {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

									__A     :						Dict          =    decoder_pipe_kwargs.get('torch_dtype'					, _A					) or prior.dtype

									__A     :						Any          =    StableDiffusionImageVariationPipeline.from_pretrained(
									    'lambdalabs/sd-image-variations-diffusers'					, **_A					)

									# replace `_encode_image` method
									__A     :						int          =    types.MethodType(_encode_image					, self.decoder_pipe					)

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



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

														if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																			__A     :						int          =    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     :						Union[str, Any]          =    text_input_ids[:, : self.tokenizer.model_max_length]

														__A     :						List[str]          =    self.text_encoder(text_input_ids.to(_A					)					)

														__A     :						int          =    text_encoder_output.text_embeds
														__A     :						Any          =    text_encoder_output.last_hidden_state

									else:
														__A     :						List[str]          =    text_model_output[0].shape[0]
														__A   ,    __A     :						int          =    text_model_output[0], text_model_output[1]
														__A     :						List[str]          =    text_attention_mask

									__A     :						Union[str, Any]          =    text_embeddings.repeat_interleave(_A					, dim=0					)
									__A     :						Union[str, Any]          =    text_encoder_hidden_states.repeat_interleave(_A					, dim=0					)
									__A     :						Tuple          =    text_mask.repeat_interleave(_A					, dim=0					)

									if do_classifier_free_guidance:
														__A     :						Dict          =    [''] * batch_size

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

														__A     :						Any          =    uncond_embeddings_text_encoder_output.text_embeds
														__A     :						Dict          =    uncond_embeddings_text_encoder_output.last_hidden_state

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

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

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

														__A     :						Dict          =    torch.cat([uncond_text_mask, text_mask]					)

									return text_embeddings, text_encoder_hidden_states, text_mask



				@property
				def  UpperCAmelCase_		(      self					):
									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  UpperCAmelCase_		(      self					, _A					, _A					, _A					, _A					, _A					, _A					):
									if latents is None:
														__A     :						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}"""					)
														__A     :						Union[str, Any]          =    latents.to(_A					)

									__A     :						Tuple          =    latents * scheduler.init_noise_sigma
									return latents



				def  UpperCAmelCase_		(      self					, _A = None					):
									self.decoder_pipe.to(_A					)
									super().to(_A					)





				@torch.no_grad()
				def __call__(      self					, _A = None					, _A = None					, _A = None					, _A = 1					, _A = 25					, _A = None					, _A = None					, _A = None					, _A = None					, _A = 4.0					, _A = 8.0					, _A = 50					, _A = 1					, _A = 0.0					, _A = "pil"					, _A = True					, ):
									if prompt is not None:
														if isinstance(_A					, _A					):
																			__A     :						Optional[Any]          =    1
														elif isinstance(_A					, _A					):
																			__A     :						Union[str, Any]          =    len(_A					)
														else:
																			raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(_A					)}"""					)
									else:
														__A     :						Union[str, Any]          =    text_model_output[0].shape[0]

									__A     :						Tuple          =    self._execution_device

									__A     :						Union[str, Any]          =    batch_size * num_images_per_prompt

									__A     :						Optional[int]          =    prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

									__A   ,    __A   ,    __A     :						Any          =    self._encode_prompt(
									    _A					, _A					, _A					, _A					, _A					, _A					)

									# prior

									self.prior_scheduler.set_timesteps(_A					, device=_A					)
									__A     :						Union[str, Any]          =    self.prior_scheduler.timesteps

									__A     :						List[str]          =    self.prior.config.embedding_dim

									__A     :						Optional[Any]          =    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     :						Union[str, Any]          =    torch.cat([prior_latents] * 2					) if do_classifier_free_guidance else prior_latents

														__A     :						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:
																			__A   ,    __A     :						Any          =    predicted_image_embedding.chunk(2					)
																			__A     :						Optional[Any]          =    predicted_image_embedding_uncond + prior_guidance_scale * (
																			    predicted_image_embedding_text - predicted_image_embedding_uncond
																			)

														if i + 1 == prior_timesteps_tensor.shape[0]:
																			__A     :						List[Any]          =    None
														else:
																			__A     :						Any          =    prior_timesteps_tensor[i + 1]

														__A     :						List[str]          =    self.prior_scheduler.step(
														    _A					, timestep=_A					, sample=_A					, generator=_A					, prev_timestep=_A					, ).prev_sample

									__A     :						str          =    self.prior.post_process_latents(_A					)

									__A     :						List[str]          =    prior_latents

									__A     :						Optional[int]          =    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 .hash_table import HashTable





class 						_A( snake_case__       ):


				"""simple docstring"""


				def __init__(      self					, *_A					, **_A					):
									super().__init__(*_A					, **_A					)





				def  UpperCAmelCase_		(      self					, _A					, _A=None					):
									__A     :						Optional[int]          =    1
									__A     :						Union[str, Any]          =    self.hash_function(key + i * i					)

									while self.values[new_key] is not None and self.values[new_key] != key:
														i += 1
														__A     :						Tuple          =    (
														    self.hash_function(key + i * i					)
														    if not self.balanced_factor() >= self.lim_charge
														    else None
														)

														if new_key is None:
																			break

									return new_key




