


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


__lowerCAmelCase     :			List[str]      =logging.get_logger(__name__)  # pylint: disable=invalid-name


def 	_UpperCamelCase							(				self   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		image.to(device=lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		image  # take image as image_embeddings
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		image_embeddings.unsqueeze(1				)

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

						if do_classifier_free_guidance:
												__SCREAMING_SNAKE_CASE						:					str		      =		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
												__SCREAMING_SNAKE_CASE						:					Dict		      =		torch.cat([uncond_embeddings, image_embeddings]				)

						return image_embeddings
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :str      ,      lowerCAmelCase__      :PriorTransformer      ,      lowerCAmelCase__      :CLIPTokenizer      ,      lowerCAmelCase__      :CLIPTextModelWithProjection      ,      lowerCAmelCase__      :UnCLIPScheduler      ,      lowerCAmelCase__      :Optional[dict] = None      ,      )		->      Optional[Any]:
										super().__init__()

										__SCREAMING_SNAKE_CASE						:					str		      =		{'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		decoder_pipe_kwargs.get('''torch_dtype'''      ,      lowerCAmelCase__     ) or prior.dtype

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		StableDiffusionImageVariationPipeline.from_pretrained(
										    '''lambdalabs/sd-image-variations-diffusers'''      ,      **lowerCAmelCase__     )

										# replace `_encode_image` method
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		types.MethodType(_encode_image      ,      self.decoder_pipe     )

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


				def 		__magic_name__(	self      :Optional[Any]      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,      lowerCAmelCase__      :Optional[torch.Tensor] = None      ,      )		->      Union[str, Any]:
										if text_model_output is None:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		len(lowerCAmelCase__     ) if isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ) else 1
																# get prompt text embeddings
																__SCREAMING_SNAKE_CASE						:					Any		      =		self.tokenizer(
																    lowerCAmelCase__      ,      padding='''max_length'''      ,      max_length=self.tokenizer.model_max_length      ,      return_tensors='''pt'''      ,      )
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		text_inputs.input_ids
																__SCREAMING_SNAKE_CASE						:					Any		      =		text_inputs.attention_mask.bool().to(lowerCAmelCase__     )

																if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						__SCREAMING_SNAKE_CASE						:					Dict		      =		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}'''     )
																						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		text_input_ids[:, : self.tokenizer.model_max_length]

																__SCREAMING_SNAKE_CASE						:					str		      =		self.text_encoder(text_input_ids.to(lowerCAmelCase__     )     )

																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		text_encoder_output.text_embeds
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		text_encoder_output.last_hidden_state

										else:
																__SCREAMING_SNAKE_CASE						:					int		      =		text_model_output[0].shape[0]
																__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Dict		      =		text_model_output[0], text_model_output[1]
																__SCREAMING_SNAKE_CASE						:					int		      =		text_attention_mask

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		text_embeddings.repeat_interleave(lowerCAmelCase__      ,      dim=0     )
										__SCREAMING_SNAKE_CASE						:					int		      =		text_encoder_hidden_states.repeat_interleave(lowerCAmelCase__      ,      dim=0     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		text_mask.repeat_interleave(lowerCAmelCase__      ,      dim=0     )

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

																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.tokenizer(
																    lowerCAmelCase__      ,      padding='''max_length'''      ,      max_length=self.tokenizer.model_max_length      ,      truncation=lowerCAmelCase__      ,      return_tensors='''pt'''      ,      )
																__SCREAMING_SNAKE_CASE						:					int		      =		uncond_input.attention_mask.bool().to(lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					int		      =		self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase__     )     )

																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		uncond_embeddings_text_encoder_output.text_embeds
																__SCREAMING_SNAKE_CASE						:					Any		      =		uncond_embeddings_text_encoder_output.last_hidden_state

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

																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		uncond_embeddings.shape[1]
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		uncond_embeddings.repeat(1      ,      lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					str		      =		uncond_embeddings.view(batch_size * num_images_per_prompt      ,      lowerCAmelCase__     )

																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		uncond_text_encoder_hidden_states.shape[1]
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		uncond_text_encoder_hidden_states.repeat(1      ,      lowerCAmelCase__      ,      1     )
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		uncond_text_encoder_hidden_states.view(
																    batch_size * num_images_per_prompt      ,      lowerCAmelCase__      ,      -1     )
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		uncond_text_mask.repeat_interleave(lowerCAmelCase__      ,      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
																__SCREAMING_SNAKE_CASE						:					Dict		      =		torch.cat([uncond_embeddings, text_embeddings]     )
																__SCREAMING_SNAKE_CASE						:					str		      =		torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

																__SCREAMING_SNAKE_CASE						:					str		      =		torch.cat([uncond_text_mask, text_mask]     )

										return text_embeddings, text_encoder_hidden_states, text_mask


				@property
				def 		__magic_name__(	self      :int     )		->      Optional[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(lowerCAmelCase__      ,      '''_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 		__magic_name__(	self      :int      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      int:
										if latents is None:
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		randn_tensor(lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      device=lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )
										else:
																if latents.shape != shape:
																						raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''     )
																__SCREAMING_SNAKE_CASE						:					int		      =		latents.to(lowerCAmelCase__     )

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


				def 		__magic_name__(	self      :Any      ,      lowerCAmelCase__      :Optional[Union[str, torch.device]] = None     )		->      Union[str, Any]:
										self.decoder_pipe.to(lowerCAmelCase__     )
										super().to(lowerCAmelCase__     )



				@torch.no_grad()
				def __call__(	self      :Dict      ,      lowerCAmelCase__      :Optional[Union[str, List[str]]] = None      ,      lowerCAmelCase__      :Optional[int] = None      ,      lowerCAmelCase__      :Optional[int] = None      ,      lowerCAmelCase__      :int = 1      ,      lowerCAmelCase__      :int = 25      ,      lowerCAmelCase__      :Optional[torch.Generator] = None      ,      lowerCAmelCase__      :Optional[torch.FloatTensor] = None      ,      lowerCAmelCase__      :Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,      lowerCAmelCase__      :Optional[torch.Tensor] = None      ,      lowerCAmelCase__      :float = 4.0      ,      lowerCAmelCase__      :float = 8.0      ,      lowerCAmelCase__      :int = 50      ,      lowerCAmelCase__      :Optional[int] = 1      ,      lowerCAmelCase__      :float = 0.0      ,      lowerCAmelCase__      :Optional[str] = "pil"      ,      lowerCAmelCase__      :bool = True      ,      )		->      List[Any]:
										if prompt is not None:
																if isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ):
																						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		1
																elif isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ):
																						__SCREAMING_SNAKE_CASE						:					str		      =		len(lowerCAmelCase__     )
																else:
																						raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__     )}'''     )
										else:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		text_model_output[0].shape[0]

										__SCREAMING_SNAKE_CASE						:					int		      =		self._execution_device

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

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					List[Any]		      =		self._encode_prompt(
										    lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     )

										# prior

										self.prior_scheduler.set_timesteps(lowerCAmelCase__      ,      device=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.prior_scheduler.timesteps

										__SCREAMING_SNAKE_CASE						:					str		      =		self.prior.config.embedding_dim

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.prepare_latents(
										    (batch_size, embedding_dim)      ,      text_embeddings.dtype      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      self.prior_scheduler      ,      )

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

																__SCREAMING_SNAKE_CASE						:					Any		      =		self.prior(
																    lowerCAmelCase__      ,      timestep=lowerCAmelCase__      ,      proj_embedding=lowerCAmelCase__      ,      encoder_hidden_states=lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      ).predicted_image_embedding

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

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

																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.prior_scheduler.step(
																    lowerCAmelCase__      ,      timestep=lowerCAmelCase__      ,      sample=lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      prev_timestep=lowerCAmelCase__      ,      ).prev_sample

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.prior.post_process_latents(lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		prior_latents

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.decoder_pipe(
										    image=lowerCAmelCase__      ,      height=lowerCAmelCase__      ,      width=lowerCAmelCase__      ,      num_inference_steps=lowerCAmelCase__      ,      guidance_scale=lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      output_type=lowerCAmelCase__      ,      return_dict=lowerCAmelCase__      ,      num_images_per_prompt=lowerCAmelCase__      ,      eta=lowerCAmelCase__      ,      )
										return output




from .number_theory.prime_numbers import next_prime
class 	_lowercase				:






				'''simple docstring'''



				def __init__(	self      :Tuple      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :int | None = None      ,      lowerCAmelCase__      :float | None = None      ,      )		->      None:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		size_table
										__SCREAMING_SNAKE_CASE						:					Dict		      =		[None] * self.size_table
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		0.75 if lim_charge is None else lim_charge
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		1 if charge_factor is None else charge_factor
										__SCREAMING_SNAKE_CASE						:					list		      =		[]
										__SCREAMING_SNAKE_CASE						:					dict		      =		{}


				def 		__magic_name__(	self      :List[Any]     )		->      Tuple:
										return self._keys


				def 		__magic_name__(	self      :Any     )		->      Dict:
										return sum(1 for slot in self.values if slot is not None     ) / (
										    self.size_table * self.charge_factor
										)


				def 		__magic_name__(	self      :Optional[Any]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      Optional[Any]:
										return key % self.size_table


				def 		__magic_name__(	self      :int      ,      lowerCAmelCase__      :Union[str, Any]     )		->      List[Any]:
										print(f'''step {step_ord}'''     )
										print(list(range(len(self.values     )     )     )     )
										print(self.values     )


				def 		__magic_name__(	self      :List[str]      ,      lowerCAmelCase__      :Optional[Any]     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		1
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		values
										for value in values:
																self.insert_data(lowerCAmelCase__     )
																self._step_by_step(lowerCAmelCase__     )
																i += 1


				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		data
										__SCREAMING_SNAKE_CASE						:					Any		      =		data


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :List[str]=None     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.hash_function(key + 1     )

										while self.values[new_key] is not None and self.values[new_key] != key:
																if self.values.count(lowerCAmelCase__     ) > 0:
																						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.hash_function(new_key + 1     )
																else:
																						__SCREAMING_SNAKE_CASE						:					int		      =		None
																						break

										return new_key


				def 		__magic_name__(	self      :Optional[int]     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					str		      =		[value for value in self.values if value is not None]
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		next_prime(self.size_table      ,      factor=2     )
										self._keys.clear()
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		[None] * self.size_table  # hell's pointers D: don't DRY ;/
										for value in survivor_values:
																self.insert_data(lowerCAmelCase__     )



				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :str     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.hash_function(lowerCAmelCase__     )

										if self.values[key] is None:
																self._set_value(lowerCAmelCase__      ,      lowerCAmelCase__     )

										elif self.values[key] == data:
																pass

										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		self._collision_resolution(lowerCAmelCase__      ,      lowerCAmelCase__     )
																if collision_resolution is not None:
																						self._set_value(lowerCAmelCase__      ,      lowerCAmelCase__     )
																else:
																						self.rehashing()
																						self.insert_data(lowerCAmelCase__     )
