



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






def 						_lowerCAmelCase    (					self :  Optional[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  Optional[int]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						image.to(device=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						image  # take image as image_embeddings
 __SCREAMING_SNAKE_CASE							:     int							=						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							:     Optional[int]							=						image_embeddings.shape
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						image_embeddings.repeat(1		,						__lowerCamelCase		,						1  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						image_embeddings.view(bs_embed * num_images_per_prompt		,						__lowerCamelCase		,						-1  )

 if do_classifier_free_guidance:
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.zeros_like(__lowerCamelCase  )

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

 return image_embeddings






class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





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

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

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						decoder_pipe_kwargs.get("torch_dtype"  ,	UpperCamelCase					) or prior.dtype

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						StableDiffusionImageVariationPipeline.from_pretrained(
          "lambdalabs/sd-image-variations-diffusers"  ,	**UpperCamelCase					)

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

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





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

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        __SCREAMING_SNAKE_CASE							:     List[str]							=						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							:     str							=						text_input_ids[:, : self.tokenizer.model_max_length]

       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.text_encoder(text_input_ids.to(UpperCamelCase					)					)

       __SCREAMING_SNAKE_CASE							:     str							=						text_encoder_output.text_embeds
       __SCREAMING_SNAKE_CASE							:     str							=						text_encoder_output.last_hidden_state

      else:
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						text_model_output[0].shape[0]
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[str]							=						text_model_output[0], text_model_output[1]
       __SCREAMING_SNAKE_CASE							:     Any							=						text_attention_mask

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

      if do_classifier_free_guidance:
       __SCREAMING_SNAKE_CASE							:     Tuple							=						[""] * batch_size

       __SCREAMING_SNAKE_CASE							:     Any							=						self.tokenizer(
           UpperCamelCase  ,	padding="max_length"  ,	max_length=self.tokenizer.model_max_length  ,	truncation=UpperCamelCase  ,	return_tensors="pt"  ,	)
       __SCREAMING_SNAKE_CASE							:     Dict							=						uncond_input.attention_mask.bool().to(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.text_encoder(uncond_input.input_ids.to(UpperCamelCase					)					)

       __SCREAMING_SNAKE_CASE							:     Any							=						uncond_embeddings_text_encoder_output.text_embeds
       __SCREAMING_SNAKE_CASE							:     Tuple							=						uncond_embeddings_text_encoder_output.last_hidden_state

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

       __SCREAMING_SNAKE_CASE							:     Tuple							=						uncond_embeddings.shape[1]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						uncond_embeddings.repeat(1  ,	UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						uncond_embeddings.view(batch_size * num_images_per_prompt  ,	UpperCamelCase					)

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

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

      return text_embeddings, text_encoder_hidden_states, text_mask





     @property
     def 						__snake_case     (							self			:				Optional[int]					)->Any:
      if self.device != torch.device("meta"					) or not hasattr(self.prior  ,	"_hf_hook"					):
       return self.device
      for module in self.prior.modules():
       if (
           hasattr(UpperCamelCase  ,	"_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[Any]  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				List[str]					)->Dict:
      if latents is None:
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						randn_tensor(UpperCamelCase  ,	generator=UpperCamelCase  ,	device=UpperCamelCase  ,	dtype=UpperCamelCase					)
      else:
       if latents.shape != shape:
        raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""					)
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						latents.to(UpperCamelCase					)

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





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






     @torch.no_grad()
     def __call__(							self			:				Any  ,	UpperCamelCase			:				Optional[Union[str, List[str]]] = None  ,	UpperCamelCase			:				Optional[int] = None  ,	UpperCamelCase			:				Optional[int] = None  ,	UpperCamelCase			:				int = 1  ,	UpperCamelCase			:				int = 2_5  ,	UpperCamelCase			:				Optional[torch.Generator] = None  ,	UpperCamelCase			:				Optional[torch.FloatTensor] = None  ,	UpperCamelCase			:				Optional[Union[CLIPTextModelOutput, Tuple]] = None  ,	UpperCamelCase			:				Optional[torch.Tensor] = None  ,	UpperCamelCase			:				float = 4.0  ,	UpperCamelCase			:				float = 8.0  ,	UpperCamelCase			:				int = 5_0  ,	UpperCamelCase			:				Optional[int] = 1  ,	UpperCamelCase			:				float = 0.0  ,	UpperCamelCase			:				Optional[str] = "pil"  ,	UpperCamelCase			:				bool = True  ,	)->int:
      if prompt is not None:
       if isinstance(UpperCamelCase  ,	UpperCamelCase					):
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						1
       elif isinstance(UpperCamelCase  ,	UpperCamelCase					):
        __SCREAMING_SNAKE_CASE							:     str							=						len(UpperCamelCase					)
       else:
        raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase					)}"""					)
      else:
       __SCREAMING_SNAKE_CASE							:     int							=						text_model_output[0].shape[0]

      __SCREAMING_SNAKE_CASE							:     Tuple							=						self._execution_device

      __SCREAMING_SNAKE_CASE							:     int							=						batch_size * num_images_per_prompt

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     int							=						self._encode_prompt(
          UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase					)

      # prior

      self.prior_scheduler.set_timesteps(UpperCamelCase  ,	device=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.prior_scheduler.timesteps

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.prior.config.embedding_dim

      __SCREAMING_SNAKE_CASE							:     int							=						self.prepare_latents(
          (batch_size, embedding_dim)  ,	text_embeddings.dtype  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	self.prior_scheduler  ,	)

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

       __SCREAMING_SNAKE_CASE							:     str							=						self.prior(
           UpperCamelCase  ,	timestep=UpperCamelCase  ,	proj_embedding=UpperCamelCase  ,	encoder_hidden_states=UpperCamelCase  ,	attention_mask=UpperCamelCase  ,	).predicted_image_embedding

       if do_classifier_free_guidance:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[Any]							=						predicted_image_embedding.chunk(2					)
        __SCREAMING_SNAKE_CASE							:     List[str]							=						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							:     Optional[int]							=						None
       else:
        __SCREAMING_SNAKE_CASE							:     Tuple							=						prior_timesteps_tensor[i + 1]

       __SCREAMING_SNAKE_CASE							:     Any							=						self.prior_scheduler.step(
           UpperCamelCase  ,	timestep=UpperCamelCase  ,	sample=UpperCamelCase  ,	generator=UpperCamelCase  ,	prev_timestep=UpperCamelCase  ,	).prev_sample

      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.prior.post_process_latents(UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prior_latents

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











from __future__ import annotations

# Define the Gravitational Constant G and the function
__magic_name__       =     6.6743E-11  # unit of G : m^3 * kg^-1 * s^-2





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       mass_a * mass_a

					if (force, mass_a, mass_a, distance).count(0				) != 1:
										raise ValueError('One and only one argument must be 0'				)
					if force < 0:
										raise ValueError('Gravitational force can not be negative'				)
					if distance < 0:
										raise ValueError('Distance can not be negative'				)
					if mass_a < 0 or mass_a < 0:
										raise ValueError('Mass can not be negative'				)
					if force == 0:
										lowercase						   =       GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
										return {"force": force}
					elif mass_a == 0:
										lowercase						   =       (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
										return {"mass_1": mass_a}
					elif mass_a == 0:
										lowercase						   =       (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
										return {"mass_2": mass_a}
					elif distance == 0:
										lowercase						   =       (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
										return {"distance": distance}
					raise ValueError('One and only one argument must be 0'				)


# Run doctest
if __name__ == "__main__":
		import doctest

		doctest.testmod()






