
'''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


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

def   UpperCamelCase_       (		self,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      int            =  image.to(device=__SCREAMING_SNAKE_CASE    )
    snake_case_	:      Tuple            =  image  # take image as image_embeddings
    snake_case_	:      int            =  image_embeddings.unsqueeze(1    )

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

    if do_classifier_free_guidance:
        snake_case_	:      Optional[int]            =  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
        snake_case_	:      List[Any]            =  torch.cat([uncond_embeddings, image_embeddings]    )

    return image_embeddings






class 				UpperCAmelCase_			(		snake_case__   ):





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

     snake_case_	:      Optional[Any]            =  {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

     snake_case_	:      Optional[Any]            =  decoder_pipe_kwargs.get("torch_dtype"	,    lowercase_) or prior.dtype

     snake_case_	:      Optional[int]            =  StableDiffusionImageVariationPipeline.from_pretrained(
         "lambdalabs/sd-image-variations-diffusers"	,    **lowercase_)

     # replace `_encode_image` method
     snake_case_	:      Optional[int]            =  types.MethodType(_encode_image	,    self.decoder_pipe)

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





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_ = None	,    lowercase_ = None	,    ):
     if text_model_output is None:
         snake_case_	:      Optional[int]            =  len(lowercase_) if isinstance(lowercase_	,    lowercase_) else 1
         # get prompt text embeddings
         snake_case_	:      Optional[int]            =  self.tokenizer(
             lowercase_	,    padding="max_length"	,    max_length=self.tokenizer.model_max_length	,    return_tensors="pt"	,    )
         snake_case_	:      Optional[Any]            =  text_inputs.input_ids
         snake_case_	:      Tuple            =  text_inputs.attention_mask.bool().to(lowercase_)

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             snake_case_	:      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}')
             snake_case_	:      str            =  text_input_ids[:, : self.tokenizer.model_max_length]

         snake_case_	:      int            =  self.text_encoder(text_input_ids.to(lowercase_))

         snake_case_	:      Optional[int]            =  text_encoder_output.text_embeds
         snake_case_	:      Union[str, Any]            =  text_encoder_output.last_hidden_state

     else:
         snake_case_	:      Union[str, Any]            =  text_model_output[0].shape[0]
         snake_case_     ,	snake_case_	:      int            =  text_model_output[0], text_model_output[1]
         snake_case_	:      str            =  text_attention_mask

     snake_case_	:      Tuple            =  text_embeddings.repeat_interleave(lowercase_	,    dim=0)
     snake_case_	:      Optional[Any]            =  text_encoder_hidden_states.repeat_interleave(lowercase_	,    dim=0)
     snake_case_	:      Any            =  text_mask.repeat_interleave(lowercase_	,    dim=0)

     if do_classifier_free_guidance:
         snake_case_	:      Optional[Any]            =  [""] * batch_size

         snake_case_	:      List[str]            =  self.tokenizer(
             lowercase_	,    padding="max_length"	,    max_length=self.tokenizer.model_max_length	,    truncation=lowercase_	,    return_tensors="pt"	,    )
         snake_case_	:      Union[str, Any]            =  uncond_input.attention_mask.bool().to(lowercase_)
         snake_case_	:      Union[str, Any]            =  self.text_encoder(uncond_input.input_ids.to(lowercase_))

         snake_case_	:      str            =  uncond_embeddings_text_encoder_output.text_embeds
         snake_case_	:      Tuple            =  uncond_embeddings_text_encoder_output.last_hidden_state

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

         snake_case_	:      Optional[int]            =  uncond_embeddings.shape[1]
         snake_case_	:      Optional[Any]            =  uncond_embeddings.repeat(1	,    lowercase_)
         snake_case_	:      List[Any]            =  uncond_embeddings.view(batch_size * num_images_per_prompt	,    lowercase_)

         snake_case_	:      Union[str, Any]            =  uncond_text_encoder_hidden_states.shape[1]
         snake_case_	:      str            =  uncond_text_encoder_hidden_states.repeat(1	,    lowercase_	,    1)
         snake_case_	:      Union[str, Any]            =  uncond_text_encoder_hidden_states.view(
             batch_size * num_images_per_prompt	,    lowercase_	,    -1)
         snake_case_	:      Optional[int]            =  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
         snake_case_	:      int            =  torch.cat([uncond_embeddings, text_embeddings])
         snake_case_	:      Union[str, Any]            =  torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])

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

     return text_embeddings, text_encoder_hidden_states, text_mask





 @property
 def     snake_case__						(      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(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     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_):
     if latents is None:
         snake_case_	:      Union[str, Any]            =  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}')
         snake_case_	:      Union[str, Any]            =  latents.to(lowercase_)

     snake_case_	:      Optional[int]            =  latents * scheduler.init_noise_sigma
     return latents





 def     snake_case__						(      self	,    lowercase_ = None):
     self.decoder_pipe.to(lowercase_)
     super().to(lowercase_)





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

     snake_case_	:      List[str]            =  self._execution_device

     snake_case_	:      Optional[int]            =  batch_size * num_images_per_prompt

     snake_case_	:      str            =  prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

     snake_case_     ,	snake_case_     ,	snake_case_	:      List[str]            =  self._encode_prompt(
         lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_)

     # prior

     self.prior_scheduler.set_timesteps(lowercase_	,    device=lowercase_)
     snake_case_	:      str            =  self.prior_scheduler.timesteps

     snake_case_	:      Optional[int]            =  self.prior.config.embedding_dim

     snake_case_	:      Tuple            =  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
         snake_case_	:      Optional[int]            =  torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents

         snake_case_	:      str            =  self.prior(
             lowercase_	,    timestep=lowercase_	,    proj_embedding=lowercase_	,    encoder_hidden_states=lowercase_	,    attention_mask=lowercase_	,    ).predicted_image_embedding

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

         snake_case_	:      Any            =  self.prior_scheduler.step(
             lowercase_	,    timestep=lowercase_	,    sample=lowercase_	,    generator=lowercase_	,    prev_timestep=lowercase_	,    ).prev_sample

     snake_case_	:      Dict            =  self.prior.post_process_latents(lowercase_)

     snake_case_	:      List[str]            =  prior_latents

     snake_case_	:      Tuple            =  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'''
import math

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Tuple            =  math.log(2,						1_0    ) * number
    snake_case_	:      Dict            =  round((log_number - int(__SCREAMING_SNAKE_CASE    )),						1_5    )

    return difference

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE = 6_7_8_9_1_0    ):
    """simple docstring"""






    snake_case_	:      Any            =  9_0
    snake_case_	:      Optional[int]            =  0

    snake_case_	:      Union[str, Any]            =  math.log(1.23,						1_0    )
    snake_case_	:      Dict            =  math.log(1.24,						1_0    )
    snake_case_	:      List[Any]            =  0

    while position < number:
        snake_case_	:      Union[str, Any]            =  log_difference(__SCREAMING_SNAKE_CASE    )

        if difference >= upper_limit:
            power_iterator += 9_3

        elif difference < lower_limit:
            power_iterator += 1_9_6

        else:
            snake_case_	:      Optional[Any]            =  power_iterator
            power_iterator += 1_9_6
            position += 1

    return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(f"""{solution() = }""")




