

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


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





def    _snake_case		(self				:    Optional[Any]						,					snake_case_				:    Union[str, Any]						,					snake_case_				:    List[str]						,					snake_case_				:    str						,					snake_case_				:    Tuple     )       ->		Optional[Any]:
  __UpperCAmelCase  :     Tuple		=    image.to(device=snake_case_     )
  __UpperCAmelCase  :     Any		=    image  # take image as image_embeddings
  __UpperCAmelCase  :     int		=    image_embeddings.unsqueeze(1     )

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

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

  return image_embeddings







class     lowerCamelCase_   ( _lowerCAmelCase):




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

   __UpperCAmelCase  :     Tuple		=    {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

   __UpperCAmelCase  :     Tuple		=    decoder_pipe_kwargs.get('torch_dtype'     ,     _UpperCAmelCase       ) or prior.dtype

   __UpperCAmelCase  :     List[str]		=    StableDiffusionImageVariationPipeline.from_pretrained(
       'lambdalabs/sd-image-variations-diffusers'     ,     **_UpperCAmelCase       )

   # replace `_encode_image` method
   __UpperCAmelCase  :     Dict		=    types.MethodType(_encode_image     ,     self.decoder_pipe       )

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




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

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
       __UpperCAmelCase  :     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}"       )
       __UpperCAmelCase  :     Tuple		=    text_input_ids[:, : self.tokenizer.model_max_length]

     __UpperCAmelCase  :     Union[str, Any]		=    self.text_encoder(text_input_ids.to(_UpperCAmelCase       )       )

     __UpperCAmelCase  :     Tuple		=    text_encoder_output.text_embeds
     __UpperCAmelCase  :     List[Any]		=    text_encoder_output.last_hidden_state

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

   __UpperCAmelCase  :     Dict		=    text_embeddings.repeat_interleave(_UpperCAmelCase     ,     dim=0       )
   __UpperCAmelCase  :     Tuple		=    text_encoder_hidden_states.repeat_interleave(_UpperCAmelCase     ,     dim=0       )
   __UpperCAmelCase  :     Dict		=    text_mask.repeat_interleave(_UpperCAmelCase     ,     dim=0       )

   if do_classifier_free_guidance:
     __UpperCAmelCase  :     List[Any]		=    [''] * batch_size

     __UpperCAmelCase  :     Tuple		=    self.tokenizer(
         _UpperCAmelCase     ,     padding='max_length'     ,     max_length=self.tokenizer.model_max_length     ,     truncation=_UpperCAmelCase     ,     return_tensors='pt'     ,     )
     __UpperCAmelCase  :     Tuple		=    uncond_input.attention_mask.bool().to(_UpperCAmelCase       )
     __UpperCAmelCase  :     Tuple		=    self.text_encoder(uncond_input.input_ids.to(_UpperCAmelCase       )       )

     __UpperCAmelCase  :     str		=    uncond_embeddings_text_encoder_output.text_embeds
     __UpperCAmelCase  :     List[Any]		=    uncond_embeddings_text_encoder_output.last_hidden_state

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

     __UpperCAmelCase  :     str		=    uncond_embeddings.shape[1]
     __UpperCAmelCase  :     Dict		=    uncond_embeddings.repeat(1     ,     _UpperCAmelCase       )
     __UpperCAmelCase  :     int		=    uncond_embeddings.view(batch_size * num_images_per_prompt     ,     _UpperCAmelCase       )

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

     __UpperCAmelCase  :     Optional[int]		=    torch.cat([uncond_text_mask, text_mask]       )

   return text_embeddings, text_encoder_hidden_states, text_mask




 @property
 def 						a					( 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(_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 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   if latents is None:
     __UpperCAmelCase  :     int		=    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}"       )
     __UpperCAmelCase  :     Union[str, Any]		=    latents.to(_UpperCAmelCase       )

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




 def 						a					( self     ,     _UpperCAmelCase = None       ):
   self.decoder_pipe.to(_UpperCAmelCase       )
   super().to(_UpperCAmelCase       )




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

   __UpperCAmelCase  :     List[Any]		=    self._execution_device

   __UpperCAmelCase  :     int		=    batch_size * num_images_per_prompt

   __UpperCAmelCase  :     List[str]		=    prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

   __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     List[str]		=    self._encode_prompt(
       _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )

   # prior

   self.prior_scheduler.set_timesteps(_UpperCAmelCase     ,     device=_UpperCAmelCase       )
   __UpperCAmelCase  :     int		=    self.prior_scheduler.timesteps

   __UpperCAmelCase  :     int		=    self.prior.config.embedding_dim

   __UpperCAmelCase  :     Tuple		=    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
     __UpperCAmelCase  :     Dict		=    torch.cat([prior_latents] * 2       ) if do_classifier_free_guidance else prior_latents

     __UpperCAmelCase  :     Dict		=    self.prior(
         _UpperCAmelCase     ,     timestep=_UpperCAmelCase     ,     proj_embedding=_UpperCAmelCase     ,     encoder_hidden_states=_UpperCAmelCase     ,     attention_mask=_UpperCAmelCase     ,     ).predicted_image_embedding

     if do_classifier_free_guidance:
       __UpperCAmelCase, __UpperCAmelCase  :     Union[str, Any]		=    predicted_image_embedding.chunk(2       )
       __UpperCAmelCase  :     Tuple		=    predicted_image_embedding_uncond + prior_guidance_scale * (
           predicted_image_embedding_text - predicted_image_embedding_uncond
       )

     if i + 1 == prior_timesteps_tensor.shape[0]:
       __UpperCAmelCase  :     List[str]		=    None
     else:
       __UpperCAmelCase  :     Union[str, Any]		=    prior_timesteps_tensor[i + 1]

     __UpperCAmelCase  :     Optional[Any]		=    self.prior_scheduler.step(
         _UpperCAmelCase     ,     timestep=_UpperCAmelCase     ,     sample=_UpperCAmelCase     ,     generator=_UpperCAmelCase     ,     prev_timestep=_UpperCAmelCase     ,     ).prev_sample

   __UpperCAmelCase  :     List[str]		=    self.prior.post_process_latents(_UpperCAmelCase       )

   __UpperCAmelCase  :     Optional[Any]		=    prior_latents

   __UpperCAmelCase  :     int		=    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








def    _snake_case		(snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    int     )       ->		int:
  return len_board * len_board_column * row + column





def    _snake_case		(snake_case_				:    list[list[str]]						,					snake_case_				:    str						,					snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    set[int]						,					)       ->		bool:

  if board[row][column] != word[word_index]:
    return False

  if word_index == len(snake_case_     ) - 1:
    return True

  __UpperCAmelCase  :     List[Any]		=    [(0, 1), (0, -1), (-1, 0), (1, 0)]
  __UpperCAmelCase  :     Dict		=    len(snake_case_     )
  __UpperCAmelCase  :     str		=    len(board[0]     )
  for direction in traverts_directions:
    __UpperCAmelCase  :     Dict		=    row + direction[0]
    __UpperCAmelCase  :     Optional[int]		=    column + direction[1]
    if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
      continue

    __UpperCAmelCase  :     str		=    get_point_key(snake_case_						,					snake_case_						,					snake_case_						,					snake_case_     )
    if key in visited_points_set:
      continue

    visited_points_set.add(snake_case_     )
    if exits_word(snake_case_						,					snake_case_						,					snake_case_						,					snake_case_						,					word_index + 1						,					snake_case_     ):
      return True

    visited_points_set.remove(snake_case_     )

  return False





def    _snake_case		(snake_case_				:    list[list[str]]						,					snake_case_				:    str     )       ->		bool:
  __UpperCAmelCase  :     str		=    (
      'The board should be a non empty matrix of single chars strings.'
  )

  __UpperCAmelCase  :     Any		=    len(snake_case_     )
  if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) == 0:
    raise ValueError(snake_case_     )

  for row in board:
    if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) == 0:
      raise ValueError(snake_case_     )

    for item in row:
      if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) != 1:
        raise ValueError(snake_case_     )

    # Validate word
  if not isinstance(snake_case_						,					snake_case_     ) or len(snake_case_     ) == 0:
    raise ValueError(
        'The word parameter should be a string of length greater than 0.'     )

  __UpperCAmelCase  :     str		=    len(board[0]     )
  for i in range(snake_case_     ):
    for j in range(snake_case_     ):
      if exits_word(
          snake_case_						,					snake_case_						,					snake_case_						,					snake_case_						,					0						,					{get_point_key(snake_case_						,					snake_case_						,					snake_case_						,					snake_case_     )}     ):
        return True

  return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()





