

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_SCREAMING_SNAKE_CASE          = False


@nightly
@require_torch_gpu
class     lowerCamelCase_   ( unittest.TestCase):




 def 						a					( self       ):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()




 def 						a					( self       ):
   __UpperCAmelCase  :     Dict		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   # remove text_unet
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Any		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )

   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     Union[str, Any]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=2     ,     output_type='numpy'     ,     ).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(_UpperCAmelCase       )
     __UpperCAmelCase  :     List[str]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_UpperCAmelCase       )

   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     List[str]		=    generator.manual_seed(0       )
   __UpperCAmelCase  :     Optional[int]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=2     ,     output_type='numpy'     ,     ).images

   assert np.abs(image - new_image       ).sum() < 1E-5, "Models don't have the same forward pass"




 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[Any]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Tuple		=    'cyberpunk 2077'
   __UpperCAmelCase  :     Dict		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     List[Any]		=    pipe(
       prompt=_UpperCAmelCase     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=50     ,     output_type='numpy'     ,     ).images

   __UpperCAmelCase  :     Tuple		=    image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   __UpperCAmelCase  :     Tuple		=    np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]       )

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2








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()





