






import argparse
import os
import re


snake_case    				=			"""src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
snake_case    				=			re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
snake_case    				=			re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")


def   lowerCamelCase__			(     lowercase						,     lowercase = False				):

  """simple docstring"""






  with open(lowercase						,     "r"						,     encoding="utf-8"				) as f:
    SCREAMING_SNAKE_CASE       :      List[str]							=    f.read()

  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    content.split("\n"				)
  SCREAMING_SNAKE_CASE       :      int							=    []
  SCREAMING_SNAKE_CASE       :      Tuple							=    0
  while line_idx < len(lowercase				):
    if _re_intro_mapping.search(lines[line_idx]				) is not None:
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    len(re.search(R"^(\s*)\S"						,     lines[line_idx]				).groups()[0]				) + 8
      # Start of a new mapping!
      while not lines[line_idx].startswith(" " * indent + "("				):
        new_lines.append(lines[line_idx]				)
        line_idx += 1

      SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
      while lines[line_idx].strip() != "]":
        # Blocks either fit in one line or not
        if lines[line_idx].strip() == "(":
          SCREAMING_SNAKE_CASE       :      Tuple							=    line_idx
          while not lines[line_idx].startswith(" " * indent + ")"				):
            line_idx += 1
          blocks.append("\n".join(lines[start_idx : line_idx + 1]				)				)
        else:
          blocks.append(lines[line_idx]				)
        line_idx += 1

      # Sort blocks by their identifiers
      SCREAMING_SNAKE_CASE       :      str							=    sorted(lowercase						,     key=lambda lowercase				: _re_identifier.search(lowercase				).groups()[0]				)
      new_lines += blocks
    else:
      new_lines.append(lines[line_idx]				)
      line_idx += 1

  if overwrite:
    with open(lowercase						,     "w"						,     encoding="utf-8"				) as f:
      f.write("\n".join(lowercase				)				)
  elif "\n".join(lowercase				) != content:
    return True


def   lowerCamelCase__			(     lowercase = False				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      int							=    [os.path.join(lowercase						,     lowercase				) for f in os.listdir(lowercase				) if f.endswith(".py"				)]
  SCREAMING_SNAKE_CASE       :      str							=    [sort_auto_mapping(lowercase						,     overwrite=lowercase				) for fname in fnames]

  if not overwrite and any(lowercase				):
    SCREAMING_SNAKE_CASE       :      str							=    [f for f, d in zip(lowercase						,     lowercase				) if d]
    raise ValueError(
        F'''The following files have auto mappings that need sorting: {', '.join(lowercase				)}. Run `make style` to fix'''
        " this."				)


if __name__ == "__main__":
   snake_case    				=			argparse.ArgumentParser()
   parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
   snake_case    				=			parser.parse_args()

   sort_all_auto_mappings(not args.check_only)














import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


@slow
@require_torch_gpu
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def 				_A       (				self       :	List[Any]      ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      )
       SCREAMING_SNAKE_CASE       :      int							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_euler"      )

       SCREAMING_SNAKE_CASE       :      str							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Any							=    sd_pipe([prompt]					, generator=UpperCAmelCase_					, guidance_scale=9.0					, num_inference_steps=20					, output_type="np"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    output.images

       SCREAMING_SNAKE_CASE       :      str							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      int							=    np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]      )

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


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      str							=    StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
       SCREAMING_SNAKE_CASE       :      str							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_euler"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    sd_pipe([prompt]					, generator=UpperCAmelCase_					, guidance_scale=9.0					, num_inference_steps=20					, output_type="np"      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    output.images

       SCREAMING_SNAKE_CASE       :      Dict							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      int							=    np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5E-1






     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
       SCREAMING_SNAKE_CASE       :      Any							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_dpmpp_2m"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Any							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    sd_pipe(
           [prompt]					, generator=UpperCAmelCase_					, guidance_scale=7.5					, num_inference_steps=15					, output_type="np"					, use_karras_sigmas=UpperCAmelCase_					, )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    output.images

       SCREAMING_SNAKE_CASE       :      List[Any]							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    np.array(
           [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]      )

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






