
"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class       __magic_name__							(							unittest.TestCase			):
       @property
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         torch.manual_seed(0							)
         _lowercase:      Optional[int]   			=  UNetaDModel(
             block_out_channels=(32, 64)			,					layers_per_block=2			,					sample_size=32			,					in_channels=3			,					out_channels=3			,					down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')			,					up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')			,					)
         return model



       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      str   			=  self.dummy_uncond_unet
         _lowercase:      Any   			=  KarrasVeScheduler()

         _lowercase:      Dict   			=  KarrasVePipeline(unet=A_			,					scheduler=A_							)
         pipe.to(A_							)
         pipe.set_progress_bar_config(disable=A_							)

         _lowercase:      Optional[Any]   			=  torch.manual_seed(0							)
         _lowercase:      int   			=  pipe(num_inference_steps=2			,					generator=A_			,					output_type='''numpy'''							).images

         _lowercase:      Any   			=  torch.manual_seed(0							)
         _lowercase:      int   			=  pipe(num_inference_steps=2			,					generator=A_			,					output_type='''numpy'''			,					return_dict=A_							)[0]

         _lowercase:      Optional[int]   			=  image[0, -3:, -3:, -1]
         _lowercase:      Union[str, Any]   			=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         _lowercase:      Any   			=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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


@slow
@require_torch
class       __magic_name__							(							unittest.TestCase			):
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  '''google/ncsnpp-celebahq-256'''
         _lowercase:      Optional[Any]   			=  UNetaDModel.from_pretrained(A_							)
         _lowercase:      Any   			=  KarrasVeScheduler()

         _lowercase:      List[str]   			=  KarrasVePipeline(unet=A_			,					scheduler=A_							)
         pipe.to(A_							)
         pipe.set_progress_bar_config(disable=A_							)

         _lowercase:      Dict   			=  torch.manual_seed(0							)
         _lowercase:      Optional[int]   			=  pipe(num_inference_steps=20			,					generator=A_			,					output_type='''numpy'''							).images

         _lowercase:      Any   			=  image[0, -3:, -3:, -1]
         assert image.shape == (1, 256, 256, 3)
         _lowercase:      Any   			=  np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]							)

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


"""simple docstring"""




import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
A__ :       Optional[int]				= [
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
  A__ :       List[Any]				= argparse.ArgumentParser()
  parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
  A__ :       Optional[int]				= parser.parse_args()
  if args.check_lib:
    A__ :       Union[str, Any]				= importlib.import_module('transformers')
    A__ :       Optional[Any]				= Path(transformers_module.__file__).parent
  else:
    A__ :       str				= Path.cwd() / 'build/lib/transformers'
  if not test_custom_files_are_present(transformers_path):
    raise ValueError('The built release does not contain the custom files. Fix this before going further!')
