
"""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       UpperCamelCase   (				unittest.TestCase	):
     @property
     def 					__SCREAMING_SNAKE_CASE				(  self					):




       """simple docstring"""


       torch.manual_seed(0					)
       _SCREAMING_SNAKE_CASE :					Tuple				     =			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 					__SCREAMING_SNAKE_CASE				(  self					):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			self.dummy_uncond_unet
       _SCREAMING_SNAKE_CASE :					List[Any]				     =			KarrasVeScheduler()

       _SCREAMING_SNAKE_CASE :					Any				     =			KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE	,  scheduler=__SCREAMING_SNAKE_CASE					)
       pipe.to(__SCREAMING_SNAKE_CASE					)
       pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

       _SCREAMING_SNAKE_CASE :					Dict				     =			torch.manual_seed(0					)
       _SCREAMING_SNAKE_CASE :					str				     =			pipe(num_inference_steps=2	,  generator=__SCREAMING_SNAKE_CASE	,  output_type="numpy"					).images

       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			torch.manual_seed(0					)
       _SCREAMING_SNAKE_CASE :					Optional[int]				     =			pipe(num_inference_steps=2	,  generator=__SCREAMING_SNAKE_CASE	,  output_type="numpy"	,  return_dict=__SCREAMING_SNAKE_CASE					)[0]

       _SCREAMING_SNAKE_CASE :					List[Any]				     =			image[0, -3:, -3:, -1]
       _SCREAMING_SNAKE_CASE :					Tuple				     =			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       _SCREAMING_SNAKE_CASE :					List[str]				     =			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       UpperCamelCase   (				unittest.TestCase	):
     def 					__SCREAMING_SNAKE_CASE				(  self					):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE :					int				     =			"google/ncsnpp-celebahq-256"
       _SCREAMING_SNAKE_CASE :					List[str]				     =			UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE					)
       _SCREAMING_SNAKE_CASE :					str				     =			KarrasVeScheduler()

       _SCREAMING_SNAKE_CASE :					str				     =			KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE	,  scheduler=__SCREAMING_SNAKE_CASE					)
       pipe.to(__SCREAMING_SNAKE_CASE					)
       pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

       _SCREAMING_SNAKE_CASE :					List[str]				     =			torch.manual_seed(0					)
       _SCREAMING_SNAKE_CASE :					Optional[int]				     =			pipe(num_inference_steps=20	,  generator=__SCREAMING_SNAKE_CASE	,  output_type="numpy"					).images

       _SCREAMING_SNAKE_CASE :					List[str]				     =			image[0, -3:, -3:, -1]
       assert image.shape == (1, 256, 256, 3)
       _SCREAMING_SNAKE_CASE :					int				     =			np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]					)

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






'''simple docstring'''



import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase					:			Any       =	'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase					:			Optional[int]       =	importlib.util.spec_from_file_location(
    'transformers',
    os.path.join(PATH_TO_TRANSFORMERS, '__init__.py'),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
UpperCAmelCase					:			Dict       =	spec.loader.load_module()

UpperCAmelCase					:			Dict       =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
UpperCAmelCase					:			Dict       =	re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')


UpperCAmelCase					:			Any       =	{
    'CLIPConfigMixin',
    'DecisionTransformerConfigMixin',
    'EncoderDecoderConfigMixin',
    'RagConfigMixin',
    'SpeechEncoderDecoderConfigMixin',
    'VisionEncoderDecoderConfigMixin',
    'VisionTextDualEncoderConfigMixin',
}







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []

     for config_class in list(CONFIG_MAPPING.values()		):
          __SCREAMING_SNAKE_CASE      = False

          # source code of `config_class`
          __SCREAMING_SNAKE_CASE      = inspect.getsource(a__		)
          __SCREAMING_SNAKE_CASE      = _re_checkpoint.findall(a__		)

          for checkpoint in checkpoints:
               # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
               # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = checkpoint

               # verify the checkpoint name corresponds to the checkpoint link
               __SCREAMING_SNAKE_CASE      = F'https://huggingface.co/{ckpt_name}'
               if ckpt_link == ckpt_link_from_name:
                    __SCREAMING_SNAKE_CASE      = True
                    break

          __SCREAMING_SNAKE_CASE      = config_class.__name__
          if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
               configs_without_checkpoint.append(a__		)

     if len(a__		) > 0:
          __SCREAMING_SNAKE_CASE      = """\n""".join(sorted(a__		)		)
          raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}'		)


if __name__ == "__main__":
  check_config_docstrings_have_checkpoints()






