



"""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  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       @property
       def 	lowercase__							(				self     :    Any       )					->		Optional[int]:


             '''simple docstring'''




             torch.manual_seed(0       )
             UpperCAmelCase_     					=				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     :    Optional[Any]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.dummy_uncond_unet
             UpperCAmelCase_     					=				KarrasVeScheduler()

             UpperCAmelCase_     					=				KarrasVePipeline(unet=_UpperCAmelCase						,							scheduler=_UpperCAmelCase       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(num_inference_steps=2						,							generator=_UpperCAmelCase						,							output_type="numpy"       ).images

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(num_inference_steps=2						,							generator=_UpperCAmelCase						,							output_type="numpy"						,							return_dict=_UpperCAmelCase       )[0]

             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]
             UpperCAmelCase_     					=				image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             UpperCAmelCase_     					=				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  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    List[Any]       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				"google/ncsnpp-celebahq-256"
             UpperCAmelCase_     					=				UNetaDModel.from_pretrained(_UpperCAmelCase       )
             UpperCAmelCase_     					=				KarrasVeScheduler()

             UpperCAmelCase_     					=				KarrasVePipeline(unet=_UpperCAmelCase						,							scheduler=_UpperCAmelCase       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				torch.manual_seed(0       )
             UpperCAmelCase_     					=				pipe(num_inference_steps=20						,							generator=_UpperCAmelCase						,							output_type="numpy"       ).images

             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]
             assert image.shape == (1, 256, 256, 3)
             UpperCAmelCase_     					=				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 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
A		=						'src/transformers'


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

A		=						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)`
A		=						re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')


A		=						{
    'CLIPConfigMixin',
    'DecisionTransformerConfigMixin',
    'EncoderDecoderConfigMixin',
    'RagConfigMixin',
    'SpeechEncoderDecoderConfigMixin',
    'VisionEncoderDecoderConfigMixin',
    'VisionTextDualEncoderConfigMixin',
}

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			Union[str, Any]						    = []

						for config_class in list(CONFIG_MAPPING.values() ):
												snake_case   :			List[str]						    = False

												# source code of `config_class`
												snake_case   :			Optional[int]						    = inspect.getsource(lowerCamelCase_ )
												snake_case   :			Optional[int]						    = _re_checkpoint.findall(lowerCamelCase_ )

												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')`
																		snake_case    ,			snake_case   :			Tuple						    = checkpoint

																		# verify the checkpoint name corresponds to the checkpoint link
																		snake_case   :			Any						    = f'''https://huggingface.co/{ckpt_name}'''
																		if ckpt_link == ckpt_link_from_name:
																								snake_case   :			List[str]						    = True
																								break

												snake_case   :			Any						    = config_class.__name__
												if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
																		configs_without_checkpoint.append(lowerCamelCase_ )

						if len(lowerCamelCase_ ) > 0:
												snake_case   :			List[str]						    = "\n".join(sorted(lowerCamelCase_ ) )
												raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )


if __name__ == "__main__":
			check_config_docstrings_have_checkpoints()



