





import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class        _SCREAMING_SNAKE_CASE						(     __lowerCamelCase   ,							__lowerCamelCase   ,							unittest.TestCase   ):







	'''simple docstring'''
	lowercase_		=      IFInpaintingPipeline
	lowercase_		=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
	lowercase_		=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	lowercase_		=      PipelineTesterMixin.required_optional_params - {'latents'}

	def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Union[str, Any]:



			'''simple docstring'''



			return self._get_dummy_components()

	def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       str=0)	->Dict:



			'''simple docstring'''



			if str(UpperCamelCase_).startswith("mps"):
					lowerCamelCase__:				List[str]        =torch.manual_seed(UpperCamelCase_)
			else:
					lowerCamelCase__:				Any        =torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)

			lowerCamelCase__:				Optional[int]        =floats_tensor((1, 3, 32, 32)	,			rng=random.Random(UpperCamelCase_)).to(UpperCamelCase_)
			lowerCamelCase__:				List[str]        =floats_tensor((1, 3, 32, 32)	,			rng=random.Random(UpperCamelCase_)).to(UpperCamelCase_)

			lowerCamelCase__:				str        ={
			    "prompt": "A painting of a squirrel eating a burger",
			    "image": image,
			    "mask_image": mask_image,
			    "generator": generator,
			    "num_inference_steps": 2,
			    "output_type": "numpy",
			}

			return inputs

	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()	,			reason="XFormers attention is only available with CUDA and `xformers` installed"	,			)
	def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Union[str, Any]:



			'''simple docstring'''



			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)

	def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->str:



			'''simple docstring'''



			self._test_save_load_optional_components()

	@unittest.skipIf(torch_device != "cuda"	,			reason="float16 requires CUDA")
	def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Optional[Any]:



			'''simple docstring'''



			super().test_save_load_floataa(expected_max_diff=1E-1)

	def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Union[str, Any]:



			'''simple docstring'''



			self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)

	def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Union[str, Any]:



			'''simple docstring'''



			self._test_save_load_local()







	def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->List[Any]:



			'''simple docstring'''



			self._test_inference_batch_single_identical(
			    expected_max_diff=1E-2	,			)








import inspect
import re

from transformers.utils import direct_transformers_import


# 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     =    direct_transformers_import(PATH_TO_TRANSFORMERS)

__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(R"\[(.+?)\]\((https://huggingface\.co/.+?)\)")


__A     =    {
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
    "MusicgenConfig",
    "RagConfig",
    "SpeechEncoderDecoderConfig",
    "TimmBackboneConfig",
    "VisionEncoderDecoderConfig",
    "VisionTextDualEncoderConfig",
    "LlamaConfig",
}





def 					lowerCAmelCase_			(	__a		)					->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =None

  # source code of `config_class`
  lowerCamelCase__:				List[Any]        =inspect.getsource(__a		)
  lowerCamelCase__:				str        =_re_checkpoint.findall(__a		)

  # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
  # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
  for ckpt_name, ckpt_link in checkpoints:
    # allow the link to end with `/`
    if ckpt_link.endswith("/"		):
      lowerCamelCase__:				str        =ckpt_link[:-1]

    # verify the checkpoint name corresponds to the checkpoint link
    lowerCamelCase__:				Optional[int]        =F"""https://huggingface.co/{ckpt_name}"""
    if ckpt_link == ckpt_link_from_name:
      lowerCamelCase__:				List[Any]        =ckpt_name
      break

  return checkpoint





def 					lowerCAmelCase_			(	)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[]

  for config_class in list(CONFIG_MAPPING.values()		):
    # Skip deprecated models
    if "models.deprecated" in config_class.__module__:
      continue
    lowerCamelCase__:				List[Any]        =get_checkpoint_from_config_class(__a		)

    lowerCamelCase__:				Tuple        =config_class.__name__
    if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
      configs_without_checkpoint.append(__a		)

  if len(__a		) > 0:
    lowerCamelCase__:				List[str]        ="\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()

