


'''simple docstring'''


import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				str				     =  """config.json"""






							@register_to_config
							def __init__(   self   , __snake_case=2   , __snake_case=5   , __snake_case=(2, 5)   , __snake_case="for diffusion"   , __snake_case=[1, 3]   , ):
											pass





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				List[str]				     =  """config.json"""






							@register_to_config
							def __init__(   self   , __snake_case=2   , __snake_case=5   , __snake_case=(2, 5)   , __snake_case="for diffusion"   , __snake_case=[1, 3]   , ):
											pass





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				str				     =  """config.json"""






							@register_to_config
							def __init__(   self   , __snake_case=2   , __snake_case=5   , __snake_case=(2, 5)   , __snake_case="for diffusion"   , __snake_case=[1, 3]   , __snake_case=[1, 3]   , ):
											pass





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				List[Any]				     =  """config.json"""






							@register_to_config
							def __init__(   self   , __snake_case=2   , __snake_case=5   , __snake_case=(2, 5)   , __snake_case="for diffusion"   , __snake_case=[1, 5]   , __snake_case=[5, 4]   , ):
											pass





class    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							def UpperCAmelCase_     (   self  ):
											with self.assertRaises(__snake_case  ):
															ConfigMixin.load_config("""dummy_path"""  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Tuple							=				SampleObject()
											_SCREAMING_SNAKE_CASE			:	Tuple							=				obj.config
											assert config["a"] == 2
											assert config["b"] == 5
											assert config["c"] == (2, 5)
											assert config["d"] == "for diffusion"
											assert config["e"] == [1, 3]

											# init ignore private arguments
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				SampleObject(_name_or_path="""lalala"""  )
											_SCREAMING_SNAKE_CASE			:	int							=				obj.config
											assert config["a"] == 2
											assert config["b"] == 5
											assert config["c"] == (2, 5)
											assert config["d"] == "for diffusion"
											assert config["e"] == [1, 3]

											# can override default
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				SampleObject(c=6  )
											_SCREAMING_SNAKE_CASE			:	int							=				obj.config
											assert config["a"] == 2
											assert config["b"] == 5
											assert config["c"] == 6
											assert config["d"] == "for diffusion"
											assert config["e"] == [1, 3]

											# can use positional arguments.
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				SampleObject(1   , c=6  )
											_SCREAMING_SNAKE_CASE			:	int							=				obj.config
											assert config["a"] == 1
											assert config["b"] == 5
											assert config["c"] == 6
											assert config["d"] == "for diffusion"
											assert config["e"] == [1, 3]






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	str							=				SampleObject()
											_SCREAMING_SNAKE_CASE			:	int							=				obj.config

											assert config["a"] == 2
											assert config["b"] == 5
											assert config["c"] == (2, 5)
											assert config["d"] == "for diffusion"
											assert config["e"] == [1, 3]

											with tempfile.TemporaryDirectory() as tmpdirname:
															obj.save_config(__snake_case  )
															_SCREAMING_SNAKE_CASE			:	Dict							=				SampleObject.from_config(SampleObject.load_config(__snake_case  )  )
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				new_obj.config

											# unfreeze configs
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				dict(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	List[str]							=				dict(__snake_case  )

											assert config.pop("""c"""  ) == (2, 5)  # instantiated as tuple
											assert new_config.pop("""c"""  ) == [2, 5]  # saved & loaded as list because of json
											config.pop("""_use_default_values"""  )
											assert config == new_config






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				logging.get_logger("""diffusers.configuration_utils"""  )
											# 30 for warning
											logger.setLevel(30  )

											with CaptureLogger(__snake_case  ) as cap_logger:
															_SCREAMING_SNAKE_CASE			:	Tuple							=				DDIMScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""   , subfolder="""scheduler"""  )

											assert ddim.__class__ == DDIMScheduler
											# no warning should be thrown
											assert cap_logger.out == ""






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				logging.get_logger("""diffusers.configuration_utils"""  )
											# 30 for warning
											logger.setLevel(30  )

											with CaptureLogger(__snake_case  ) as cap_logger:
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				EulerDiscreteScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""   , subfolder="""scheduler"""  )

											assert euler.__class__ == EulerDiscreteScheduler
											# no warning should be thrown
											assert cap_logger.out == ""






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	int							=				logging.get_logger("""diffusers.configuration_utils"""  )
											# 30 for warning
											logger.setLevel(30  )

											with CaptureLogger(__snake_case  ) as cap_logger:
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				EulerAncestralDiscreteScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""   , subfolder="""scheduler"""  )

											assert euler.__class__ == EulerAncestralDiscreteScheduler
											# no warning should be thrown
											assert cap_logger.out == ""






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				logging.get_logger("""diffusers.configuration_utils"""  )
											# 30 for warning
											logger.setLevel(30  )

											with CaptureLogger(__snake_case  ) as cap_logger:
															_SCREAMING_SNAKE_CASE			:	int							=				PNDMScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""   , subfolder="""scheduler"""  )

											assert pndm.__class__ == PNDMScheduler
											# no warning should be thrown
											assert cap_logger.out == ""






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Tuple							=				logging.get_logger("""diffusers.configuration_utils"""  )
											# 30 for warning
											logger.setLevel(30  )

											with CaptureLogger(__snake_case  ) as cap_logger:
															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				DDPMScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""   , subfolder="""scheduler"""   , prediction_type="""sample"""   , beta_end=8   , )

											with CaptureLogger(__snake_case  ) as cap_logger_a:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				DDPMScheduler.from_pretrained("""google/ddpm-celebahq-256"""   , beta_start=88  )

											assert ddpm.__class__ == DDPMScheduler
											assert ddpm.config.prediction_type == "sample"
											assert ddpm.config.beta_end == 8
											assert ddpm_a.config.beta_start == 88

											# no warning should be thrown
											assert cap_logger.out == ""
											assert cap_logger_a.out == ""






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				logging.get_logger("""diffusers.configuration_utils"""  )
											# 30 for warning
											logger.setLevel(30  )

											with CaptureLogger(__snake_case  ) as cap_logger:
															_SCREAMING_SNAKE_CASE			:	List[str]							=				DPMSolverMultistepScheduler.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-torch"""   , subfolder="""scheduler"""  )

											assert dpm.__class__ == DPMSolverMultistepScheduler
											# no warning should be thrown
											assert cap_logger.out == ""






							def UpperCAmelCase_     (   self  ):
											# let's first save a config that should be in the form
											#    a=2,
											#    b=5,
											#    c=(2, 5),
											#    d="for diffusion",
											#    e=[1, 3],

											_SCREAMING_SNAKE_CASE			:	Dict							=				SampleObject()

											_SCREAMING_SNAKE_CASE			:	Dict							=				{k: v for k, v in config.config.items() if not k.startswith("""_"""  )}

											# make sure that default config has all keys in `_use_default_values`
											assert set(config_dict.keys()  ) == set(config.config._use_default_values  )

											with tempfile.TemporaryDirectory() as tmpdirname:
															config.save_config(__snake_case  )

															# now loading it with SampleObject2 should put f into `_use_default_values`
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				SampleObjecta.from_config(__snake_case  )

															assert "f" in config._use_default_values
															assert config.f == [1, 3]

											# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
											# **BECAUSE** it is part of `config._use_default_values`
											_SCREAMING_SNAKE_CASE			:	List[str]							=				SampleObjecta.from_config(config.config  )
											assert new_config.f == [5, 4]

											config.config._use_default_values.pop()
											_SCREAMING_SNAKE_CASE			:	Tuple							=				SampleObjecta.from_config(config.config  )
											assert new_config_a.f == [1, 3]

											# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
											assert new_config_a.e == [1, 3]









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		:       Tuple							       =		logging.get_logger(__name__)

UpperCAmelCase_		:       List[Any]							       =		{
    # pcqm4mv1 now deprecated
    'graphormer-base': 'https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				Union[str, Any]				     =  """graphormer"""
							A_							:				Tuple				     =  ["""past_key_values"""]






							def __init__(   self   , __snake_case = 1   , __snake_case = 512 * 9   , __snake_case = 512 * 3   , __snake_case = 512   , __snake_case = 512   , __snake_case = 512   , __snake_case = 128   , __snake_case = 5   , __snake_case = 1024   , __snake_case = "multi_hop"   , __snake_case = 512   , __snake_case = False   , __snake_case = 12   , __snake_case = 768   , __snake_case = 768   , __snake_case = 32   , __snake_case = 0.1   , __snake_case = 0.1   , __snake_case = 0.0   , __snake_case = False   , __snake_case = False   , __snake_case = False   , __snake_case = "gelu"   , __snake_case = None   , __snake_case = False   , __snake_case = 0   , __snake_case = False   , __snake_case = 0.0   , __snake_case = 8   , __snake_case = None   , __snake_case = None   , __snake_case = True   , __snake_case = True   , __snake_case=0   , __snake_case=1   , __snake_case=2   , **__snake_case   , ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_classes
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_atoms
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_in_degree
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				num_out_degree
											_SCREAMING_SNAKE_CASE			:	Any							=				num_edges
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_spatial
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_edge_dis
											_SCREAMING_SNAKE_CASE			:	int							=				edge_type
											_SCREAMING_SNAKE_CASE			:	Tuple							=				multi_hop_max_dist
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				spatial_pos_max
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				max_nodes
											_SCREAMING_SNAKE_CASE			:	str							=				num_hidden_layers
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				embedding_dim
											_SCREAMING_SNAKE_CASE			:	int							=				ffn_embedding_dim
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_attention_heads
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				dropout
											_SCREAMING_SNAKE_CASE			:	List[str]							=				attention_dropout
											_SCREAMING_SNAKE_CASE			:	int							=				layerdrop
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				encoder_normalize_before
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				pre_layernorm
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				apply_graphormer_init
											_SCREAMING_SNAKE_CASE			:	List[str]							=				activation_fn
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				embed_scale
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				freeze_embeddings
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				num_trans_layers_to_freeze
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				share_input_output_embed
											_SCREAMING_SNAKE_CASE			:	Tuple							=				traceable
											_SCREAMING_SNAKE_CASE			:	List[str]							=				q_noise
											_SCREAMING_SNAKE_CASE			:	str							=				qn_block_size

											# These parameters are here for future extensions
											# atm, the model only supports self attention
											_SCREAMING_SNAKE_CASE			:	Any							=				kdim
											_SCREAMING_SNAKE_CASE			:	int							=				vdim
											_SCREAMING_SNAKE_CASE			:	Any							=				self_attention
											_SCREAMING_SNAKE_CASE			:	Tuple							=				bias

											super().__init__(
											    pad_token_id=__snake_case   , bos_token_id=__snake_case   , eos_token_id=__snake_case   , **__snake_case   , )




