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


__SCREAMING_SNAKE_CASE						:    int     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    Dict     = {
    # 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		):
							_lowerCamelCase						=		'graphormer'
							_lowerCamelCase						=		['past_key_values']



							def __init__(						self      ,				lowercase_ = 1      ,				lowercase_ = 512 * 9      ,				lowercase_ = 512 * 3      ,				lowercase_ = 512      ,				lowercase_ = 512      ,				lowercase_ = 512      ,				lowercase_ = 128      ,				lowercase_ = 5      ,				lowercase_ = 1_024      ,				lowercase_ = "multi_hop"      ,				lowercase_ = 512      ,				lowercase_ = False      ,				lowercase_ = 12      ,				lowercase_ = 768      ,				lowercase_ = 768      ,				lowercase_ = 32      ,				lowercase_ = 0.1      ,				lowercase_ = 0.1      ,				lowercase_ = 0.0      ,				lowercase_ = False      ,				lowercase_ = False      ,				lowercase_ = False      ,				lowercase_ = "gelu"      ,				lowercase_ = None      ,				lowercase_ = False      ,				lowercase_ = 0      ,				lowercase_ = False      ,				lowercase_ = 0.0      ,				lowercase_ = 8      ,				lowercase_ = None      ,				lowercase_ = None      ,				lowercase_ = True      ,				lowercase_ = True      ,				lowercase_=0      ,				lowercase_=1      ,				lowercase_=2      ,				**lowercase_      ,				):
													_snake_case						:     Any				=    num_classes
													_snake_case						:     int				=    num_atoms
													_snake_case						:     List[str]				=    num_in_degree
													_snake_case						:     Dict				=    num_out_degree
													_snake_case						:     Tuple				=    num_edges
													_snake_case						:     Tuple				=    num_spatial
													_snake_case						:     List[str]				=    num_edge_dis
													_snake_case						:     Any				=    edge_type
													_snake_case						:     Optional[int]				=    multi_hop_max_dist
													_snake_case						:     str				=    spatial_pos_max
													_snake_case						:     Tuple				=    max_nodes
													_snake_case						:     List[str]				=    num_hidden_layers
													_snake_case						:     Optional[int]				=    embedding_dim
													_snake_case						:     List[Any]				=    embedding_dim
													_snake_case						:     Any				=    ffn_embedding_dim
													_snake_case						:     List[str]				=    num_attention_heads
													_snake_case						:     int				=    dropout
													_snake_case						:     Any				=    attention_dropout
													_snake_case						:     List[str]				=    layerdrop
													_snake_case						:     str				=    encoder_normalize_before
													_snake_case						:     Dict				=    pre_layernorm
													_snake_case						:     List[Any]				=    apply_graphormer_init
													_snake_case						:     int				=    activation_fn
													_snake_case						:     Union[str, Any]				=    embed_scale
													_snake_case						:     Optional[Any]				=    freeze_embeddings
													_snake_case						:     Any				=    num_trans_layers_to_freeze
													_snake_case						:     Optional[int]				=    share_input_output_embed
													_snake_case						:     str				=    traceable
													_snake_case						:     Any				=    q_noise
													_snake_case						:     List[str]				=    qn_block_size

													# These parameters are here for future extensions
													# atm, the model only supports self attention
													_snake_case						:     List[str]				=    kdim
													_snake_case						:     Tuple				=    vdim
													_snake_case						:     List[str]				=    self_attention
													_snake_case						:     Optional[Any]				=    bias

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



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Any ,			_a    :					Optional[Any] ,			_a    :					List[str]     )							->   Tuple:


										"""simple docstring"""





										super().__init__()
										self.register_modules(unet=_a ,			scheduler=_a     )




						@torch.no_grad()
						def __call__(				self    :					Any ,			_a    :					int = 1 ,			_a    :					Optional[torch.Generator] = None ,			_a    :					int = 50 ,			_a    :					Optional[str] = "pil" ,			_a    :					bool = True ,			**_a    :					Dict ,			)							->   Union[ImagePipelineOutput, Tuple]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=_a ,			)
										_SCREAMING_SNAKE_CASE	     =image.to(self.device     )

										# set step values
										self.scheduler.set_timesteps(_a     )

										for t in self.progress_bar(self.scheduler.timesteps     ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE	     =self.unet(_a ,			_a     ).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														_SCREAMING_SNAKE_CASE	     =self.scheduler.step(_a ,			_a ,			_a     ).prev_sample

										_SCREAMING_SNAKE_CASE	     =(image / 2 + 0.5).clamp(0 ,			1     )
										_SCREAMING_SNAKE_CASE	     =image.cpu().permute(0 ,			2 ,			3 ,			1     ).numpy()
										if output_type == "pil":
														_SCREAMING_SNAKE_CASE	     =self.numpy_to_pil(_a     )

										if not return_dict:
														return (image,), "This is a local test"

										return ImagePipelineOutput(images=_a     ), "This is a local test"