






'''simple docstring'''


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


SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				      =							{
    # 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 							lowerCAmelCase					(							_SCREAMING_SNAKE_CASE   ):







   """simple docstring"""







   _A						=   'graphormer'
   _A						=   ['past_key_values']



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

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

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












'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Any:
    torch.manual_seed(0			)
    __a    :	Optional[Any]									=						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    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Union[str, Any]									=						'cpu'
    __a    :	Tuple									=						self.dummy_uncond_unet
    __a    :	str									=						DDPMScheduler()

    __a    :	int									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	str									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	Optional[int]									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'   ,   return_dict=_A			)[0]

    __a    :	List[str]									=						image[0, -3:, -3:, -1]
    __a    :	List[str]									=						image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	List[Any]									=						np.array(
        [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]			)

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



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Optional[Any]									=						self.dummy_uncond_unet
    __a    :	List[str]									=						DDPMScheduler(prediction_type='sample'			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	Optional[Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			)[0]

    __a    :	Dict									=						image[0, -3:, -3:, -1]
    __a    :	int									=						image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	int									=						1E-2 if torch_device != 'mps' else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   List[str]:
    __a    :	Union[str, Any]									=						'google/ddpm-cifar10-32'

    __a    :	str									=						UNetaDModel.from_pretrained(_A			)
    __a    :	List[str]									=						DDPMScheduler.from_pretrained(_A			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	int									=						torch.manual_seed(0			)
    __a    :	Optional[Any]									=						ddpm(generator=_A   ,   output_type='numpy'			).images

    __a    :	int									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	Optional[Any]									=						np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2




