





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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
      __magic_name__:			int       =       "pegasus"
      __magic_name__:			Dict       =       ["past_key_values"]
      __magic_name__:			str       =       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






      def __init__(      self      :      Dict		,   _A      :      str=50265		,   _A      :      Dict=1024		,   _A      :      Optional[Any]=12		,   _A      :      Union[str, Any]=4096		,   _A      :      Any=16		,   _A      :      Optional[int]=12		,   _A      :      Any=4096		,   _A      :      Optional[Any]=16		,   _A      :      Tuple=0.0		,   _A      :      List[Any]=0.0		,   _A      :      str=True		,   _A      :      Tuple=True		,   _A      :      Optional[Any]="gelu"		,   _A      :      Optional[int]=1024		,   _A      :      Union[str, Any]=0.1		,   _A      :      Union[str, Any]=0.0		,   _A      :      Any=0.0		,   _A      :      Union[str, Any]=0.0_2		,   _A      :      int=0		,   _A      :      Optional[int]=False		,   _A      :      int=0		,   _A      :      List[Any]=1		,   _A      :      Tuple=1		,   **_A      :      str		,   )							->    str:







       """simple docstring"""


       snake_case_		:			List[str]											=	vocab_size
       snake_case_		:			List[Any]											=	max_position_embeddings
       snake_case_		:			Any											=	d_model
       snake_case_		:			Optional[Any]											=	encoder_ffn_dim
       snake_case_		:			int											=	encoder_layers
       snake_case_		:			Tuple											=	encoder_attention_heads
       snake_case_		:			Tuple											=	decoder_ffn_dim
       snake_case_		:			List[str]											=	decoder_layers
       snake_case_		:			Any											=	decoder_attention_heads
       snake_case_		:			Optional[int]											=	dropout
       snake_case_		:			str											=	attention_dropout
       snake_case_		:			Optional[Any]											=	activation_dropout
       snake_case_		:			Any											=	activation_function
       snake_case_		:			Dict											=	init_std
       snake_case_		:			Union[str, Any]											=	encoder_layerdrop
       snake_case_		:			List[str]											=	decoder_layerdrop
       snake_case_		:			int											=	use_cache
       snake_case_		:			str											=	encoder_layers
       snake_case_		:			int											=	scale_embedding  # scale factor will be sqrt(d_model) if True
       super().__init__(
           pad_token_id=_A		,   eos_token_id=_A		,   is_encoder_decoder=_A		,   decoder_start_token_id=_A		,   forced_eos_token_id=_A		,   **_A		,   )






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







       """simple docstring"""


       return self.encoder_attention_heads






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







       """simple docstring"""


       return self.d_model








import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






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







							"""simple docstring"""


							torch.manual_seed(0	)
							snake_case_		:			List[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 UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.dummy_uncond_unet
							snake_case_		:			Optional[Any]											=	ScoreSdeVeScheduler()

							snake_case_		:			Tuple											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Optional[Any]											=	torch.manual_seed(0	)
							snake_case_		:			Tuple											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			int											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A		,   return_dict=_A	)[
							    0
							]

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

							assert image.shape == (1, 32, 32, 3)
							snake_case_		:			List[Any]											=	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 SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Dict	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Dict											=	'google/ncsnpp-church-256'
							snake_case_		:			List[Any]											=	UNetaDModel.from_pretrained(_A	)

							snake_case_		:			str											=	ScoreSdeVeScheduler.from_pretrained(_A	)

							snake_case_		:			Optional[Any]											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Any											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=10		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			Union[str, Any]											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							snake_case_		:			Dict											=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]	)

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

