


'''simple docstring'''






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


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							{
    '''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 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Optional[Any]					=   "pegasus"
							lowercase__    :     List[str]					=   ["past_key_values"]
							lowercase__    :     Union[str, Any]					=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







							def __init__(	self      ,					lowerCamelCase_=5_02_65      ,					lowerCamelCase_=10_24      ,					lowerCamelCase_=12      ,					lowerCamelCase_=40_96      ,					lowerCamelCase_=16      ,					lowerCamelCase_=12      ,					lowerCamelCase_=40_96      ,					lowerCamelCase_=16      ,					lowerCamelCase_=0.0      ,					lowerCamelCase_=0.0      ,					lowerCamelCase_=True      ,					lowerCamelCase_=True      ,					lowerCamelCase_="gelu"      ,					lowerCamelCase_=10_24      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_=0.0      ,					lowerCamelCase_=0.0      ,					lowerCamelCase_=0.02      ,					lowerCamelCase_=0      ,					lowerCamelCase_=False      ,					lowerCamelCase_=0      ,					lowerCamelCase_=1      ,					lowerCamelCase_=1      ,					**lowerCamelCase_      ,					) ->							Optional[int]:
														lowerCAmelCase__      =			vocab_size
														lowerCAmelCase__      =			max_position_embeddings
														lowerCAmelCase__      =			d_model
														lowerCAmelCase__      =			encoder_ffn_dim
														lowerCAmelCase__      =			encoder_layers
														lowerCAmelCase__      =			encoder_attention_heads
														lowerCAmelCase__      =			decoder_ffn_dim
														lowerCAmelCase__      =			decoder_layers
														lowerCAmelCase__      =			decoder_attention_heads
														lowerCAmelCase__      =			dropout
														lowerCAmelCase__      =			attention_dropout
														lowerCAmelCase__      =			activation_dropout
														lowerCAmelCase__      =			activation_function
														lowerCAmelCase__      =			init_std
														lowerCAmelCase__      =			encoder_layerdrop
														lowerCAmelCase__      =			decoder_layerdrop
														lowerCAmelCase__      =			use_cache
														lowerCAmelCase__      =			encoder_layers
														lowerCAmelCase__      =			scale_embedding  # scale factor will be sqrt(d_model) if True
														super().__init__(
														    pad_token_id=lowerCamelCase_      ,					eos_token_id=lowerCamelCase_      ,					is_encoder_decoder=lowerCamelCase_      ,					decoder_start_token_id=lowerCamelCase_      ,					forced_eos_token_id=lowerCamelCase_      ,					**lowerCamelCase_      ,					)







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														return self.encoder_attention_heads







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														return self.d_model



'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase					       =							False




class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							pass




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





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''     )
														pipe.to(lowerCamelCase_     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

														lowerCAmelCase__      =			load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''     )
														lowerCAmelCase__      =			torch.manual_seed(0     )
														lowerCAmelCase__      =			pipe(
														    image=lowerCamelCase_      ,					generator=lowerCamelCase_      ,					guidance_scale=7.5      ,					num_inference_steps=50      ,					output_type='''numpy'''      ,					).images

														lowerCAmelCase__      =			image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__      =			np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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