





from typing import Callable, Optional, Union

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


lowerCAmelCase_				= logging.get_logger(__name__)

lowerCAmelCase_				= {
    'microsoft/xprophetnet-large-wiki100-cased': (
        'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
    ),
}

class 					A     (_SCREAMING_SNAKE_CASE					):
      _SCREAMING_SNAKE_CASE       =				"""xlm-prophetnet"""
      _SCREAMING_SNAKE_CASE       =				["""past_key_values"""]
      _SCREAMING_SNAKE_CASE       =				{
          """num_attention_heads""": """num_encoder_attention_heads""",
      }



      def __init__(       self  ,	lowercase_ = 0.1  ,	lowercase_ = "gelu"  ,	lowercase_ = 3_0522  ,	lowercase_ = 1024  ,	lowercase_ = 4096  ,	lowercase_ = 12  ,	lowercase_ = 16  ,	lowercase_ = 4096  ,	lowercase_ = 12  ,	lowercase_ = 16  ,	lowercase_ = 0.1  ,	lowercase_ = 0.1  ,	lowercase_ = 512  ,	lowercase_ = 0.02  ,	lowercase_ = True  ,	lowercase_ = True  ,	lowercase_ = 0  ,	lowercase_ = 2  ,	lowercase_ = 32  ,	lowercase_ = 128  ,	lowercase_ = False  ,	lowercase_ = 0.0  ,	lowercase_ = True  ,	lowercase_ = 0  ,	lowercase_ = 1  ,	lowercase_ = 2  ,	**lowercase_  ,	)   -> List[str]:



             '''simple docstring'''

             _snake_case   :				List[str]  					=    vocab_size
             _snake_case   :				str  					=    hidden_size
             _snake_case   :				Optional[int]  					=    encoder_ffn_dim
             _snake_case   :				Any  					=    num_encoder_layers
             _snake_case   :				int  					=    num_encoder_attention_heads
             _snake_case   :				Dict  					=    decoder_ffn_dim
             _snake_case   :				int  					=    num_decoder_layers
             _snake_case   :				List[str]  					=    num_decoder_attention_heads
             _snake_case   :				Optional[int]  					=    max_position_embeddings
             _snake_case   :				Union[str, Any]  					=    init_std  # Normal(0, this parameter)
             _snake_case   :				List[Any]  					=    activation_function

             # parameters for xlmprophetnet
             _snake_case   :				List[str]  					=    ngram
             _snake_case   :				List[Any]  					=    num_buckets
             _snake_case   :				Tuple  					=    relative_max_distance
             _snake_case   :				int  					=    disable_ngram_loss
             _snake_case   :				List[str]  					=    eps

             # 3 Types of Dropout
             _snake_case   :				List[Any]  					=    attention_dropout
             _snake_case   :				Dict  					=    activation_dropout
             _snake_case   :				Optional[Any]  					=    dropout

             _snake_case   :				Optional[int]  					=    use_cache

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



      @property
      def  __a			(       self       )   -> int:



             '''simple docstring'''

             return self.num_encoder_layers + self.num_decoder_layers



      @num_hidden_layers.setter
      def  __a			(       self  ,	lowercase_       )   -> Any:



             '''simple docstring'''

             raise NotImplementedError(
                 '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
                 ''' `num_decoder_layers`.'''       )













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


a__      :     Optional[int]         =		False





class UpperCAmelCase_				( unittest.TestCase ):
  pass





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
       pipe.to(__snake_case )
       pipe.set_progress_bar_config(disable=__snake_case )

       A_          =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
       A_          =       torch.manual_seed(0 )
       A_          =       pipe(
           image=__snake_case ,generator=__snake_case ,guidance_scale=7.5 ,num_inference_steps=5_0 ,output_type='''numpy''' ,).images

       A_          =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       A_          =       np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )

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



