




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort







@nightly
@require_onnxruntime
@require_torch_gpu
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


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

       """simple docstring"""






       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )

 @property
 def        a__				(       self      :						Optional[Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					ort.SessionOptions()
       lowerCamelCase_	  =					False
       return options





 def        a__				(       self      :						Dict						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo.png'						)
       lowerCamelCase_	  =					load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'						)
       lowerCamelCase_	  =					load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'						)

       # using the PNDM scheduler by default
       lowerCamelCase_	  =					OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           'CompVis/stable-diffusion-v1-4'		,							revision='onnx'		,							safety_checker=A_		,							feature_extractor=A_		,							provider=self.gpu_provider		,							sess_options=self.gpu_options		,							)
       pipe.set_progress_bar_config(disable=A_						)

       lowerCamelCase_	  =					'A red cat sitting on a park bench'

       lowerCamelCase_	  =					np.random.RandomState(0						)
       lowerCamelCase_	  =					pipe(
           prompt=A_		,							image=A_		,							mask_image=A_		,							strength=0.75		,							guidance_scale=7.5		,							num_inference_steps=15		,							generator=A_		,							output_type='np'		,							)

       lowerCamelCase_	  =					output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image						).max() < 1E-2









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


lowerCamelCase       :	str									=     logging.get_logger(__name__)

lowerCamelCase       :	List[str]									=     {
    "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		'''gpt_neox_japanese'''

 def __init__(       self      :						int		,							A_      :						Dict=32000		,							A_      :						List[Any]=2560		,							A_      :						Dict=32		,							A_      :						Union[str, Any]=32		,							A_      :						List[Any]=4		,							A_      :						List[str]="gelu"		,							A_      :						Dict=1.00		,							A_      :						int=10000		,							A_      :						Dict=2048		,							A_      :						Dict=0.02		,							A_      :						Any=1E-5		,							A_      :						Union[str, Any]=True		,							A_      :						int=31996		,							A_      :						List[str]=31999		,							A_      :						List[Any]=0.1		,							A_      :						List[Any]=0.0		,							**A_      :						Tuple		,							)     ->			Dict:

       """simple docstring"""






       super().__init__(bos_token_id=A_		,							eos_token_id=A_		,							**A_						)
       lowerCamelCase_	  =					vocab_size
       lowerCamelCase_	  =					max_position_embeddings
       lowerCamelCase_	  =					hidden_size
       lowerCamelCase_	  =					num_hidden_layers
       lowerCamelCase_	  =					num_attention_heads
       lowerCamelCase_	  =					intermediate_multiple_size
       lowerCamelCase_	  =					hidden_act
       lowerCamelCase_	  =					rotary_pct
       lowerCamelCase_	  =					rotary_emb_base
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					layer_norm_eps
       lowerCamelCase_	  =					use_cache
       lowerCamelCase_	  =					attention_dropout
       lowerCamelCase_	  =					hidden_dropout



