



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 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 @property
 def     snake_case_      (    self					:					List[str]      )   ->	Dict:
    return (
        "CUDAExecutionProvider",
        {
            "gpu_mem_limit": "15000000000",  # 15GB
            "arena_extend_strategy": "kSameAsRequested",
        },
    )

 @property
 def     snake_case_      (    self					:					Dict      )   ->	Any:
    _A      =       ort.SessionOptions()
    _A      =       False
    return options

 def     snake_case_      (    self					:					str      )   ->	List[Any]:
    _A      =       load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/in_paint/overture-creations-5sI6fQgYIuo.png'''      )
    _A      =       load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''      )
    _A      =       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
    _A      =       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
        '''CompVis/stable-diffusion-v1-4'''			,						revision='''onnx'''			,						safety_checker=__lowerCAmelCase			,						feature_extractor=__lowerCAmelCase			,						provider=self.gpu_provider			,						sess_options=self.gpu_options			,						)
    pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

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

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

    _A      =       output.images[0]

    assert image.shape == (5_12, 5_12, 3)
    assert np.abs(expected_image - image      ).max() < 1E-2








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


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: int =  "openai-gpt"
 a__					: Dict =  {
     "max_position_embeddings": "n_positions",
     "hidden_size": "n_embd",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					int=4_04_78			,						__lowerCAmelCase					:					Tuple=5_12			,						__lowerCAmelCase					:					str=7_68			,						__lowerCAmelCase					:					List[Any]=12			,						__lowerCAmelCase					:					Any=12			,						__lowerCAmelCase					:					Dict="gelu"			,						__lowerCAmelCase					:					Union[str, Any]=0.1			,						__lowerCAmelCase					:					Optional[int]=0.1			,						__lowerCAmelCase					:					Any=0.1			,						__lowerCAmelCase					:					List[str]=1E-5			,						__lowerCAmelCase					:					Optional[int]=0.02			,						__lowerCAmelCase					:					Optional[Any]="cls_index"			,						__lowerCAmelCase					:					str=True			,						__lowerCAmelCase					:					int=None			,						__lowerCAmelCase					:					Optional[int]=True			,						__lowerCAmelCase					:					Optional[Any]=0.1			,						**__lowerCAmelCase					:					Tuple			,						)   ->	Optional[Any]:
    _A      =       vocab_size
    _A      =       n_positions
    _A      =       n_embd
    _A      =       n_layer
    _A      =       n_head
    _A      =       afn
    _A      =       resid_pdrop
    _A      =       embd_pdrop
    _A      =       attn_pdrop
    _A      =       layer_norm_epsilon
    _A      =       initializer_range
    _A      =       summary_type
    _A      =       summary_use_proj
    _A      =       summary_activation
    _A      =       summary_first_dropout
    _A      =       summary_proj_to_labels
    super().__init__(**__lowerCAmelCase      )



