




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):






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         lowercase			:Any							=  ort.SessionOptions()
         lowercase			:List[str]							=  False
         return options






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase			:Any							=  load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo.png" )
         lowercase			:str							=  load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
         lowercase			:Dict							=  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
         lowercase			:Any							=  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 )

         lowercase			:List[str]							=  "A red cat sitting on a park bench"

         lowercase			:Union[str, Any]							=  np.random.RandomState(0 )
         lowercase			:int							=  pipe(
             prompt=_lowerCAmelCase				,    image=_lowerCAmelCase				,    mask_image=_lowerCAmelCase				,    strength=0.75				,    guidance_scale=7.5				,    num_inference_steps=15				,    generator=_lowerCAmelCase				,    output_type="np"				,    )

         lowercase			:str							=  output.images[0]

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









from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_UpperCAmelCase		:  Tuple	=   {
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}




class       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  '''ernie_m'''
       _a           =  {"dropout": "classifier_dropout", "num_classes": "num_labels"}






       def __init__(      self:    List[Any]				,    _lowerCAmelCase:    int = 25_00_02				,    _lowerCAmelCase:    int = 7_68				,    _lowerCAmelCase:    int = 12				,    _lowerCAmelCase:    int = 12				,    _lowerCAmelCase:    int = 30_72				,    _lowerCAmelCase:    str = "gelu"				,    _lowerCAmelCase:    float = 0.1				,    _lowerCAmelCase:    float = 0.1				,    _lowerCAmelCase:    int = 5_14				,    _lowerCAmelCase:    float = 0.02				,    _lowerCAmelCase:    int = 1				,    _lowerCAmelCase:    float = 1e-0_5				,    _lowerCAmelCase:    Dict=None				,    _lowerCAmelCase:    Optional[int]=False				,    _lowerCAmelCase:    List[str]=0.0				,    **_lowerCAmelCase:    Tuple				,    ):
         super().__init__(pad_token_id=_lowerCAmelCase				,    **_lowerCAmelCase )
         lowercase			:Tuple							=  vocab_size
         lowercase			:List[str]							=  hidden_size
         lowercase			:Optional[int]							=  num_hidden_layers
         lowercase			:Optional[Any]							=  num_attention_heads
         lowercase			:Optional[Any]							=  intermediate_size
         lowercase			:Optional[Any]							=  hidden_act
         lowercase			:Any							=  hidden_dropout_prob
         lowercase			:int							=  attention_probs_dropout_prob
         lowercase			:Dict							=  max_position_embeddings
         lowercase			:Optional[Any]							=  initializer_range
         lowercase			:Any							=  layer_norm_eps
         lowercase			:Union[str, Any]							=  classifier_dropout
         lowercase			:int							=  is_decoder
         lowercase			:List[str]							=  act_dropout



