




"""simple docstring"""


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


A_						=				logging.get_logger(__name__)

A_						=				{
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"xglm"
 lowercase__ 					=					["past_key_values"]

 lowercase__ 					=					{
     "num_attention_heads": "attention_heads",
     "hidden_size": "d_model",
     "num_hidden_layers": "num_layers",
 }


 def __init__(				self:		Dict,      a_:		int=256_008,      a_:		List[str]=2_048,      a_:		Dict=1_024,      a_:		int=4_096,      a_:		List[Any]=24,      a_:		Any=16,      a_:		Dict="gelu",      a_:		Optional[Any]=0.1,      a_:		str=0.1,      a_:		Union[str, Any]=0.0,      a_:		List[str]=0.0,      a_:		List[Any]=0.02,      a_:		Dict=True,      a_:		int=True,      a_:		List[Any]=2,      a_:		str=1,      a_:		Optional[int]=0,      a_:		Tuple=2,      **a_:		Tuple,      ):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  vocab_size
   _snake_case							:		Optional[int]								=  max_position_embeddings
   _snake_case							:		Union[str, Any]								=  d_model
   _snake_case							:		Optional[int]								=  ffn_dim
   _snake_case							:		List[Any]								=  num_layers
   _snake_case							:		int								=  attention_heads
   _snake_case							:		int								=  activation_function
   _snake_case							:		List[str]								=  dropout
   _snake_case							:		List[Any]								=  attention_dropout
   _snake_case							:		Any								=  activation_dropout
   _snake_case							:		Union[str, Any]								=  layerdrop
   _snake_case							:		int								=  init_std
   _snake_case							:		Optional[Any]								=  scale_embedding  # scale factor will be sqrt(d_model) if True
   _snake_case							:		Union[str, Any]								=  use_cache

   super().__init__(
       pad_token_id=a_,      bos_token_id=a_,      eos_token_id=a_,      decoder_start_token_id=a_,      **a_,      )








"""simple docstring"""


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 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 @property
 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



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


 @property
 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Any								=  ort.SessionOptions()
   _snake_case							:		Union[str, Any]								=  False
   return options


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



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

   _snake_case							:		Optional[Any]								=  """A red cat sitting on a park bench"""

   _snake_case							:		Optional[int]								=  np.random.RandomState(0		)
   _snake_case							:		Any								=  pipe(
       prompt=a_,      image=a_,      mask_image=a_,      strength=0.75,      guidance_scale=7.5,      num_inference_steps=15,      generator=a_,      output_type="""np""",      )

   _snake_case							:		Dict								=  output.images[0]

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


