



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


__snake_case     :str			       =logging.get_logger(__name__)

__snake_case     :int			       ={
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Union[str, Any]								=     'pegasus'
				A_   :     int								=     ['past_key_values']
				A_   :     Dict								=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


				def __init__(							self      :  Dict						, __UpperCamelCase      :  List[Any]=50_265						, __UpperCamelCase      :  Any=1_024						, __UpperCamelCase      :  List[str]=12						, __UpperCamelCase      :  Optional[Any]=4_096						, __UpperCamelCase      :  Optional[int]=16						, __UpperCamelCase      :  List[Any]=12						, __UpperCamelCase      :  Union[str, Any]=4_096						, __UpperCamelCase      :  int=16						, __UpperCamelCase      :  Optional[Any]=0.0						, __UpperCamelCase      :  Tuple=0.0						, __UpperCamelCase      :  Optional[int]=True						, __UpperCamelCase      :  Tuple=True						, __UpperCamelCase      :  Optional[int]="gelu"						, __UpperCamelCase      :  Optional[Any]=1_024						, __UpperCamelCase      :  str=0.1						, __UpperCamelCase      :  str=0.0						, __UpperCamelCase      :  Dict=0.0						, __UpperCamelCase      :  Tuple=0.0_2						, __UpperCamelCase      :  int=0						, __UpperCamelCase      :  int=False						, __UpperCamelCase      :  Optional[int]=0						, __UpperCamelCase      :  Union[str, Any]=1						, __UpperCamelCase      :  List[str]=1						, **__UpperCamelCase      :  Optional[int]						, )		->     Dict:
											A      						=    vocab_size
											A      						=    max_position_embeddings
											A      						=    d_model
											A      						=    encoder_ffn_dim
											A      						=    encoder_layers
											A      						=    encoder_attention_heads
											A      						=    decoder_ffn_dim
											A      						=    decoder_layers
											A      						=    decoder_attention_heads
											A      						=    dropout
											A      						=    attention_dropout
											A      						=    activation_dropout
											A      						=    activation_function
											A      						=    init_std
											A      						=    encoder_layerdrop
											A      						=    decoder_layerdrop
											A      						=    use_cache
											A      						=    encoder_layers
											A      						=    scale_embedding  # scale factor will be sqrt(d_model) if True
											super().__init__(
											    pad_token_id=__UpperCamelCase						, eos_token_id=__UpperCamelCase						, is_encoder_decoder=__UpperCamelCase						, decoder_start_token_id=__UpperCamelCase						, forced_eos_token_id=__UpperCamelCase						, **__UpperCamelCase						, )


				@property
				def __UpperCamelCase						(							self      :  int       )		->     int:
											return self.encoder_attention_heads


				@property
				def __UpperCamelCase						(							self      :  Dict       )		->     int:
											return self.d_model

"""simple docstring"""





import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort



class __lowerCAmelCase					(       _lowercase  ,					unittest.TestCase   ):




			"""simple docstring"""







			pass





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




			"""simple docstring"""







			@property
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







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




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







								"""simple docstring"""
								A_								=    ort.SessionOptions()
								A_								=    False
								return options




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								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_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=10							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3






			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Tuple:







								"""simple docstring"""
								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_								=    LMSDiscreteScheduler.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, subfolder="scheduler"							, revision="onnx"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, scheduler=_snake_case							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=20							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3




