
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  A_				(			self				)					->      str:


								'''simple docstring'''

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

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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







						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/in_paint/overture-creations-5sI6fQgYIuo.png"""				)
								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""				)
								_UpperCamelCase						    =	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
								_UpperCamelCase						    =	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				)

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

								_UpperCamelCase						    =	np.random.RandomState(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							strength=0.75       ,							guidance_scale=7.5       ,							num_inference_steps=15       ,							generator=a       ,							output_type="""np"""       ,							)

								_UpperCamelCase						    =	output.images[0]

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





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__	=							{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							[
		    "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MCTCTForCTC",
		    "MCTCTModel",
		    "MCTCTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
		from .feature_extraction_mctct import MCTCTFeatureExtractor
		from .processing_mctct import MCTCTProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
		import sys

		lowerCamelCase__	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



