



"""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       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			pass






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

			"""simple docstring"""


			@property
			def   snake_case__	(      self		)      ->	Tuple:
				return (
				    "CUDAExecutionProvider",
				    {
				        "gpu_mem_limit": "15000000000",  # 15GB
				        "arena_extend_strategy": "kSameAsRequested",
				    },
				)


			@property
			def   snake_case__	(      self		)      ->	Any:
				A__											=					ort.SessionOptions()
				A__											=					False
				return options


			def   snake_case__	(      self		)      ->	Union[str, 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__											=					OnnxStableDiffusionInpaintPipeline.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"						,	revision="onnx"						,	safety_checker=SCREAMING_SNAKE_CASE__						,	feature_extractor=SCREAMING_SNAKE_CASE__						,	provider=self.gpu_provider						,	sess_options=self.gpu_options						,	)
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

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

				A__											=					np.random.RandomState(0		)
				A__											=					pipe(
				    prompt=SCREAMING_SNAKE_CASE__						,	image=SCREAMING_SNAKE_CASE__						,	mask_image=SCREAMING_SNAKE_CASE__						,	guidance_scale=7.5						,	num_inference_steps=10						,	generator=SCREAMING_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   snake_case__	(      self		)      ->	str:
				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=SCREAMING_SNAKE_CASE__						,	safety_checker=SCREAMING_SNAKE_CASE__						,	feature_extractor=SCREAMING_SNAKE_CASE__						,	provider=self.gpu_provider						,	sess_options=self.gpu_options						,	)
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

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

				A__											=					np.random.RandomState(0		)
				A__											=					pipe(
				    prompt=SCREAMING_SNAKE_CASE__						,	image=SCREAMING_SNAKE_CASE__						,	mask_image=SCREAMING_SNAKE_CASE__						,	guidance_scale=7.5						,	num_inference_steps=20						,	generator=SCREAMING_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












'''simple docstring'''



from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def 					A__   (			self      :		Optional[int]		)	->	List[str]:


       '''simple docstring'''

       return [
           {"col_1": 3, "col_2": "a"},
           {"col_1": 2, "col_2": "b"},
           {"col_1": 1, "col_2": "c"},
           {"col_1": 0, "col_2": "d"},
       ]






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


       '''simple docstring'''

       lowercase       : List[str]						={'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
       return Dataset.from_dict(UpperCAmelCase		)






      def 					A__   (			self      :		Any		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Tuple						=self._create_example_records()
       lowercase       : Tuple						=Dataset.from_list(UpperCAmelCase		)
       self.assertListEqual(dset.column_names  ,   ['''col_1''', '''col_2''']		)
       for i, r in enumerate(UpperCAmelCase		):
        self.assertDictEqual(UpperCAmelCase  ,   example_records[i]		)






      def 					A__   (			self      :		Dict		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=self._create_example_records()
       lowercase       : Union[str, Any]						=Dataset.from_list(UpperCAmelCase		)
       lowercase       : Tuple						=Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}		)
       self.assertEqual(dset.info  ,   dset_from_dict.info		)






      def 					A__   (			self      :		str		)	->	List[Any]:  # checks what happens with missing columns


       '''simple docstring'''

       lowercase       : Tuple						=[{'''col_1''': 1}, {'''col_2''': '''x'''}]
       lowercase       : Optional[Any]						=Dataset.from_list(UpperCAmelCase		)
       self.assertDictEqual(dset[0]  ,   {'''col_1''': 1}		)
       self.assertDictEqual(dset[1]  ,   {'''col_1''': None}		)  # NB: first record is used for columns






      def 					A__   (			self      :		List[Any]		)	->	int:  # checks if the type can be inferred from the second record


       '''simple docstring'''

       lowercase       : List[Any]						=[{'''col_1''': []}, {'''col_1''': [1, 2]}]
       lowercase       : Union[str, Any]						=Dataset.from_list(UpperCAmelCase		)
       self.assertEqual(dset.info.features['''col_1''']  ,   Sequence(Value('''int64'''		)		)		)






      def 					A__   (			self      :		int		)	->	str:


       '''simple docstring'''

       lowercase       : List[str]						=Dataset.from_list([]		)
       self.assertEqual(len(UpperCAmelCase		)  ,   0		)
       self.assertListEqual(dset.column_names  ,   []		)



