




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 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						# FIXME: add fast tests
						pass




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

						@property
						def      A__    (		self :int			):
										'''simple docstring'''

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

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						int										=ort.SessionOptions()
										__magic_name__						:						int										=False
										return options

						def      A__    (		self :Tuple			):
										'''simple docstring'''

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

										__magic_name__						:						Tuple										="""A red cat sitting on a park bench"""

										__magic_name__						:						int										=np.random.RandomState(0			)
										__magic_name__						:						List[str]										=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"""     ,			)
										__magic_name__						:						Union[str, Any]										=output.images
										__magic_name__						:						str										=images[0, 2_55:2_58, 2_55:2_58, -1]

										assert images.shape == (1, 5_12, 5_12, 3)
										__magic_name__						:						Tuple										=np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]			)

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


						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/in_paint/overture-creations-5sI6fQgYIuo.png"""			)
										__magic_name__						:						Tuple										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""			)
										__magic_name__						:						Optional[int]										=LMSDiscreteScheduler.from_pretrained(
										    """runwayml/stable-diffusion-inpainting"""     ,			subfolder="""scheduler"""     ,			revision="""onnx"""			)
										__magic_name__						:						Optional[Any]										=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			)

										__magic_name__						:						str										="""A red cat sitting on a park bench"""

										__magic_name__						:						Optional[int]										=np.random.RandomState(0			)
										__magic_name__						:						Optional[int]										=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"""     ,			)
										__magic_name__						:						Union[str, Any]										=output.images
										__magic_name__						:						Union[str, Any]										=images[0, 2_55:2_58, 2_55:2_58, -1]

										assert images.shape == (1, 5_12, 5_12, 3)
										__magic_name__						:						Any										=np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]			)

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







'''simple docstring'''



from typing import TYPE_CHECKING

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


_UpperCamelCase						:  str							       =				{
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  Dict							       =				[
         "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoXJapaneseForCausalLM",
         "GPTNeoXJapaneseLayer",
         "GPTNeoXJapaneseModel",
         "GPTNeoXJapanesePreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
     from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neox_japanese import (
              GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoXJapaneseForCausalLM,
              GPTNeoXJapaneseLayer,
              GPTNeoXJapaneseModel,
              GPTNeoXJapanesePreTrainedModel,
          )


else:
     import sys

     _UpperCamelCase						:  List[Any]							       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





