






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 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





     """simple docstring"""






     pass



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





     """simple docstring"""






     @property
     def  _lowercase  (      self  :						List[Any] ):
      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )



     @property
     def  _lowercase  (      self  :						Union[str, Any] ):
      snake_case__				:   Optional[Any]     =						ort.SessionOptions()
      snake_case__				:   Dict     =						False
      return options



     def  _lowercase  (      self  :						Optional[Any] ):
      snake_case__				:   List[str]     =						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__				:   List[Any]     =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
          "runwayml/stable-diffusion-inpainting"							,	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__				:   Dict     =						"A red cat sitting on a park bench"

      snake_case__				:   int     =						np.random.RandomState(0 )
      snake_case__				:   List[Any]     =						pipe(
          prompt=__A							,	image=__A							,	mask_image=__A							,	guidance_scale=7.5							,	num_inference_steps=1_0							,	generator=__A							,	output_type="np"							,	)
      snake_case__				:   List[Any]     =						output.images
      snake_case__				:   Tuple     =						images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

      assert images.shape == (1, 5_1_2, 5_1_2, 3)
      snake_case__				:   int     =						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  _lowercase  (      self  :						Optional[Any] ):
      snake_case__				:   Optional[int]     =						load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo.png" )
      snake_case__				:   List[str]     =						load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
      snake_case__				:   Dict     =						LMSDiscreteScheduler.from_pretrained(
          "runwayml/stable-diffusion-inpainting"							,	subfolder="scheduler"							,	revision="onnx" )
      snake_case__				:   Optional[Any]     =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
          "runwayml/stable-diffusion-inpainting"							,	revision="onnx"							,	scheduler=__A							,	safety_checker=__A							,	feature_extractor=__A							,	provider=self.gpu_provider							,	sess_options=self.gpu_options							,	)
      pipe.set_progress_bar_config(disable=__A )

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

      snake_case__				:   Union[str, Any]     =						np.random.RandomState(0 )
      snake_case__				:   Optional[Any]     =						pipe(
          prompt=__A							,	image=__A							,	mask_image=__A							,	guidance_scale=7.5							,	num_inference_steps=2_0							,	generator=__A							,	output_type="np"							,	)
      snake_case__				:   Optional[int]     =						output.images
      snake_case__				:   Any     =						images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

      assert images.shape == (1, 5_1_2, 5_1_2, 3)
      snake_case__				:   List[str]     =						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











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase      :    List[str]   		=   {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   ["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   ["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   [
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   [
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    int   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



