'''simple docstring'''





import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, 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	):
    a__       :						str          =  """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



    def 					_lowercase						(self    :    List[str]   ,			__a    :    str=0 ):
         UpperCAmelCase_           =   np.random.RandomState(__a )
         UpperCAmelCase_           =   {
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         UpperCAmelCase_           =   PNDMScheduler.from_config(pipe.scheduler.config   ,			skip_prk_steps=__a )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         UpperCAmelCase_           =   LMSDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         UpperCAmelCase_           =   EulerDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         UpperCAmelCase_           =   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         UpperCAmelCase_           =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   pipe(**__a ).images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =   np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   3 * [inputs["prompt"]]

         # forward
         UpperCAmelCase_           =   pipe(**__a )
         UpperCAmelCase_           =   output.images[0, -3:, -3:, -1]

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   3 * [inputs.pop("prompt" )]

         UpperCAmelCase_           =   pipe.tokenizer(
             __a   ,			padding="max_length"   ,			max_length=pipe.tokenizer.model_max_length   ,			truncation=__a   ,			return_tensors="np"   ,			)
         UpperCAmelCase_           =   text_inputs["input_ids"]

         UpperCAmelCase_           =   pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]

         UpperCAmelCase_           =   prompt_embeds

         # forward
         UpperCAmelCase_           =   pipe(**__a )
         UpperCAmelCase_           =   output.images[0, -3:, -3:, -1]

         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4






    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,			provider="CPUExecutionProvider" )
         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   3 * ["this is a negative prompt"]
         UpperCAmelCase_           =   negative_prompt
         UpperCAmelCase_           =   3 * [inputs["prompt"]]

         # forward
         UpperCAmelCase_           =   pipe(**__a )
         UpperCAmelCase_           =   output.images[0, -3:, -3:, -1]

         UpperCAmelCase_           =   self.get_dummy_inputs()
         UpperCAmelCase_           =   3 * [inputs.pop("prompt" )]

         UpperCAmelCase_           =   []
         for p in [prompt, negative_prompt]:
              UpperCAmelCase_           =   pipe.tokenizer(
                  __a   ,			padding="max_length"   ,			max_length=pipe.tokenizer.model_max_length   ,			truncation=__a   ,			return_tensors="np"   ,			)
              UpperCAmelCase_           =   text_inputs["input_ids"]

              embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )

         UpperCAmelCase_    , UpperCAmelCase_           =   embeds

         # forward
         UpperCAmelCase_           =   pipe(**__a )
         UpperCAmelCase_           =   output.images[0, -3:, -3:, -1]

         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4




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



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



    @property
    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   ort.SessionOptions()
         UpperCAmelCase_           =   False
         return options



    def 					_lowercase						(self    :    str ):
         # using the PNDM scheduler by default
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"   ,			revision="onnx"   ,			safety_checker=__a   ,			feature_extractor=__a   ,			provider=self.gpu_provider   ,			sess_options=self.gpu_options   ,			)
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A painting of a squirrel eating a burger"
         np.random.seed(0 )
         UpperCAmelCase_           =   sd_pipe([prompt]   ,			guidance_scale=6.0   ,			num_inference_steps=10   ,			output_type="np" )
         UpperCAmelCase_           =   output.images

         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =   np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20] )

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



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   DDIMScheduler.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			subfolder="scheduler"   ,			revision="onnx" )
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			revision="onnx"   ,			scheduler=__a   ,			safety_checker=__a   ,			feature_extractor=__a   ,			provider=self.gpu_provider   ,			sess_options=self.gpu_options   ,			)
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "open neural network exchange"
         UpperCAmelCase_           =   np.random.RandomState(0 )
         UpperCAmelCase_           =   sd_pipe([prompt]   ,			guidance_scale=7.5   ,			num_inference_steps=10   ,			generator=__a   ,			output_type="np" )
         UpperCAmelCase_           =   output.images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =   np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86] )

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



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   LMSDiscreteScheduler.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			subfolder="scheduler"   ,			revision="onnx" )
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			revision="onnx"   ,			scheduler=__a   ,			safety_checker=__a   ,			feature_extractor=__a   ,			provider=self.gpu_provider   ,			sess_options=self.gpu_options   ,			)
         sd_pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "open neural network exchange"
         UpperCAmelCase_           =   np.random.RandomState(0 )
         UpperCAmelCase_           =   sd_pipe([prompt]   ,			guidance_scale=7.5   ,			num_inference_steps=10   ,			generator=__a   ,			output_type="np" )
         UpperCAmelCase_           =   output.images
         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =   np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61] )

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



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   0

         def test_callback_fn(__a    :    int   ,			__a    :    int   ,			__a    :    np.ndarray ) -> None:
              UpperCAmelCase_           =   True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 0:
                   assert latents.shape == (1, 4, 64, 64)
                   UpperCAmelCase_           =   latents[0, -3:, -3:, -1]
                   UpperCAmelCase_           =   np.array(
                       [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67] )

                   assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
              elif step == 5:
                   assert latents.shape == (1, 4, 64, 64)
                   UpperCAmelCase_           =   latents[0, -3:, -3:, -1]
                   UpperCAmelCase_           =   np.array(
                       [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75] )

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

         UpperCAmelCase_           =   False

         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			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_           =   "Andromeda galaxy in a bottle"

         UpperCAmelCase_           =   np.random.RandomState(0 )
         pipe(
             prompt=__a   ,			num_inference_steps=5   ,			guidance_scale=7.5   ,			generator=__a   ,			callback=__a   ,			callback_steps=1   ,			)
         assert test_callback_fn.has_been_called
         assert number_of_steps == 6






    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,			revision="onnx"   ,			safety_checker=__a   ,			feature_extractor=__a   ,			provider=self.gpu_provider   ,			sess_options=self.gpu_options   ,			)
         assert isinstance(__a   ,			__a )
         assert pipe.safety_checker is None

         UpperCAmelCase_           =   pipe("example prompt"   ,			num_inference_steps=2 ).images[0]
         assert image is not None

         # check that there's no error when saving a pipeline with one of the models being None
         with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(__a )
              UpperCAmelCase_           =   OnnxStableDiffusionPipeline.from_pretrained(__a )

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         UpperCAmelCase_           =   pipe("example prompt"   ,			num_inference_steps=2 ).images[0]
         assert image is not None





def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		[0 for i in range(len(SCREAMING_SNAKE_CASE	)	)]

    # initialize interval's left pointer and right pointer
    __UpperCamelCase  ,       __UpperCamelCase     :str			  =		0, 0

    for i in range(1  ,			len(SCREAMING_SNAKE_CASE	)	):
        # case when current index is inside the interval
        if i <= right_pointer:
            __UpperCamelCase     :Union[str, Any]			  =		min(right_pointer - i + 1  ,			z_result[i - left_pointer]	)
            __UpperCamelCase     :Tuple			  =		min_edge

        while go_next(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
            z_result[i] += 1

        # if new index's result gives us more right interval,
        # we've to update left_pointer and right_pointer
        if i + z_result[i] - 1 > right_pointer:
            __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		i, i + z_result[i] - 1

    return z_result



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    return i + z_result[i] < len(SCREAMING_SNAKE_CASE	) and s[z_result[i]] == s[i + z_result[i]]



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		0
    # concatenate 'pattern' and 'input_str' and call z_function
    # with concatenated string
    __UpperCamelCase     :Tuple			  =		z_function(pattern + input_str	)

    for val in z_result:
        # if value is greater then length of the pattern string
        # that means this index is starting position of substring
        # which is equal to pattern string
        if val >= len(SCREAMING_SNAKE_CASE	):
            answer += 1

    return answer


if __name__ == "__main__":
    import doctest

    doctest.testmod()





