




import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"

     def       a__			(						self	,  _a=0       )		->							str:
           _A							:  Union[str, Any]    =		floats_tensor((1, 3, 128, 128)	,  rng=random.Random(_a       )       )
           _A							:  Any    =		np.random.RandomState(_a       )
           _A							:  List[str]    =		{
               """prompt""": """A painting of a squirrel eating a burger""",
               """image""": image,
               """generator""": generator,
               """num_inference_steps""": 3,
               """strength""": 0.75,
               """guidance_scale""": 7.5,
               """output_type""": """numpy""",
           }
           return inputs

     def       a__			(						self       )		->							Optional[Any]:
           _A							:  List[str]    =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,  provider="""CPUExecutionProvider"""       )
           pipe.set_progress_bar_config(disable=_a       )

           _A							:  int    =		self.get_dummy_inputs()
           _A							:  Union[str, Any]    =		pipe(**_a       ).images
           _A							:  Union[str, Any]    =		image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 128, 128, 3)
           _A							:  int    =		np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]       )
           assert np.abs(image_slice - expected_slice       ).max() < 1e-1

     def       a__			(						self       )		->							List[str]:
           _A							:  Optional[Any]    =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,  provider="""CPUExecutionProvider"""       )
           _A							:  List[Any]    =		PNDMScheduler.from_config(pipe.scheduler.config	,  skip_prk_steps=_a       )
           pipe.set_progress_bar_config(disable=_a       )

           _A							:  Dict    =		self.get_dummy_inputs()
           _A							:  int    =		pipe(**_a       ).images
           _A							:  str    =		image[0, -3:, -3:, -1]

           assert image.shape == (1, 128, 128, 3)
           _A							:  List[str]    =		np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]       )

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

     def       a__			(						self       )		->							Any:
           _A							:  Dict    =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,  provider="""CPUExecutionProvider"""       )
           _A							:  Union[str, Any]    =		LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
           pipe.set_progress_bar_config(disable=_a       )

           # warmup pass to apply optimizations
           _A							:  Union[str, Any]    =		pipe(**self.get_dummy_inputs()       )

           _A							:  int    =		self.get_dummy_inputs()
           _A							:  List[str]    =		pipe(**_a       ).images
           _A							:  Optional[Any]    =		image[0, -3:, -3:, -1]

           assert image.shape == (1, 128, 128, 3)
           _A							:  List[str]    =		np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]       )

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

     def       a__			(						self       )		->							Optional[Any]:
           _A							:  str    =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,  provider="""CPUExecutionProvider"""       )
           _A							:  Dict    =		EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
           pipe.set_progress_bar_config(disable=_a       )

           _A							:  Dict    =		self.get_dummy_inputs()
           _A							:  str    =		pipe(**_a       ).images
           _A							:  Dict    =		image[0, -3:, -3:, -1]

           assert image.shape == (1, 128, 128, 3)
           _A							:  Dict    =		np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]       )

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

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Optional[int]    =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,  provider="""CPUExecutionProvider"""       )
           _A							:  str    =		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
           pipe.set_progress_bar_config(disable=_a       )

           _A							:  List[Any]    =		self.get_dummy_inputs()
           _A							:  str    =		pipe(**_a       ).images
           _A							:  Optional[Any]    =		image[0, -3:, -3:, -1]

           assert image.shape == (1, 128, 128, 3)
           _A							:  Union[str, Any]    =		np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]       )

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



     def       a__			(						self       )		->							List[Any]:
           _A							:  Optional[int]    =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,  provider="""CPUExecutionProvider"""       )
           _A							:  Dict    =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
           pipe.set_progress_bar_config(disable=_a       )

           _A							:  Any    =		self.get_dummy_inputs()
           _A							:  Any    =		pipe(**_a       ).images
           _A							:  List[str]    =		image[0, -3:, -3:, -1]

           assert image.shape == (1, 128, 128, 3)
           _A							:  int    =		np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]       )

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







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

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

     @property
     def       a__			(						self       )		->							List[str]:
           _A							:  str    =		ort.SessionOptions()
           _A							:  Any    =		False
           return options

     def       a__			(						self       )		->							List[str]:
           _A							:  Tuple    =		load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""       )
           _A							:  Optional[int]    =		init_image.resize((768, 512)       )
           # using the PNDM scheduler by default
           _A							:  Optional[int]    =		OnnxStableDiffusionImgaImgPipeline.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       )

           _A							:  List[Any]    =		"""A fantasy landscape, trending on artstation"""

           _A							:  List[Any]    =		np.random.RandomState(0       )
           _A							:  Dict    =		pipe(
               prompt=_a	,  image=_a	,  strength=0.75	,  guidance_scale=7.5	,  num_inference_steps=10	,  generator=_a	,  output_type="""np"""	,  )
           _A							:  str    =		output.images
           _A							:  Tuple    =		images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 768, 3)
           _A							:  Optional[Any]    =		np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]       )
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



     def       a__			(						self       )		->							Dict:
           _A							:  Dict    =		load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""       )
           _A							:  List[str]    =		init_image.resize((768, 512)       )
           _A							:  List[str]    =		LMSDiscreteScheduler.from_pretrained(
               """runwayml/stable-diffusion-v1-5"""	,  subfolder="""scheduler"""	,  revision="""onnx"""       )
           _A							:  Any    =		OnnxStableDiffusionImgaImgPipeline.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	,  )
           pipe.set_progress_bar_config(disable=_a       )

           _A							:  Any    =		"""A fantasy landscape, trending on artstation"""

           _A							:  Optional[Any]    =		np.random.RandomState(0       )
           _A							:  Union[str, Any]    =		pipe(
               prompt=_a	,  image=_a	,  strength=0.75	,  guidance_scale=7.5	,  num_inference_steps=20	,  generator=_a	,  output_type="""np"""	,  )
           _A							:  Optional[Any]    =		output.images
           _A							:  Any    =		images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 768, 3)
           _A							:  Tuple    =		np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]       )
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







from __future__ import annotations







def        lowerCAmelCase_			(  snake_case_,snake_case_ = None  ):
      _A							:  Tuple    =		word_bank or []
      # create a table
      _A							:  int    =		len(snake_case_  ) + 1

      _A							:  list[list[list[str]]]    =		[]
      for _ in range(snake_case_  ):
            table.append([]  )
      # seed value
      _A							:  Dict    =		[[]]  # because empty string has empty combination

      # iterate through the indices
      for i in range(snake_case_  ):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(snake_case_  )] == word:
                              _A							:  list[list[str]]    =		[
                                  [word, *way] for way in table[i]
                              ]
                              # adds the word to every combination the current position holds
                              # now,push that combination to the table[i+len(word)]
                              table[i + len(snake_case_  )] += new_combinations

    # combinations are in reverse order so reverse for better output
      for combination in table[len(snake_case_  )]:
            combination.reverse()

      return table[len(snake_case_  )]


if __name__ == "__main__":
  print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
  print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
  print(
      all_construct(
          "hexagonosaurus",
          ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
      )
  )

