






'''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 				lowercase       (							_lowerCamelCase ,       unittest.TestCase      ):


				'''simple docstring'''

				lowerCAmelCase__      				=							"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"

				def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     List[str]=0     )							->							Union[str, Any]:

					'''simple docstring'''




					lowerCamelCase__  =     np.random.RandomState(__lowerCamelCase     )
					lowerCamelCase__  =     {
					    "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    a__				(					self     :     Optional[Any]     )							->							List[str]:

					'''simple docstring'''




					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
					pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

					assert image.shape == (1, 128, 128, 3)
					lowerCamelCase__  =     np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]     )

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

				def    a__				(					self     :     str     )							->							int:

					'''simple docstring'''




					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
					lowerCamelCase__  =     PNDMScheduler.from_config(pipe.scheduler.config				,     skip_prk_steps=__lowerCamelCase     )
					pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

					assert image.shape == (1, 128, 128, 3)
					lowerCamelCase__  =     np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]     )

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

				def    a__				(					self     :     Any     )							->							Tuple:

					'''simple docstring'''




					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
					lowerCamelCase__  =     LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
					pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

					assert image.shape == (1, 128, 128, 3)
					lowerCamelCase__  =     np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]     )

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

				def    a__				(					self     :     List[str]     )							->							str:

					'''simple docstring'''




					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
					lowerCamelCase__  =     EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
					pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

					assert image.shape == (1, 128, 128, 3)
					lowerCamelCase__  =     np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]     )

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

				def    a__				(					self     :     Optional[Any]     )							->							str:

					'''simple docstring'''




					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
					lowerCamelCase__  =     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
					pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

					assert image.shape == (1, 128, 128, 3)
					lowerCamelCase__  =     np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]     )

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

				def    a__				(					self     :     Dict     )							->							str:

					'''simple docstring'''




					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
					lowerCamelCase__  =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
					pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

					assert image.shape == (1, 128, 128, 3)
					lowerCamelCase__  =     np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]     )

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

				def    a__				(					self     :     str     )							->							Optional[Any]:

					'''simple docstring'''




					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
					pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

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

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

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

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

					lowerCamelCase__  =     prompt_embeds

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

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




				def    a__				(					self     :     Tuple     )							->							Optional[Any]:

					'''simple docstring'''




					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
					pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

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

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

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

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

					lowerCamelCase__  ,	lowerCamelCase__  =     embeds

					# forward
					lowerCamelCase__  =     pipe(**__lowerCamelCase     )
					lowerCamelCase__  =     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 				lowercase       (							unittest.TestCase      ):


				'''simple docstring'''

				@property
				def    a__				(					self     :     Any     )							->							Tuple:

					'''simple docstring'''




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

				@property
				def    a__				(					self     :     Optional[int]     )							->							Optional[Any]:

					'''simple docstring'''




					lowerCamelCase__  =     ort.SessionOptions()
					lowerCamelCase__  =     False
					return options

				def    a__				(					self     :     Union[str, Any]     )							->							Any:

					'''simple docstring'''




					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(
					    "CompVis/stable-diffusion-v1-4"				,     revision="onnx"				,     safety_checker=__lowerCamelCase				,     feature_extractor=__lowerCamelCase				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
					sd_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

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

					assert image.shape == (1, 512, 512, 3)
					lowerCamelCase__  =     np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]     )

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

				def    a__				(					self     :     Optional[Any]     )							->							Dict:

					'''simple docstring'''




					lowerCamelCase__  =     DDIMScheduler.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,     subfolder="scheduler"				,     revision="onnx"     )
					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,     revision="onnx"				,     scheduler=__lowerCamelCase				,     safety_checker=__lowerCamelCase				,     feature_extractor=__lowerCamelCase				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
					sd_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

					assert image.shape == (1, 512, 512, 3)
					lowerCamelCase__  =     np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]     )

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

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

					'''simple docstring'''




					lowerCamelCase__  =     LMSDiscreteScheduler.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,     subfolder="scheduler"				,     revision="onnx"     )
					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,     revision="onnx"				,     scheduler=__lowerCamelCase				,     safety_checker=__lowerCamelCase				,     feature_extractor=__lowerCamelCase				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
					sd_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

					assert image.shape == (1, 512, 512, 3)
					lowerCamelCase__  =     np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]     )

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

				def    a__				(					self     :     Optional[int]     )							->							Any:

					'''simple docstring'''




					lowerCamelCase__  =     0

					def test_callback_fn(__lowerCamelCase     :     int				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     np.ndarray     ) -> None:
						lowerCamelCase__  =     True
						nonlocal number_of_steps
						number_of_steps += 1
						if step == 0:
							assert latents.shape == (1, 4, 64, 64)
							lowerCamelCase__  =     latents[0, -3:, -3:, -1]
							lowerCamelCase__  =     np.array(
							    [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]     )

							assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 1E-3
						elif step == 5:
							assert latents.shape == (1, 4, 64, 64)
							lowerCamelCase__  =     latents[0, -3:, -3:, -1]
							lowerCamelCase__  =     np.array(
							    [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]     )

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

					lowerCamelCase__  =     False

					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,     revision="onnx"				,     safety_checker=__lowerCamelCase				,     feature_extractor=__lowerCamelCase				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
					pipe.set_progress_bar_config(disable=__lowerCamelCase     )

					lowerCamelCase__  =     "Andromeda galaxy in a bottle"

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




				def    a__				(					self     :     int     )							->							List[Any]:

					'''simple docstring'''




					lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,     revision="onnx"				,     safety_checker=__lowerCamelCase				,     feature_extractor=__lowerCamelCase				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
					assert isinstance(__lowerCamelCase				,     __lowerCamelCase     )
					assert pipe.safety_checker is None

					lowerCamelCase__  =     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(__lowerCamelCase     )
						lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(__lowerCamelCase     )

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









'''simple docstring'''





class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     Union[str, Any]     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     {}  # Mapping from char to TrieNode
     lowerCamelCase__  =     False

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     list[str]     )							->							None:

     '''simple docstring'''




     for word in words:
      self.insert(__lowerCamelCase     )

    def    a__				(					self     :     int				,     __lowerCamelCase     :     str     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     self
     for char in word:
      if char not in curr.nodes:
       lowerCamelCase__  =     TrieNode()
      lowerCamelCase__  =     curr.nodes[char]
     lowerCamelCase__  =     True

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     str     )							->							bool:

     '''simple docstring'''




     lowerCamelCase__  =     self
     for char in word:
      if char not in curr.nodes:
       return False
      lowerCamelCase__  =     curr.nodes[char]
     return curr.is_leaf




    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     str     )							->							None:

     '''simple docstring'''




     def _delete(__lowerCamelCase     :     TrieNode				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     int     ) -> bool:
      if index == len(__lowerCamelCase     ):
       # If word does not exist
       if not curr.is_leaf:
        return False
       lowerCamelCase__  =     False
       return len(curr.nodes     ) == 0
      lowerCamelCase__  =     word[index]
      lowerCamelCase__  =     curr.nodes.get(__lowerCamelCase     )
      # If char not in current trie node
      if not char_node:
       return False
      # Flag to check if node can be deleted
      lowerCamelCase__  =     _delete(__lowerCamelCase				,     __lowerCamelCase				,     index + 1     )
      if delete_curr:
       del curr.nodes[char]
       return len(curr.nodes     ) == 0
      return delete_curr

     _delete(self				,     __lowerCamelCase				,     0     )






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 if node.is_leaf:
  print(lowercase__				,     end=" ")

 for key, value in node.nodes.items():
  print_words(lowercase__				,     word + key)






def    lowerCamelCase_     (    ):
 lowerCamelCase__  =     "banana bananas bandana band apple all beast".split()
 lowerCamelCase__  =     TrieNode()
 root.insert_many(lowercase__)
 # print_words(root, "")
 assert all(root.find(lowercase__) for word in words)
 assert root.find("banana")
 assert not root.find("bandanas")
 assert not root.find("apps")
 assert root.find("apple")
 assert root.find("all")
 root.delete("all")
 assert not root.find("all")
 root.delete("banana")
 assert not root.find("banana")
 assert root.find("bananas")
 return True






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 print(str(lowercase__)				,     "works!" if passes else "doesn't work :(")






def    lowerCamelCase_     (    ):
 assert test_trie()






def    lowerCamelCase_     (    ):
 print_results("Testing trie functionality"				,     test_trie())


if __name__ == "__main__":
 main()

