






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 				_A			(     UpperCamelCase					,					unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							List[Any]				= 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     str=0      )      ->							Any:
           __UpperCAmelCase     				=floats_tensor((1, 3, 128, 128) ,     rng=random.Random(__SCREAMING_SNAKE_CASE      )      )
           __UpperCAmelCase     				=np.random.RandomState(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				={
               """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]      )      ->							int:
           __UpperCAmelCase     				=OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,     provider="""CPUExecutionProvider"""      )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=self.get_dummy_inputs()
           __UpperCAmelCase     				=pipe(**__SCREAMING_SNAKE_CASE      ).images
           __UpperCAmelCase     				=image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]      )
           assert np.abs(image_slice - expected_slice      ).max() < 1e-1



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

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

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]      )

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



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

           # warmup pass to apply optimizations
           __UpperCAmelCase     				=pipe(**self.get_dummy_inputs()      )

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

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]      )

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



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

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

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]      )

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



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

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

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]      )

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



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

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

           assert image.shape == (1, 128, 128, 3)
           __UpperCAmelCase     				=np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]      )

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




@nightly
@require_onnxruntime
@require_torch_gpu
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

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



    @property
    def _a							(				self      :     Dict      )      ->							int:
           __UpperCAmelCase     				=ort.SessionOptions()
           __UpperCAmelCase     				=False
           return options



    def _a							(				self      :     Dict      )      ->							Any:
           __UpperCAmelCase     				=load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""      )
           __UpperCAmelCase     				=init_image.resize((768, 512)      )
           # using the PNDM scheduler by default
           __UpperCAmelCase     				=OnnxStableDiffusionImgaImgPipeline.from_pretrained(
               """CompVis/stable-diffusion-v1-4""" ,     revision="""onnx""" ,     safety_checker=__SCREAMING_SNAKE_CASE ,     feature_extractor=__SCREAMING_SNAKE_CASE ,     provider=self.gpu_provider ,     sess_options=self.gpu_options ,     )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				="""A fantasy landscape, trending on artstation"""

           __UpperCAmelCase     				=np.random.RandomState(0      )
           __UpperCAmelCase     				=pipe(
               prompt=__SCREAMING_SNAKE_CASE ,     image=__SCREAMING_SNAKE_CASE ,     strength=0.75 ,     guidance_scale=7.5 ,     num_inference_steps=10 ,     generator=__SCREAMING_SNAKE_CASE ,     output_type="""np""" ,     )
           __UpperCAmelCase     				=output.images
           __UpperCAmelCase     				=images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 768, 3)
           __UpperCAmelCase     				=np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]      )
           # 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      :     List[str]      )      ->							str:
           __UpperCAmelCase     				=load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""      )
           __UpperCAmelCase     				=init_image.resize((768, 512)      )
           __UpperCAmelCase     				=LMSDiscreteScheduler.from_pretrained(
               """runwayml/stable-diffusion-v1-5""" ,     subfolder="""scheduler""" ,     revision="""onnx"""      )
           __UpperCAmelCase     				=OnnxStableDiffusionImgaImgPipeline.from_pretrained(
               """runwayml/stable-diffusion-v1-5""" ,     revision="""onnx""" ,     scheduler=__SCREAMING_SNAKE_CASE ,     safety_checker=__SCREAMING_SNAKE_CASE ,     feature_extractor=__SCREAMING_SNAKE_CASE ,     provider=self.gpu_provider ,     sess_options=self.gpu_options ,     )
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				="""A fantasy landscape, trending on artstation"""

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

           assert images.shape == (1, 512, 768, 3)
           __UpperCAmelCase     				=np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]      )
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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








from typing import Any



class 	__lowerCamelCase			:







       def __init__(			self: int,A_: Any					):






            '''simple docstring'''




            __UpperCamelCase =			data
            __UpperCamelCase =			None







       def __repr__(			self: Any					):






            '''simple docstring'''




            return F'''Node({self.data})'''



class 	__lowerCamelCase			:







       def __init__(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			None







       def __iter__(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			self.head
            while node:
                 yield node.data
                 __UpperCamelCase =			node.next







       def __len__(			self: List[str]					):






            '''simple docstring'''




            return sum(1 for _ in self					)







       def __repr__(			self: Any					):






            '''simple docstring'''




            return "->".join([str(A_					) for item in self]					)







       def __getitem__(			self: int,A_: int					):






            '''simple docstring'''




            if not 0 <= index < len(self					):
                 raise ValueError('list index out of range.'					)
            for i, node in enumerate(self					):
                 if i == index:
                      return node
            return None







       def __setitem__(			self: int,A_: int,A_: Any					):






            '''simple docstring'''




            if not 0 <= index < len(self					):
                 raise ValueError('list index out of range.'					)
            __UpperCamelCase =			self.head
            for _ in range(A_					):
                 __UpperCamelCase =			current.next
            __UpperCamelCase =			data







       def 		snake_case_	(			self: Union[str, Any],A_: Any					):






            '''simple docstring'''




            self.insert_nth(len(self					),A_					)







       def 		snake_case_	(			self: List[Any],A_: Any					):






            '''simple docstring'''




            self.insert_nth(0,A_					)







       def 		snake_case_	(			self: Optional[Any],A_: int,A_: Any					):






            '''simple docstring'''




            if not 0 <= index <= len(self					):
                 raise IndexError('list index out of range'					)
            __UpperCamelCase =			Node(A_					)
            if self.head is None:
                 __UpperCamelCase =			new_node
            elif index == 0:
                 __UpperCamelCase =			self.head  # link new_node to head
                 __UpperCamelCase =			new_node
            else:
                 __UpperCamelCase =			self.head
                 for _ in range(index - 1					):
                      __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next
                 __UpperCamelCase =			new_node







       def 		snake_case_	(			self: str					):  # print every node data






            '''simple docstring'''




            print(self					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return self.delete_nth(0					)







       def 		snake_case_	(			self: str					):  # delete from tail






            '''simple docstring'''




            return self.delete_nth(len(self					) - 1					)







       def 		snake_case_	(			self: Any,A_: int = 0					):






            '''simple docstring'''




            if not 0 <= index <= len(self					) - 1:  # test if index is valid
                 raise IndexError('List index out of range.'					)
            __UpperCamelCase =			self.head  # default first node
            if index == 0:
                 __UpperCamelCase =			self.head.next
            else:
                 __UpperCamelCase =			self.head
                 for _ in range(index - 1					):
                      __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next.next
            return delete_node.data







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            return self.head is None







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			None
            __UpperCamelCase =			self.head

            while current:
                 # Store the current node's next node.
                 __UpperCamelCase =			current.next
                 # Make the current node's next point backwards
                 __UpperCamelCase =			prev
                 # Make the previous node be the current node
                 __UpperCamelCase =			current
                 # Make the current node the next node (to progress iteration)
                 __UpperCamelCase =			next_node
            # Return prev in order to put the head at the end
            __UpperCamelCase =			prev






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			LinkedList()
     assert linked_list.is_empty() is True
     assert str(_lowercase ) == ""

     try:
          linked_list.delete_head()
          raise AssertionError  # This should not happen.
     except IndexError:
          assert True  # This should happen.

     try:
          linked_list.delete_tail()
          raise AssertionError  # This should not happen.
     except IndexError:
          assert True  # This should happen.

     for i in range(10 ):
          assert len(_lowercase ) == i
          linked_list.insert_nth(_lowercase					,  i + 1 )
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1					,  11 ) )

     linked_list.insert_head(0 )
     linked_list.insert_tail(11 )
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(0					,  12 ) )

     assert linked_list.delete_head() == 0
     assert linked_list.delete_nth(9 ) == 10
     assert linked_list.delete_tail() == 11
     assert len(_lowercase ) == 9
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1					,  10 ) )

     assert all(linked_list[i] == i + 1 for i in range(0					,  9 ) ) is True

     for i in range(0					,  9 ):
          __UpperCamelCase =			-i
     assert all(linked_list[i] == -i for i in range(0					,  9 ) ) is True

     linked_list.reverse()
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(-8					,  1 ) )






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			[
         -9,
         1_00,
         Node(77_34_51_12 ),
         'dlrow olleH',
         7,
         55_55,
         0,
         -1_92.5_55_55,
         'Hello, world!',
         77.9,
         Node(10 ),
         None,
         None,
         12.20,
     ]
     __UpperCamelCase =			LinkedList()

     for i in test_input:
          linked_list.insert_tail(_lowercase )

     # Check if it's empty or not
     assert linked_list.is_empty() is False
     assert (
         str(_lowercase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
         "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
     )

     # Delete the head
     __UpperCamelCase =			linked_list.delete_head()
     assert result == -9
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None->None->12.2"
     )

     # Delete the tail
     __UpperCamelCase =			linked_list.delete_tail()
     assert result == 12.2
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None->None"
     )

     # Delete a node in specific location in linked list
     __UpperCamelCase =			linked_list.delete_nth(10 )
     assert result is None
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None"
     )

     # Add a Node instance to its head
     linked_list.insert_head(Node('Hello again, world!' ) )
     assert (
         str(_lowercase )
         == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
         "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
     )

     # Add None to its tail
     linked_list.insert_tail(_lowercase )
     assert (
         str(_lowercase )
         == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
         "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
     )

     # Reverse the linked list
     linked_list.reverse()
     assert (
         str(_lowercase )
         == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
         "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
     )






def 		_A		(		)					-> List[str]:


     """simple docstring"""


     from doctest import testmod

     testmod()

     __UpperCamelCase =			LinkedList()
     linked_list.insert_head(input('Inserting 1st at head ' ).strip() )
     linked_list.insert_head(input('Inserting 2nd at head ' ).strip() )
     print('\nPrint list:' )
     linked_list.print_list()
     linked_list.insert_tail(input('\nInserting 1st at tail ' ).strip() )
     linked_list.insert_tail(input('Inserting 2nd at tail ' ).strip() )
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nDelete head' )
     linked_list.delete_head()
     print('Delete tail' )
     linked_list.delete_tail()
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nReverse linked list' )
     linked_list.reverse()
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nString representation of linked list:' )
     print(_lowercase )
     print('\nReading/changing Node data using indexing:' )
     print(f'''Element at Position 1: {linked_list[1]}''' )
     __UpperCamelCase =			input('Enter New Value: ' ).strip()
     print('New list:' )
     print(_lowercase )
     print(f'''length of linked_list is : {len(_lowercase )}''' )


if __name__ == "__main__":
    main()


