'''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       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase=0			)  ->  List[Any]:
        lowercase__    :   Union[str, Any]						= np.random.RandomState(__lowerCAmelCase			)
        lowercase__    :   Optional[Any]						= {
            '''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 _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   int						= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Tuple						= self.get_dummy_inputs()
        lowercase__    :   Union[str, Any]						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   Optional[int]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        lowercase__    :   Optional[Any]						= 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 _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   int						= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        lowercase__    :   List[Any]						= PNDMScheduler.from_config(pipe.scheduler.config     ,       skip_prk_steps=__lowerCAmelCase			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

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

        assert image.shape == (1, 128, 128, 3)
        lowercase__    :   Tuple						= 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 _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   Optional[int]						= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        lowercase__    :   Any						= LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Optional[int]						= self.get_dummy_inputs()
        lowercase__    :   Union[str, Any]						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   int						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        lowercase__    :   List[str]						= 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 _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   Optional[int]						= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        lowercase__    :   List[Any]						= EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= self.get_dummy_inputs()
        lowercase__    :   Tuple						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   List[str]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        lowercase__    :   int						= 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 _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Union[str, Any]						= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        lowercase__    :   Dict						= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Optional[Any]						= self.get_dummy_inputs()
        lowercase__    :   Optional[int]						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   Any						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 128, 128, 3)
        lowercase__    :   Optional[int]						= 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 _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   str						= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        lowercase__    :   List[Any]						= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

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

        assert image.shape == (1, 128, 128, 3)
        lowercase__    :   str						= 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 _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   Tuple						= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   List[Any]						= self.get_dummy_inputs()
        lowercase__    :   Union[str, Any]						= 3 * [inputs['''prompt''']]

        # forward
        lowercase__    :   Optional[int]						= pipe(**__lowerCAmelCase			)
        lowercase__    :   Tuple						= output.images[0, -3:, -3:, -1]

        lowercase__    :   Any						= self.get_dummy_inputs()
        lowercase__    :   Any						= 3 * [inputs.pop('''prompt'''			)]

        lowercase__    :   Any						= pipe.tokenizer(
            __lowerCAmelCase     ,       padding='''max_length'''     ,       max_length=pipe.tokenizer.model_max_length     ,       truncation=__lowerCAmelCase     ,       return_tensors='''np'''     ,       )
        lowercase__    :   int						= text_inputs['''input_ids''']

        lowercase__    :   Any						= pipe.text_encoder(input_ids=text_inputs.astype(np.intaa			)			)[0]

        lowercase__    :   Tuple						= prompt_embeds

        # forward
        lowercase__    :   str						= pipe(**__lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= output.images[0, -3:, -3:, -1]

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


       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   Tuple						= OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Optional[int]						= self.get_dummy_inputs()
        lowercase__    :   Union[str, Any]						= 3 * ['''this is a negative prompt''']
        lowercase__    :   Union[str, Any]						= negative_prompt
        lowercase__    :   Optional[int]						= 3 * [inputs['''prompt''']]

        # forward
        lowercase__    :   Optional[Any]						= pipe(**__lowerCAmelCase			)
        lowercase__    :   Any						= output.images[0, -3:, -3:, -1]

        lowercase__    :   Optional[int]						= self.get_dummy_inputs()
        lowercase__    :   Dict						= 3 * [inputs.pop('''prompt'''			)]

        lowercase__    :   Optional[Any]						= []
        for p in [prompt, negative_prompt]:
         lowercase__    :   Optional[int]						= pipe.tokenizer(
             __lowerCAmelCase     ,       padding='''max_length'''     ,       max_length=pipe.tokenizer.model_max_length     ,       truncation=__lowerCAmelCase     ,       return_tensors='''np'''     ,       )
         lowercase__    :   Union[str, Any]						= text_inputs['''input_ids''']

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

        lowercase__			,							lowercase__    :   Tuple						= embeds

        # forward
        lowercase__    :   str						= pipe(**__lowerCAmelCase			)
        lowercase__    :   int						= 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       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



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

       @property
       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Union[str, Any]						= ort.SessionOptions()
        lowercase__    :   Tuple						= False
        return options

       def _lowerCAmelCase(						self			)  ->  Tuple:
        # using the PNDM scheduler by default
        lowercase__    :   Dict						= 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			)

        lowercase__    :   Optional[int]						= '''A painting of a squirrel eating a burger'''
        np.random.seed(0			)
        lowercase__    :   Dict						= sd_pipe([prompt]     ,       guidance_scale=6.0     ,       num_inference_steps=10     ,       output_type='''np'''			)
        lowercase__    :   str						= output.images

        lowercase__    :   List[Any]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__    :   Tuple						= 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 _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   int						= DDIMScheduler.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''     ,       subfolder='''scheduler'''     ,       revision='''onnx'''			)
        lowercase__    :   Dict						= 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			)

        lowercase__    :   Dict						= '''open neural network exchange'''
        lowercase__    :   Union[str, Any]						= np.random.RandomState(0			)
        lowercase__    :   List[str]						= sd_pipe([prompt]     ,       guidance_scale=7.5     ,       num_inference_steps=10     ,       generator=__lowerCAmelCase     ,       output_type='''np'''			)
        lowercase__    :   Dict						= output.images
        lowercase__    :   Tuple						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__    :   Dict						= 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 _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   Optional[Any]						= LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''     ,       subfolder='''scheduler'''     ,       revision='''onnx'''			)
        lowercase__    :   Dict						= 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			)

        lowercase__    :   Union[str, Any]						= '''open neural network exchange'''
        lowercase__    :   Union[str, Any]						= np.random.RandomState(0			)
        lowercase__    :   Tuple						= sd_pipe([prompt]     ,       guidance_scale=7.5     ,       num_inference_steps=10     ,       generator=__lowerCAmelCase     ,       output_type='''np'''			)
        lowercase__    :   Tuple						= output.images
        lowercase__    :   str						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__    :   Tuple						= 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 _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Optional[Any]						= 0

        def test_callback_fn(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			) -> None:
         lowercase__    :   Dict						= True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 0:
          assert latents.shape == (1, 4, 64, 64)
          lowercase__    :   List[Any]						= latents[0, -3:, -3:, -1]
          lowercase__    :   List[str]						= 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)
          lowercase__    :   Dict						= latents[0, -3:, -3:, -1]
          lowercase__    :   List[str]						= 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

        lowercase__    :   Optional[Any]						= False

        lowercase__    :   Optional[Any]						= 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			)

        lowercase__    :   List[str]						= '''Andromeda galaxy in a bottle'''

        lowercase__    :   Dict						= 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 _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   int						= 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

        lowercase__    :   Tuple						= 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			)
         lowercase__    :   int						= OnnxStableDiffusionPipeline.from_pretrained(__lowerCAmelCase			)

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


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= len(UpperCAmelCase					)
 print('''The following activities are selected:'''					)

 # The first activity is always selected
 lowercase__    :   str						= 0
 print(UpperCAmelCase		,					end=''','''					)

 # Consider rest of the activities
 for j in range(UpperCAmelCase					):
  # If this activity has start time greater than
  # or equal to the finish time of previously
  # selected activity, then select it
  if start[j] >= finish[i]:
   print(UpperCAmelCase		,					end=''','''					)
   lowercase__    :   str						= j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __a:			str	       =       [1, 3, 0, 5, 8, 5]
  __a:			Optional[Any]	       =       [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)

