





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 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[Any]				=	"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



    def  lowercase_					(       self   ,				A_=0     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			np.random.RandomState(A_     )
        SCREAMING_SNAKE_CASE__											=			{
            '''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     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        SCREAMING_SNAKE_CASE__											=			PNDMScheduler.from_config(pipe.scheduler.config   ,				skip_prk_steps=A_     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        SCREAMING_SNAKE_CASE__											=			LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        SCREAMING_SNAKE_CASE__											=			EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        SCREAMING_SNAKE_CASE__											=			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        SCREAMING_SNAKE_CASE__											=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
        pipe.set_progress_bar_config(disable=A_     )

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

        assert image.shape == (1, 1_28, 1_28, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        pipe.set_progress_bar_config(disable=A_     )

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_inputs()
        SCREAMING_SNAKE_CASE__											=			3 * [inputs['''prompt''']]

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

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_inputs()
        SCREAMING_SNAKE_CASE__											=			3 * [inputs.pop('''prompt'''     )]

        SCREAMING_SNAKE_CASE__											=			pipe.tokenizer(
            A_   ,				padding='''max_length'''   ,				max_length=pipe.tokenizer.model_max_length   ,				truncation=A_   ,				return_tensors='''np'''   ,				)
        SCREAMING_SNAKE_CASE__											=			text_inputs['''input_ids''']

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

        SCREAMING_SNAKE_CASE__											=			prompt_embeds

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

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




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint   ,				provider='''CPUExecutionProvider'''     )
        pipe.set_progress_bar_config(disable=A_     )

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

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

        SCREAMING_SNAKE_CASE__											=			self.get_dummy_inputs()
        SCREAMING_SNAKE_CASE__											=			3 * [inputs.pop('''prompt'''     )]

        SCREAMING_SNAKE_CASE__											=			[]
        for p in [prompt, negative_prompt]:
            SCREAMING_SNAKE_CASE__											=			pipe.tokenizer(
                A_   ,				padding='''max_length'''   ,				max_length=pipe.tokenizer.model_max_length   ,				truncation=A_   ,				return_tensors='''np'''   ,				)
            SCREAMING_SNAKE_CASE__											=			text_inputs['''input_ids''']

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

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			embeds

        # forward
        SCREAMING_SNAKE_CASE__											=			pipe(**A_     )
        SCREAMING_SNAKE_CASE__											=			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 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			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_     )

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

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

        assert image.shape == (1, 5_12, 5_12, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			DDIMScheduler.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''   ,				subfolder='''scheduler'''   ,				revision='''onnx'''     )
        SCREAMING_SNAKE_CASE__											=			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_     )

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

        assert image.shape == (1, 5_12, 5_12, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''   ,				subfolder='''scheduler'''   ,				revision='''onnx'''     )
        SCREAMING_SNAKE_CASE__											=			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_     )

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

        assert image.shape == (1, 5_12, 5_12, 3)
        SCREAMING_SNAKE_CASE__											=			np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			0

        def test_callback_fn(A_   ,				A_   ,				A_     ) -> None:
            SCREAMING_SNAKE_CASE__											=			True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 0:
                assert latents.shape == (1, 4, 64, 64)
                SCREAMING_SNAKE_CASE__											=			latents[0, -3:, -3:, -1]
                SCREAMING_SNAKE_CASE__											=			np.array(
                    [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]     )

                assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 1E-3
            elif step == 5:
                assert latents.shape == (1, 4, 64, 64)
                SCREAMING_SNAKE_CASE__											=			latents[0, -3:, -3:, -1]
                SCREAMING_SNAKE_CASE__											=			np.array(
                    [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]     )

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

        SCREAMING_SNAKE_CASE__											=			False

        SCREAMING_SNAKE_CASE__											=			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_     )

        SCREAMING_SNAKE_CASE__											=			'''Andromeda galaxy in a bottle'''

        SCREAMING_SNAKE_CASE__											=			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     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			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

        SCREAMING_SNAKE_CASE__											=			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_     )
            SCREAMING_SNAKE_CASE__											=			OnnxStableDiffusionPipeline.from_pretrained(A_     )

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

import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (abs(source - target       ) / target) < 0.01






@pytest.mark.integration
def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	_TestCommandArgs(dataset=_A				,					all_configs=_A				,					save_infos=_A       )
 __a							=	TestCommand(*_A       )
 test_command.run()
 __a							=	os.path.join(_A				,					"README.md"       )
 assert os.path.exists(_A       )
 __a							=	DatasetInfosDict.from_directory(_A       )
 __a							=	DatasetInfosDict(
     {
         "default": DatasetInfo(
             features=Features(
                 {
                     "tokens": Sequence(Value("string"       )       ),
                     "ner_tags": Sequence(
                         ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]       )       ),
                     "langs": Sequence(Value("string"       )       ),
                     "spans": Sequence(Value("string"       )       ),
                 }       )				,					splits=[
                 {
                     "name": "train",
                     "num_bytes": 235_1563,
                     "num_examples": 1_0000,
                 },
                 {
                     "name": "validation",
                     "num_bytes": 23_8418,
                     "num_examples": 1000,
                 },
             ]				,					download_size=394_0680				,					dataset_size=258_9981				,					)
     }       )
 assert dataset_infos.keys() == expected_dataset_infos.keys()
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  __a							,  __a							=	getattr(dataset_infos["default"]				,					_A       ), getattr(expected_dataset_infos["default"]				,					_A       )
  if key == "num_bytes":
   assert is_apercent_close(_A				,					_A       )
  elif key == "splits":
   assert list(_A       ) == list(_A       )
   for split in result:
    assert result[split].name == expected[split].name
    assert result[split].num_examples == expected[split].num_examples
    assert is_apercent_close(result[split].num_bytes				,					expected[split].num_bytes       )
  else:
   result == expected


