


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   a__	(						lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     int							    =     """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     Union[str, Any]=0    ):
            a_    :     Any										=    np.random.RandomState(lowerCamelCase_    )
            a_    :     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 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     List[Any]										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     str										=    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 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     str										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Any										=    PNDMScheduler.from_config(pipe.scheduler.config      ,							skip_prk_steps=lowerCamelCase_    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     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 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Optional[Any]										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Optional[int]										=    LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Dict										=    self.get_dummy_inputs()
            a_    :     Union[str, Any]										=    pipe(**lowerCamelCase_    ).images
            a_    :     int										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     Optional[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 						UpperCAmelCase(   self      :     Any    ):
            a_    :     int										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Optional[Any]										=    EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     Optional[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 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Any										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     str										=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Dict										=    self.get_dummy_inputs()
            a_    :     Union[str, Any]										=    pipe(**lowerCamelCase_    ).images
            a_    :     Any										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     Tuple										=    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 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Tuple										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Tuple										=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Tuple										=    self.get_dummy_inputs()
            a_    :     Optional[int]										=    pipe(**lowerCamelCase_    ).images
            a_    :     str										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     List[Any]										=    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 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     int										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     List[str]										=    self.get_dummy_inputs()
            a_    :     Optional[int]										=    3 * [inputs["""prompt"""]]

            # forward
            a_    :     Optional[int]										=    pipe(**lowerCamelCase_    )
            a_    :     Union[str, Any]										=    output.images[0, -3:, -3:, -1]

            a_    :     Tuple										=    self.get_dummy_inputs()
            a_    :     List[Any]										=    3 * [inputs.pop("""prompt"""    )]

            a_    :     List[Any]										=    pipe.tokenizer(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=pipe.tokenizer.model_max_length      ,							truncation=lowerCamelCase_      ,							return_tensors="""np"""      ,							)
            a_    :     str										=    text_inputs["""input_ids"""]

            a_    :     Dict										=    pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]

            a_    :     List[Any]										=    prompt_embeds

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

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

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Any										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     int										=    self.get_dummy_inputs()
            a_    :     str										=    3 * ["""this is a negative prompt"""]
            a_    :     int										=    negative_prompt
            a_    :     Dict										=    3 * [inputs["""prompt"""]]

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

            a_    :     Tuple										=    self.get_dummy_inputs()
            a_    :     Optional[int]										=    3 * [inputs.pop("""prompt"""    )]

            a_    :     str										=    []
            for p in [prompt, negative_prompt]:
                 a_    :     Optional[int]										=    pipe.tokenizer(
                     lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=pipe.tokenizer.model_max_length      ,							truncation=lowerCamelCase_      ,							return_tensors="""np"""      ,							)
                 a_    :     int										=    text_inputs["""input_ids"""]

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

            a_			,			a_    :     Union[str, Any]										=    embeds

            # forward
            a_    :     Optional[Any]										=    pipe(**lowerCamelCase_    )
            a_    :     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   a__	(						unittest.TestCase    ):

       @property
       def 						UpperCAmelCase(   self      :     Tuple    ):
            return (
                "CUDAExecutionProvider",
                {
                    "gpu_mem_limit": "15000000000",  # 15GB
                    "arena_extend_strategy": "kSameAsRequested",
                },
            )

       @property
       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     List[str]										=    ort.SessionOptions()
            a_    :     List[str]										=    False
            return options

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            # using the PNDM scheduler by default
            a_    :     int										=    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_    )

            a_    :     Tuple										=    """A painting of a squirrel eating a burger"""
            np.random.seed(0    )
            a_    :     int										=    sd_pipe([prompt]      ,							guidance_scale=6.0      ,							num_inference_steps=1_0      ,							output_type="""np"""    )
            a_    :     Optional[int]										=    output.images

            a_    :     Tuple										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            a_    :     Optional[int]										=    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 						UpperCAmelCase(   self      :     str    ):
            a_    :     str										=    DDIMScheduler.from_pretrained(
                """runwayml/stable-diffusion-v1-5"""      ,							subfolder="""scheduler"""      ,							revision="""onnx"""    )
            a_    :     Any										=    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_    )

            a_    :     Union[str, Any]										=    """open neural network exchange"""
            a_    :     str										=    np.random.RandomState(0    )
            a_    :     str										=    sd_pipe([prompt]      ,							guidance_scale=7.5      ,							num_inference_steps=1_0      ,							generator=lowerCamelCase_      ,							output_type="""np"""    )
            a_    :     str										=    output.images
            a_    :     Optional[Any]										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            a_    :     List[str]										=    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 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     Optional[int]										=    LMSDiscreteScheduler.from_pretrained(
                """runwayml/stable-diffusion-v1-5"""      ,							subfolder="""scheduler"""      ,							revision="""onnx"""    )
            a_    :     List[str]										=    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_    )

            a_    :     Optional[int]										=    """open neural network exchange"""
            a_    :     Tuple										=    np.random.RandomState(0    )
            a_    :     Tuple										=    sd_pipe([prompt]      ,							guidance_scale=7.5      ,							num_inference_steps=1_0      ,							generator=lowerCamelCase_      ,							output_type="""np"""    )
            a_    :     Tuple										=    output.images
            a_    :     str										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            a_    :     str										=    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 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     List[str]										=    0

            def test_callback_fn(lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     np.ndarray    ) -> None:
                 a_    :     List[Any]										=    True
                 nonlocal number_of_steps
                 number_of_steps += 1
                 if step == 0:
                      assert latents.shape == (1, 4, 6_4, 6_4)
                      a_    :     Union[str, Any]										=    latents[0, -3:, -3:, -1]
                      a_    :     Optional[Any]										=    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, 6_4, 6_4)
                      a_    :     List[str]										=    latents[0, -3:, -3:, -1]
                      a_    :     int										=    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

            a_    :     Optional[int]										=    False

            a_    :     List[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_    )

            a_    :     List[str]										=    """Andromeda galaxy in a bottle"""

            a_    :     Union[str, Any]										=    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 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Tuple										=    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

            a_    :     Union[str, Any]										=    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_    )
                 a_    :     Tuple										=    OnnxStableDiffusionPipeline.from_pretrained(lowerCamelCase_    )

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







import numpy as np

__lowerCamelCase        =     [
    ['''a''', '''b''', '''c''', '''d''', '''e'''],
    ['''f''', '''g''', '''h''', '''i''', '''k'''],
    ['''l''', '''m''', '''n''', '''o''', '''p'''],
    ['''q''', '''r''', '''s''', '''t''', '''u'''],
    ['''v''', '''w''', '''x''', '''y''', '''z'''],
]


class   a__	:

       def __init__(   self      :     Union[str, Any]    ):
            a_    :     List[Any]										=    np.array(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     str    ):
            a_			,			a_    :     Optional[int]										=    np.where(letter == self.SQUARE    )
            a_    :     Union[str, Any]										=    np.concatenate([indexa + 1, indexa + 1]    )
            return indexes

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int    ):
            a_    :     str										=    self.SQUARE[indexa - 1, indexa - 1]
            return letter

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     str    ):
            a_    :     List[str]										=    message.lower()
            a_    :     str										=    message.replace(""" """      ,							""""""    )
            a_    :     Union[str, Any]										=    message.replace("""j"""      ,							"""i"""    )

            a_    :     Optional[Any]										=    np.empty((2, len(lowerCamelCase_    ))    )
            for letter_index in range(len(lowerCamelCase_    )    ):
                 a_    :     int										=    self.letter_to_numbers(message[letter_index]    )

                 a_    :     str										=    numbers[0]
                 a_    :     int										=    numbers[1]

            a_    :     int										=    first_step.reshape(2 * len(lowerCamelCase_    )    )
            a_    :     Optional[Any]										=    """"""
            for numbers_index in range(len(lowerCamelCase_    )    ):
                 a_    :     Optional[Any]										=    int(second_step[numbers_index * 2]    )
                 a_    :     Optional[Any]										=    int(second_step[(numbers_index * 2) + 1]    )
                 a_    :     Optional[Any]										=    self.numbers_to_letter(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     List[str]										=    encoded_message + letter

            return encoded_message

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     str    ):
            a_    :     Optional[Any]										=    message.lower()
            message.replace(""" """      ,							""""""    )
            a_    :     int										=    np.empty(2 * len(lowerCamelCase_    )    )
            for letter_index in range(len(lowerCamelCase_    )    ):
                 a_    :     str										=    self.letter_to_numbers(message[letter_index]    )
                 a_    :     Optional[int]										=    numbers[0]
                 a_    :     Optional[Any]										=    numbers[1]

            a_    :     Tuple										=    first_step.reshape((2, len(lowerCamelCase_    ))    )
            a_    :     Optional[int]										=    """"""
            for numbers_index in range(len(lowerCamelCase_    )    ):
                 a_    :     Dict										=    int(second_step[0, numbers_index]    )
                 a_    :     Tuple										=    int(second_step[1, numbers_index]    )
                 a_    :     Tuple										=    self.numbers_to_letter(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    decoded_message + letter

            return decoded_message



