





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       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


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






  def 							lowercase						(							self					:						Optional[Any]   ,    lowerCAmelCase_					:						Tuple=0	)				->		str:
     __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 							lowercase						(							self					:						Tuple	)				->		List[str]:
     __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, 1_2_8, 1_2_8, 3)
     __lowerCAmelCase				      =      np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55]	)

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






  def 							lowercase						(							self					:						Optional[Any]	)				->		Optional[Any]:
     __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, 1_2_8, 1_2_8, 3)
     __lowerCAmelCase				      =      np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30]	)

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






  def 							lowercase						(							self					:						Optional[int]	)				->		Optional[Any]:
     __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, 1_2_8, 1_2_8, 3)
     __lowerCAmelCase				      =      np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]	)

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






  def 							lowercase						(							self					:						str	)				->		Union[str, Any]:
     __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, 1_2_8, 1_2_8, 3)
     __lowerCAmelCase				      =      np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]	)

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






  def 							lowercase						(							self					:						Any	)				->		int:
     __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, 1_2_8, 1_2_8, 3)
     __lowerCAmelCase				      =      np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71]	)

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






  def 							lowercase						(							self					:						List[str]	)				->		Optional[Any]:
     __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, 1_2_8, 1_2_8, 3)
     __lowerCAmelCase				      =      np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00]	)

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






  def 							lowercase						(							self					:						Optional[int]	)				->		int:
     __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 							lowercase						(							self					:						List[str]	)				->		Optional[Any]:
     __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 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


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






  @property
  def 							lowercase						(							self					:						List[Any]	)				->		str:
     __lowerCAmelCase				      =      ort.SessionOptions()
     __lowerCAmelCase				      =      False
     return options






  def 							lowercase						(							self					:						int	)				->		Dict:
     # using the PNDM scheduler by default
     __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=1_0   ,    output_type='np'	)
     __lowerCAmelCase				      =      output.images

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

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase				      =      np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20]	)

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






  def 							lowercase						(							self					:						Optional[int]	)				->		Tuple:
     __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=1_0   ,    generator=lowerCAmelCase_   ,    output_type='np'	)
     __lowerCAmelCase				      =      output.images
     __lowerCAmelCase				      =      image[0, -3:, -3:, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase				      =      np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86]	)

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






  def 							lowercase						(							self					:						List[str]	)				->		int:
     __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=1_0   ,    generator=lowerCAmelCase_   ,    output_type='np'	)
     __lowerCAmelCase				      =      output.images
     __lowerCAmelCase				      =      image[0, -3:, -3:, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase				      =      np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61]	)

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






  def 							lowercase						(							self					:						List[str]	)				->		List[Any]:
     __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, 6_4, 6_4)
           __lowerCAmelCase				      =      latents[0, -3:, -3:, -1]
           __lowerCAmelCase				      =      np.array(
               [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67]	)

           assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1e-3
        elif step == 5:
           assert latents.shape == (1, 4, 6_4, 6_4)
           __lowerCAmelCase				      =      latents[0, -3:, -3:, -1]
           __lowerCAmelCase				      =      np.array(
               [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75]	)

           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 							lowercase						(							self					:						Optional[int]	)				->		Optional[Any]:
     __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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCamelCase		       =      {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TrOCRForCausalLM""",
						    """TrOCRPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


