


"""simple docstring"""

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  _UpperCAmelCase (    UpperCamelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    str	      =	"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""

 def 		_snake_case      (  self	: int						,  lowercase_	: List[str]=0       ):
     snake_case_				:  Union[str, Any]       =			floats_tensor((1, 3, 128, 128)						,  rng=random.Random(_a       )       )
     snake_case_				:  Any       =			np.random.RandomState(_a       )
     snake_case_				:  List[str]       =			{
         """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 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  List[str]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     pipe.set_progress_bar_config(disable=_a       )

     snake_case_				:  int       =			self.get_dummy_inputs()
     snake_case_				:  Union[str, Any]       =			pipe(**_a       ).images
     snake_case_				:  Union[str, Any]       =			image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  int       =			np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]       )
     assert np.abs(image_slice - expected_slice       ).max() < 1E-1

 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  Optional[Any]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  List[Any]       =			PNDMScheduler.from_config(pipe.scheduler.config						,  skip_prk_steps=_a       )
     pipe.set_progress_bar_config(disable=_a       )

     snake_case_				:  Dict       =			self.get_dummy_inputs()
     snake_case_				:  int       =			pipe(**_a       ).images
     snake_case_				:  str       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  List[str]       =			np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]       )

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

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  Dict       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  Union[str, Any]       =			LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=_a       )

     # warmup pass to apply optimizations
     snake_case_				:  Union[str, Any]       =			pipe(**self.get_dummy_inputs()       )

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

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  List[str]       =			np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]       )

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

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  str       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  Dict       =			EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=_a       )

     snake_case_				:  Dict       =			self.get_dummy_inputs()
     snake_case_				:  str       =			pipe(**_a       ).images
     snake_case_				:  Dict       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  Dict       =			np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]       )

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

 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  Optional[int]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  str       =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=_a       )

     snake_case_				:  List[Any]       =			self.get_dummy_inputs()
     snake_case_				:  str       =			pipe(**_a       ).images
     snake_case_				:  Optional[Any]       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  Union[str, Any]       =			np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]       )

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






 def 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  Optional[int]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  Dict       =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=_a       )

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

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  int       =			np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]       )

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





@nightly
@require_onnxruntime
@require_torch_gpu
class  _UpperCAmelCase (    unittest.TestCase):

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

 @property
 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  str       =			ort.SessionOptions()
     snake_case_				:  Any       =			False
     return options

 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Tuple       =			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''       )
     snake_case_				:  Optional[int]       =			init_image.resize((768, 512)       )
     # using the PNDM scheduler by default
     snake_case_				:  Optional[int]       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4'''						,  revision='''onnx'''						,  safety_checker=_a						,  feature_extractor=_a						,  provider=self.gpu_provider						,  sess_options=self.gpu_options						,  )
     pipe.set_progress_bar_config(disable=_a       )

     snake_case_				:  List[Any]       =			"""A fantasy landscape, trending on artstation"""

     snake_case_				:  List[Any]       =			np.random.RandomState(0       )
     snake_case_				:  Dict       =			pipe(
         prompt=_a						,  image=_a						,  strength=0.75						,  guidance_scale=7.5						,  num_inference_steps=10						,  generator=_a						,  output_type='''np'''						,  )
     snake_case_				:  str       =			output.images
     snake_case_				:  Tuple       =			images[0, 255:258, 383:386, -1]

     assert images.shape == (1, 512, 768, 3)
     snake_case_				:  Optional[Any]       =			np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]       )
     # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Dict       =			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''       )
     snake_case_				:  List[str]       =			init_image.resize((768, 512)       )
     snake_case_				:  List[str]       =			LMSDiscreteScheduler.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''						,  subfolder='''scheduler'''						,  revision='''onnx'''       )
     snake_case_				:  Any       =			OnnxStableDiffusionImgaImgPipeline.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						,  )
     pipe.set_progress_bar_config(disable=_a       )

     snake_case_				:  Any       =			"""A fantasy landscape, trending on artstation"""

     snake_case_				:  Optional[Any]       =			np.random.RandomState(0       )
     snake_case_				:  Union[str, Any]       =			pipe(
         prompt=_a						,  image=_a						,  strength=0.75						,  guidance_scale=7.5						,  num_inference_steps=20						,  generator=_a						,  output_type='''np'''						,  )
     snake_case_				:  Optional[Any]       =			output.images
     snake_case_				:  Any       =			images[0, 255:258, 383:386, -1]

     assert images.shape == (1, 512, 768, 3)
     snake_case_				:  Tuple       =			np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]       )
     # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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










"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__					:						List[Any]												=			{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[Any]												=			['''XGLMTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[str]												=			['''XGLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''XGLMForCausalLM''',
         '''XGLMModel''',
         '''XGLMPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						int												=			[
         '''FlaxXGLMForCausalLM''',
         '''FlaxXGLMModel''',
         '''FlaxXGLMPreTrainedModel''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFXGLMForCausalLM''',
         '''TFXGLMModel''',
         '''TFXGLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm import XGLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm_fast import XGLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xglm import (
              TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXGLMForCausalLM,
              TFXGLMModel,
              TFXGLMPreTrainedModel,
          )


else:
     import sys

     lowercase__					:						List[Any]												=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)





