





'''simple docstring'''




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE     :str       =				{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :Optional[Any]       =				['''RoFormerTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :int       =				[
      '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''RoFormerForCausalLM''',
      '''RoFormerForMaskedLM''',
      '''RoFormerForMultipleChoice''',
      '''RoFormerForQuestionAnswering''',
      '''RoFormerForSequenceClassification''',
      '''RoFormerForTokenClassification''',
      '''RoFormerLayer''',
      '''RoFormerModel''',
      '''RoFormerPreTrainedModel''',
      '''load_tf_weights_in_roformer''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :List[Any]       =				[
      '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFRoFormerForCausalLM''',
      '''TFRoFormerForMaskedLM''',
      '''TFRoFormerForMultipleChoice''',
      '''TFRoFormerForQuestionAnswering''',
      '''TFRoFormerForSequenceClassification''',
      '''TFRoFormerForTokenClassification''',
      '''TFRoFormerLayer''',
      '''TFRoFormerModel''',
      '''TFRoFormerPreTrainedModel''',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :int       =				[
      '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''FlaxRoFormerForMaskedLM''',
      '''FlaxRoFormerForMultipleChoice''',
      '''FlaxRoFormerForQuestionAnswering''',
      '''FlaxRoFormerForSequenceClassification''',
      '''FlaxRoFormerForTokenClassification''',
      '''FlaxRoFormerModel''',
      '''FlaxRoFormerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

  __SCREAMING_SNAKE_CASE     :Tuple       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)











"""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   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Union[str, Any]=0		):
												A		=  floats_tensor((1, 3, 1_28, 1_28)     ,       rng=random.Random(__UpperCamelCase		)		)
												A		=  np.random.RandomState(__UpperCamelCase		)
												A		=  {
												    "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 	lowerCamelCase       (					self			:Any		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]		)
												assert np.abs(image_slice - expected_slice		).max() < 1e-1


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  PNDMScheduler.from_config(pipe.scheduler.config     ,       skip_prk_steps=__UpperCamelCase		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

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

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]		)

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


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												# warmup pass to apply optimizations
												A		=  pipe(**self.get_dummy_inputs()		)

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

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]		)

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


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  EulerDiscreteScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

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

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]		)

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


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

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

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]		)

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


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

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

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]		)

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







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


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


					@property
					def 	lowerCamelCase       (					self			:Optional[int]		):
												A		=  ort.SessionOptions()
												A		=  False
												return options


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/img2img/sketch-mountains-input.jpg"		)
												A		=  init_image.resize((7_68, 5_12)		)
												# using the PNDM scheduler by default
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    "CompVis/stable-diffusion-v1-4"     ,       revision="onnx"     ,       safety_checker=__UpperCamelCase     ,       feature_extractor=__UpperCamelCase     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  "A fantasy landscape, trending on artstation"

												A		=  np.random.RandomState(0		)
												A		=  pipe(
												    prompt=__UpperCamelCase     ,       image=__UpperCamelCase     ,       strength=0.75     ,       guidance_scale=7.5     ,       num_inference_steps=10     ,       generator=__UpperCamelCase     ,       output_type="np"     ,       )
												A		=  output.images
												A		=  images[0, 2_55:2_58, 3_83:3_86, -1]

												assert images.shape == (1, 5_12, 7_68, 3)
												A		=  np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]		)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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


					def 	lowerCamelCase       (					self			:Any		):
												A		=  load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/img2img/sketch-mountains-input.jpg"		)
												A		=  init_image.resize((7_68, 5_12)		)
												A		=  LMSDiscreteScheduler.from_pretrained(
												    "runwayml/stable-diffusion-v1-5"     ,       subfolder="scheduler"     ,       revision="onnx"		)
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    "runwayml/stable-diffusion-v1-5"     ,       revision="onnx"     ,       scheduler=__UpperCamelCase     ,       safety_checker=__UpperCamelCase     ,       feature_extractor=__UpperCamelCase     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  "A fantasy landscape, trending on artstation"

												A		=  np.random.RandomState(0		)
												A		=  pipe(
												    prompt=__UpperCamelCase     ,       image=__UpperCamelCase     ,       strength=0.75     ,       guidance_scale=7.5     ,       num_inference_steps=20     ,       generator=__UpperCamelCase     ,       output_type="np"     ,       )
												A		=  output.images
												A		=  images[0, 2_55:2_58, 3_83:3_86, -1]

												assert images.shape == (1, 5_12, 7_68, 3)
												A		=  np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]		)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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


