


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 		__magic_name__						(snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Tuple	   =       'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:Optional[int]=0   ):
       snake_case__						= floats_tensor((1, 3, 1_28, 1_28)     ,		rng=random.Random(_a   )   )
       snake_case__						= np.random.RandomState(_a   )
       snake_case__						= {
           '''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 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,		provider='''CPUExecutionProvider'''   )
       pipe.set_progress_bar_config(disable=_a   )

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

       assert image.shape == (1, 1_28, 1_28, 3)
       snake_case__						= np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]   )
       assert np.abs(image_slice - expected_slice   ).max() < 1e-1

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,		provider='''CPUExecutionProvider'''   )
       snake_case__						= PNDMScheduler.from_config(pipe.scheduler.config     ,		skip_prk_steps=_a   )
       pipe.set_progress_bar_config(disable=_a   )

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

       assert image.shape == (1, 1_28, 1_28, 3)
       snake_case__						= np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]   )

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

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

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

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

       assert image.shape == (1, 1_28, 1_28, 3)
       snake_case__						= np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,		provider='''CPUExecutionProvider'''   )
       snake_case__						= EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
       pipe.set_progress_bar_config(disable=_a   )

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

       assert image.shape == (1, 1_28, 1_28, 3)
       snake_case__						= np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,		provider='''CPUExecutionProvider'''   )
       snake_case__						= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
       pipe.set_progress_bar_config(disable=_a   )

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

       assert image.shape == (1, 1_28, 1_28, 3)
       snake_case__						= np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]   )

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







     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,		provider='''CPUExecutionProvider'''   )
       snake_case__						= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
       pipe.set_progress_bar_config(disable=_a   )

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

       assert image.shape == (1, 1_28, 1_28, 3)
       snake_case__						= np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]   )

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




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





     '''simple docstring'''



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

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= ort.SessionOptions()
       snake_case__						= False
       return options

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg'''   )
       snake_case__						= init_image.resize((7_68, 5_12)   )
       # using the PNDM scheduler by default
       snake_case__						= 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__						= '''A fantasy landscape, trending on artstation'''

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

       assert images.shape == (1, 5_12, 7_68, 3)
       snake_case__						= np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]   )
       # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg'''   )
       snake_case__						= init_image.resize((7_68, 5_12)   )
       snake_case__						= LMSDiscreteScheduler.from_pretrained(
           '''runwayml/stable-diffusion-v1-5'''     ,		subfolder='''scheduler'''     ,		revision='''onnx'''   )
       snake_case__						= 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__						= '''A fantasy landscape, trending on artstation'''

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

       assert images.shape == (1, 5_12, 7_68, 3)
       snake_case__						= np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]   )
       # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





import os
import sys

lowerCamelCase__							:						Optional[int]							     =   os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__							:						Optional[int]							     =   [
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]







@add_start_docstrings(AutoConfig.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Any:
  return AutoConfig.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoTokenizer.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoTokenizer.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModel.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Tuple:
  return AutoModel.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForCausalLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[Any]:
  return AutoModelForMaskedLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoModelForSequenceClassification.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForQuestionAnswering.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)

