"""simple docstring"""



import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
       import jax

       from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def __init__(		self							:     Tuple	,      A_							:     Optional[int]	,      A_							:     Union[str, Any]=13	,      A_							:     Optional[Any]=30	,      A_							:     Dict=2	,      A_							:     Tuple=3	,      A_							:     Tuple=True	,      A_							:     Tuple=True	,      A_							:     str=32	,      A_							:     List[Any]=5	,      A_							:     List[str]=4	,      A_							:     Dict=37	,      A_							:     List[Any]="gelu"	,      A_							:     Union[str, Any]=0.1	,      A_							:     Optional[Any]=0.1	,      A_							:     Optional[int]=10	,      A_							:     List[str]=0.02	,      )    ->       Tuple:
         __snake_case				=							parent
         __snake_case				=							batch_size
         __snake_case				=							image_size
         __snake_case				=							patch_size
         __snake_case				=							num_channels
         __snake_case				=							is_training
         __snake_case				=							use_labels
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							type_sequence_label_size
         __snake_case				=							initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __snake_case				=							(image_size // patch_size) ** 2
         __snake_case				=							num_patches + 1
    def  lowercase					(		self							:     int    )    ->       List[str]:
         __snake_case				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

         __snake_case				=							ViTConfig(
             image_size=self.image_size	,      patch_size=self.patch_size	,      num_channels=self.num_channels	,      hidden_size=self.hidden_size	,      num_hidden_layers=self.num_hidden_layers	,      num_attention_heads=self.num_attention_heads	,      intermediate_size=self.intermediate_size	,      hidden_act=self.hidden_act	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      is_decoder=A_	,      initializer_range=self.initializer_range	,      )

         return config, pixel_values
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     Union[str, Any]	,      A_							:     Optional[int]    )    ->       str:
         __snake_case				=							FlaxViTModel(config=A_    )
         __snake_case				=							model(A_    )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         __snake_case				=							(self.image_size, self.image_size)
         __snake_case				=							(self.patch_size, self.patch_size)
         __snake_case				=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, num_patches + 1, self.hidden_size)    )
    def  lowercase					(		self							:     List[Any]	,      A_							:     List[str]	,      A_							:     Tuple    )    ->       Optional[int]:
         __snake_case				=							self.type_sequence_label_size
         __snake_case				=							FlaxViTForImageClassification(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)    )

         # test greyscale images
         __snake_case				=							1
         __snake_case				=							FlaxViTForImageClassification(A_    )

         __snake_case				=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
         __snake_case				=							model(A_    )







    def  lowercase					(		self							:     Any    )    ->       str:
         __snake_case				=							self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )		,	(
             __snake_case
         )		,	
         )				=							config_and_inputs
         __snake_case				=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Any							=					(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
    def  lowercase					(		self							:     int    )    ->       None:
         __snake_case				=							FlaxViTModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=A_	,      has_text_modality=A_	,      hidden_size=37    )
    def  lowercase					(		self							:     str    )    ->       Any:
         self.config_tester.run_common_tests()
    def  lowercase					(		self							:     Optional[int]    )    ->       Tuple:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )
    def  lowercase					(		self							:     str    )    ->       Dict:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       List[Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __snake_case				=							model_class(A_    )
              __snake_case				=							inspect.signature(model.__call__    )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __snake_case				=							[*signature.parameters.keys()]

              __snake_case				=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]	,      A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       List[Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__    ):
                   __snake_case				=							self._prepare_for_class(A_	,      A_    )
                   __snake_case				=							model_class(A_    )

                   @jax.jit
                   def model_jitted(A_							:     str	,      **A_							:     int    ):
                        return model(pixel_values=A_	,      **A_    )

                   with self.subTest('''JIT Enabled'''    ):
                        __snake_case				=							model_jitted(**A_    ).to_tuple()

                   with self.subTest('''JIT Disabled'''    ):
                        with jax.disable_jit():
                             __snake_case				=							model_jitted(**A_    ).to_tuple()

                   self.assertEqual(len(A_    )	,      len(A_    )    )
                   for jitted_output, output in zip(A_	,      A_    ):
                        self.assertEqual(jitted_output.shape	,      output.shape    )







    @slow
    def  lowercase					(		self							:     int    )    ->       Dict:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''google/vit-base-patch16-224'''    )
              __snake_case				=							model(np.ones((1, 3, 224, 224)    )    )
              self.assertIsNotNone(A_    )
"""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      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
    def  lowercase					(		self							:     Optional[int]	,      A_							:     List[str]=0    )    ->       int:
         __snake_case				=							floats_tensor((1, 3, 128, 128)	,      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  lowercase					(		self							:     Optional[Any]    )    ->       List[Any]:
         __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, 128, 128, 3)
         __snake_case				=							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  lowercase					(		self							:     Tuple    )    ->       Optional[int]:
         __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, 128, 128, 3)
         __snake_case				=							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  lowercase					(		self							:     Optional[int]    )    ->       str:
         __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, 128, 128, 3)
         __snake_case				=							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  lowercase					(		self							:     str    )    ->       List[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, 128, 128, 3)
         __snake_case				=							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  lowercase					(		self							:     Optional[int]    )    ->       Union[str, Any]:
         __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, 128, 128, 3)
         __snake_case				=							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  lowercase					(		self							:     List[str]    )    ->       Any:
         __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, 128, 128, 3)
         __snake_case				=							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      _A	(						unittest.TestCase					):




    """simple docstring"""




    @property
    def  lowercase					(		self							:     str    )    ->       int:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )
    @property
    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case				=							ort.SessionOptions()
         __snake_case				=							False
         return options
    def  lowercase					(		self							:     Optional[Any]    )    ->       Union[str, 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((768, 512)    )
         # 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, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case				=							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  lowercase					(		self							:     str    )    ->       Optional[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((768, 512)    )
         __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, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case				=							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