





import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    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__				(   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Union[str, Any]				=    "ssube/stable-diffusion-x4-upscaler-onnx"






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					List[str]=0 )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		floats_tensor((1, 3, 128, 128)		,rng=random.Random(lowerCamelCase__ ) )
           SCREAMING_SNAKE_CASE	=		torch.manual_seed(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		{
               """prompt""": """A painting of a squirrel eating a burger""",
               """image""": image,
               """generator""": generator,
               """num_inference_steps""": 3,
               """guidance_scale""": 7.5,
               """output_type""": """numpy""",
           }
           return inputs






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider""" )
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

           # started as 128, should now be 512
           assert image.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE	=		np.array(
               [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223] )
           assert np.abs(image_slice - expected_slice ).max() < 1e-1






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider""" )
           SCREAMING_SNAKE_CASE	=		PNDMScheduler.from_config(pipe.scheduler.config		,skip_prk_steps=lowerCamelCase__ )
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

           assert image.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE	=		np.array(
               [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964] )
           assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider""" )
           SCREAMING_SNAKE_CASE	=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

           assert image.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE	=		np.array(
               [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515] )

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider""" )
           SCREAMING_SNAKE_CASE	=		EulerDiscreteScheduler.from_config(pipe.scheduler.config )
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

           assert image.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE	=		np.array(
               [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223] )
           assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,provider="""CPUExecutionProvider""" )
           SCREAMING_SNAKE_CASE	=		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

           assert image.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE	=		np.array(
               [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043] )

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

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



     '''simple docstring'''

     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Optional[int]:




           '''simple docstring'''


           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		ort.SessionOptions()
           SCREAMING_SNAKE_CASE	=		False
           return options






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg""" )
           SCREAMING_SNAKE_CASE	=		init_image.resize((128, 128) )
           # using the PNDM scheduler by default
           SCREAMING_SNAKE_CASE	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(
               """ssube/stable-diffusion-x4-upscaler-onnx"""		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		"""A fantasy landscape, trending on artstation"""

           SCREAMING_SNAKE_CASE	=		torch.manual_seed(0 )
           SCREAMING_SNAKE_CASE	=		pipe(
               prompt=lowerCamelCase__		,image=lowerCamelCase__		,guidance_scale=7.5		,num_inference_steps=10		,generator=lowerCamelCase__		,output_type="""np"""		,)
           SCREAMING_SNAKE_CASE	=		output.images
           SCREAMING_SNAKE_CASE	=		images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE	=		np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972] )
           # 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		:					Any )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg""" )
           SCREAMING_SNAKE_CASE	=		init_image.resize((128, 128) )
           SCREAMING_SNAKE_CASE	=		LMSDiscreteScheduler.from_pretrained(
               """ssube/stable-diffusion-x4-upscaler-onnx"""		,subfolder="""scheduler""" )
           SCREAMING_SNAKE_CASE	=		OnnxStableDiffusionUpscalePipeline.from_pretrained(
               """ssube/stable-diffusion-x4-upscaler-onnx"""		,scheduler=lowerCamelCase__		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		"""A fantasy landscape, trending on artstation"""

           SCREAMING_SNAKE_CASE	=		torch.manual_seed(0 )
           SCREAMING_SNAKE_CASE	=		pipe(
               prompt=lowerCamelCase__		,image=lowerCamelCase__		,guidance_scale=7.5		,num_inference_steps=20		,generator=lowerCamelCase__		,output_type="""np"""		,)
           SCREAMING_SNAKE_CASE	=		output.images
           SCREAMING_SNAKE_CASE	=		images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE	=		np.array(
               [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566] )
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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









from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFResNetForImageClassification, TFResNetModel
      from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					List[str]=3		,lowerCamelCase__		:					Optional[Any]=32		,lowerCamelCase__		:					Any=3		,lowerCamelCase__		:					Any=10		,lowerCamelCase__		:					Any=[10, 20, 30, 40]		,lowerCamelCase__		:					Any=[1, 1, 2, 1]		,lowerCamelCase__		:					Union[str, Any]=True		,lowerCamelCase__		:					List[str]=True		,lowerCamelCase__		:					Union[str, Any]="relu"		,lowerCamelCase__		:					Dict=3		,lowerCamelCase__		:					Any=None		,)  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		parent
           SCREAMING_SNAKE_CASE	=		batch_size
           SCREAMING_SNAKE_CASE	=		image_size
           SCREAMING_SNAKE_CASE	=		num_channels
           SCREAMING_SNAKE_CASE	=		embeddings_size
           SCREAMING_SNAKE_CASE	=		hidden_sizes
           SCREAMING_SNAKE_CASE	=		depths
           SCREAMING_SNAKE_CASE	=		is_training
           SCREAMING_SNAKE_CASE	=		use_labels
           SCREAMING_SNAKE_CASE	=		hidden_act
           SCREAMING_SNAKE_CASE	=		num_labels
           SCREAMING_SNAKE_CASE	=		scope
           SCREAMING_SNAKE_CASE	=		len(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

           SCREAMING_SNAKE_CASE	=		None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE	=		ids_tensor([self.batch_size]		,self.num_labels )

           SCREAMING_SNAKE_CASE	=		self.get_config()

           return config, pixel_values, labels






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Union[str, Any]:




           '''simple docstring'''


           return ResNetConfig(
               num_channels=self.num_channels		,embeddings_size=self.embeddings_size		,hidden_sizes=self.hidden_sizes		,depths=self.depths		,hidden_act=self.hidden_act		,num_labels=self.num_labels		,image_size=self.image_size		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					List[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModel(config=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__ )
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Dict )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.num_labels
           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__		,labels=lowerCamelCase__ )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		config_and_inputs
           SCREAMING_SNAKE_CASE	=		{"""pixel_values""": pixel_values}
           return config, inputs_dict

@require_tf
class 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Union[str, Any]				=    (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __snake_case					:			int				=    (
         {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __snake_case					:			Optional[int]				=    False
     __snake_case					:			int				=    False
     __snake_case					:			Optional[Any]				=    False
     __snake_case					:			Union[str, Any]				=    False
     __snake_case					:			str				=    False






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,has_text_modality=lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           return






     @unittest.skip(reason="""ResNet does not use inputs_embeds""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""ResNet does not support input and output embeddings""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE	=		["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__		:					str		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Dict ):
                 SCREAMING_SNAKE_CASE	=		model_class(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		model(**self._prepare_for_class(lowerCamelCase__		,lowerCamelCase__ ) )

                 SCREAMING_SNAKE_CASE	=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 SCREAMING_SNAKE_CASE	=		self.model_tester.num_stages
                 self.assertEqual(len(lowerCamelCase__ )		,expected_num_stages + 1 )

                 # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:] )		,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,)

           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE	=		["""basic""", """bottleneck"""]
           for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                       SCREAMING_SNAKE_CASE	=		layer_type
                       SCREAMING_SNAKE_CASE	=		True
                       check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )

                       # check that output_hidden_states also work using config
                       del inputs_dict["output_hidden_states"]
                       SCREAMING_SNAKE_CASE	=		True

                       check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE	=		TFResNetModel.from_pretrained(lowerCamelCase__ )
                 self.assertIsNotNone(lowerCamelCase__ )



def      __lowercase     (   )				->     Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
      return image



@require_tf
@require_vision
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     @cached_property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Dict:




           '''simple docstring'''


           return (
               AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
               if is_vision_available()
               else None
           )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

           SCREAMING_SNAKE_CASE	=		self.default_image_processor
           SCREAMING_SNAKE_CASE	=		prepare_img()
           SCREAMING_SNAKE_CASE	=		image_processor(images=lowerCamelCase__		,return_tensors="""tf""" )

           # forward pass
           SCREAMING_SNAKE_CASE	=		model(**lowerCamelCase__ )

           # verify the logits
           SCREAMING_SNAKE_CASE	=		tf.TensorShape((1, 1000) )
           self.assertEqual(outputs.logits.shape		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tf.constant([-11.1069, -9.7877, -8.3777] )

           self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()		,lowerCamelCase__		,atol=1e-4 ) )


